The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI

The Facets of Artiﬁcial Intelligence:
A Framework to Track the Evolution of AI
Fernando Mart ´ınez-Plumed1, Bao Sheng Loe2, Peter Flach3,
Se´an ´Oh ´Eigeartaigh2,Karina V old2and Jos ´e Hern ´andez-Orallo1
1Universitat Polit `ecnica de V al `encia, Spain
2University of Cambridge, United Kingdom
3University of Bristol, United Kingdom
fmartinez@dsic.upv.es, bsl28@cam.ac.uk, Peter.Flach@bristol.ac.uk,
so348@cam.ac.uk, kvv22@cam.ac.uk, jorallo@dsic.upv.es
Abstract
We present nine facets for the analysis of the past
and future evolution of AI. Each facet has also a setof edges that can summarise different trends andcontours in AI. With them, we ﬁrst conduct a quan-titative analysis using the information from twodecades of AAAI/IJCAI conferences and around50 years of documents from AI topics,a no f ﬁ -
cial database from the AAAI, illustrated by severalplots. We then perform a qualitative analysis usingthe facets and edges, locating AI systems in the in-telligence landscape and the discipline as a whole.This analytical framework provides a more struc-tured and systematic way of looking at the shapeand boundaries of AI.
1 Introduction
“What is AI?” has been a common question from the incep-tion of the discipline in the 1950s [McCarthy et al., 2006;
Moor, 2006; Solomonoff, 2017 ]until the end of the last cen-
tury [Lehman-Wilzig, 1981; Fetzer, 1990; McCarthy, 1998 ].
In the twenty-ﬁrst century, the discipline is not only well-developed but it is often said that this is the age of AI [Mc-
Corduck, 2004 ]. AI is set to pervade and transform every
aspect of life. In a way, this is no different from what com-puter science has already been doing, creating computerised,digital and virtual versions of almost everything, with AI nowintroducing new adjectives such as ‘intelligent’, ‘smart’ and‘cognitive’ to almost every process or gadget, from medi-cal diagnosis to personal assistants. This expected expansionand intertwining with every other research discipline and as-pect of life is pushing the contours of AI in many directions.However, unlike computer science, which is based on well-established models of computation that integrate hardwareand software, AI has evolved with a more ﬂuid deﬁnition,primarily because of our varied conceptions of intelligence.
When looking at the deﬁnitions of (artiﬁcial) intelligence
(see, e.g., [Legg and Hutter, 2007 ], for a compendium),we see that deﬁnitions can be categorised according to twodistinct dimensions, following [Russell and Norvig, 2009 ].
First, we can characterise intelligence in terms of “thinking”(process-oriented) or “acting” (goal-oriented). Second, wecan characterise intelligence taking humans as a reference orlooking for a more abstract or universal reference (such as ra-tionality). These two dimensions are summarised in Table 1.
Not all deﬁnitions can be clearly classiﬁed according to
this table. For instance, Minsky’s famous deﬁnition of AI, asthe “science of making machines capable of performing tasksthat would require intelligence if done by [humans]” [Minsky,
1968 ], uses humans as a reference, so it would be located
on the left of the table, but still somewhere between the topand bottom part of the table as it focuses on processes (i.e.,thinking) for humans and tasks (i.e., acting) for machines
This suggests that we can look at these dimensions in a
more nuanced, continuous way, as facets rather than discrete
categories, to look at the evolution of AI as a discipline. Forinstance, has AI been more focused towards “thinking” (andhence processes or techniques) or more focused towards “act-ing’ (and hence tasks and applications)? Is it more or less in-ﬂuenced by human intelligence (using human processes andthe tasks humans can actually solve) or is the discipline goingin a direction of a more abstract or universal characterisation?In this paper, we will develop these and other dimensions intofacets to characterise the object of AI, i.e., the AI systems and
services, such as generality, location and embodiment, andthe subject of AI, such as paradigms, actors, character and
nature, as a discipline. With these facets we will perform a
qualitative and quantitative analysis of the evolution of AI.
We will base our analysis on evidence as much as possible,
looking at data from scientiﬁc venues, reports, surveys andother sources. At the end of the paper, we will take a moreprincipled stance and we will discuss how to extrapolate these
Thinking Humanly
Thinking Rationally
Acting Humanly Acting Rationally
Table 1: Categories of (artiﬁcial) intelligence deﬁnitions, according
to the two binary dimensions in[Russell and Norvig, 2009, Fig. 1.1 ].Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5180
trends or identify what kinds of criteria are necessary to es-
tablish AI contours that can be more stable and useful.
The contributions of this paper are a set of well-structured
facets to analyse the location and contours of AI. This is
complemented by substantial evidence collected from severalsources, such as 20 years of the IJCAI and AAAI proceed-ings and the whole AI topics database
1. All the datasets, plots
and code to scrape and process this data are made publiclyavailable (see footnote 7).
There are several reasons why using clear criteria such as
the facets and edges are helpful for deﬁning AI, how it has
evolved and what it is likely to be in the future. First, in-ternally, the research community needs clear criteria to de-termine what is in or out of scope, for conferences, journals,funding and hiring. Second, policy makers are consideringways of regulating AI, but wrestle with the problem of deﬁn-ing what systems are actually AI. Third, different terms arebeing used to capture the same subparts or forms of AI, suchas artiﬁcial general intelligence (AGI), machine intelligence,cognitive computing, computational intelligence, soft com-puting, etc. Without a clear structure about AI, it is difﬁ-cult to determine whether they are part, they overlap or theyare simply redundant. Fourth, some areas, such as machinelearning, are taking a more relevant role in AI, but they arealso intertwined with areas such as statistics, optimisation orprobability theory, which were not always considered near thecontours of AI. Fifth, along the history of AI there has beenrise and decline of interest and research towards the differentAI paradigms, techniques and approaches.
2 Background
Disciplines are commonly analysed historically, and AI isnot an exception. A history of a discipline usually empha-sises the problems, progress and prospects, but not neces-sarily delineates its contours. For instance, there are severalexcellent accounts of the history of AI [McCorduck, 2004;
Buchanan, 2005; Nilsson, 2009; Boden, 2016 ]. Some of them
cover AI from a philosophical perspective. However, giventhe implications of AI for the interpretation of the humanmind, the analysis of AI as a discipline (as usual from theviewpoint of philosophy –or methodology– of science) is notcommonly done in terms of its external and internal contours.As a result, several key questions remain: what are the crite-ria to recognise that an entity is part of AI? Furthermore, howcan we recognise the internal subdisciplines in AI?
One possible approach to this is to determine the nature
and contours of AI by its common use. While this may bea good approach for evaluating progress as a whole or for afew benchmarks
2, the analysis of subdisciplines by their pop-
ularity may be prone to many terminological confusions andmany vested interests, with the risk of having characterisa-tions that are very volatile, such as big data.
Bibliometric approaches are a common tool to analyse dis-
ciplines, but the focus is usually put on impacts per author,venue, location or institution. Sometimes, bibliometrics stud-ies disciplines and subdisciplines. For instance, Scimago pro-
1https://aitopics.org.
2See, e.g., http://aiindex.org/ and https://www.eff.org/ai/metrics.Artiﬁcial Intelligence Artiﬁcial Neural network+ Genetic algorithm+
Expert System\* Optimization\* Prediction\*
Classiﬁcation\* Design\* Fuzzy Logic+
Multi-agent system+ Simulation\* Support V ector Machine+
Machine Learning+ Diagnosis\* Data mining\*
Swarm intelligence+ Particle swarm optimization+ Decision support system\*
Pattern recognition\* Knowledge-based system\* Case-based reasoning\*
Distributed artiﬁcial intelligence+ Knowledge representation\* Management\*
Identiﬁcation\* Decision making\* Fault Diagnosis\*
Computational intelligence+ Recognition\* Ontology+
Table 2: Top 30 high-frequency keywords used for the bibliometric
analysis in [Niu et al., 2016, Tab. 6 ]. Those shown with ‘+’ are
included in their Fig. 6 (methods and models) and those with ‘\*’ areincluded in their Fig. 7 (applications).
Applications Cognitive modeling Game theory
Heuristic search & optimization Humans & AI Knowledge representation
Machine learning Multiagent systems Natural language processing
Planning Search & constraint satisfaction Vision & robotics
Table 3: A selection of keywords as an intersection of the two lastcolumns of [Moran et al., 2014, Tab. 2 ].
vides a way of looking at the “shape of science”3where one
can locate AI and some of its subareas in terms of their inter-
nal and external relations. Provided with a set of tags, biblio-metrics can study how frequent several tags are in publishedpapers (including titles, keywords or abstracts). For instance,
[Niu et al., 2016 ]includes a thorough historical analysis of
publications in about 20 relevant journals in AI (but not con-
ferences or open journals such as JAIR) from 1990 to 2014.The number of publications is shown by 5-year periods. Thekeywords used are shown in Table 2.
As we can see in the list, the keywords include terms for
disciplines and subdisciplines, techniques, and application ar-eas, from which the authors distinguish “methods and mod-els” and “applications”. As the analysis is limited to most fre-quent areas, it excludes important subﬁelds of AI (e.g., “plan-ning”) and some assignments are vague, with keywords suchas “design”, “identiﬁcation” or “prediction”.
Still, we see that this analysis is aligned with the ﬁrst facet
mentioned in the introduction, of whether the discipline ischaracterised by their techniques or their applications. Thisis not surprising, as it is a typical categorisation of disciplinesaccording to their techniques and applications, especially inengineering. A proper cataloguing effort (e.g., as done byACM and other associations for computing) would be an op-tion, but AI is too dynamic to allow for a stable set of termsfor a long period.
In the end, instead of a reactive approach focusing on the
trends, a more proactive stance towards the recognition of thesubdisciplines can be seen as a duty for scientiﬁc associa-tions, editorial boards and program chairs. Accordingly, AIresearchers should conﬁgure the landscape of AI
4and deter-
mine what is relevant or off-topic for a venue. They can alsodetermine what the subareas are, so that proper reviewers andsessions are allocated depending on the importance of eacharea. It is unusual, however, to conduct a more systematicanalysis of how these choices are made. Three remarkableexceptions are [Shah et al., 2017 ], where area relevance is
only examined on passing, [Fast and Horvitz, 2017 ]where
3http://www.scimagojr.com/shapeofscience/.
4https://aaai.org/Magazine/ailandscape.php.Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5181
●
●●●●●
●●●
●●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●
●●●●●
●
●●●●
●●●●
●●●●
●●●●●
●●●●●●●
●●●●●●●●●●
●●●●●●●●●●●●●●●
●●●
●
●●●●●
●●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●●
●●●●●
●●●●●
●●●●●●●●●
●●●●●●●
●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●
●
●
●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●●●●
●
●●●●
●●
●
●●
●●●
●●●
●●
●
●●●●●
●●●●●●●●●
●●●●●●●●●
●●●●●●●●●●
●●●
●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●
●●
●●
●●
●
●●●
●●●●●●●●●●●●
●
●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●●●●
●●●●
●
●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●
●●●
●●
●
●●●●●●●●●●●●●
●●●●●●●●●●Planning methods Search & constraint satisfaction Vision & roboticsMachine learning Multiagent systems NDWXUDO/DQJXDJH3URF
Heuristic search & optimization Humans & AI Knowledge representat ionApplications Cognitive Modeling Game theory
199701020
10203051015
02468−5051015
−100102005101520
05101520−1001020
1234561020304050
510Papers (%)
●●
1997●
1997●
19970
Venue ●AAAI IJCAI●●
1997●
1997
Figure 1: Papers published in AAAI (red dots) and IJCAI (blue triangles) conferences (1997-2017) separated by their area (topic “keyword”,
Table 3). Areas are mutually exclusive and sum up to 100% per year. General (dashed black line) tendencies and standard errors (bands) areshown for both data series together.
●●●
●●
●●●●●●●●●
●●●●●
●
●●●●
●●
●●
●●
●●●●
●●●●●
●
●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●●●●●●●●●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●●
●
●
●●●
●
●●
● ●●●●●●●●●●
●●●
●●●●●●●●●●●●●●●●●●●●●●
●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●
●●●●
●
●●●
●●●●
●●●
●●
●●●
●●
●●●●●●
●●●●●●●●●●●
●●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●●●●●●
●●●●●●●●●●●
●●●
●
●●●
●●
●
●●
●
●●
●●
●●
●●●
●
●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●
●●●●●●●● ●●●●●●●●●●●●●●●●●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●
●
●●●
● ● ●●
●
●●
●●●
●●●●●
●●●●●
●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●●
●●●●●●●●●●●●●●
●●●●
●●
●●●●
●●●
●●●
●●
●●●
●●●●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●
●●●●
●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●
●●
●●
●●
●●
●●●●
●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●
●●●●●●●
●●●
●●
●●●●●
●●●●●●
●●●●●●●●●●
●
●●●
●●
●●●●●●●● ●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●
●●●
●
●●
●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●●●
●●
●
●●
●●
●●●
●
●●●
●●
●●
●●●●●●●
●●●●●
●●
●●●
●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●
●●●●●●●●●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●●
●●
●●
●●
●●●
●●●●●●●●●●
●●
●●●●
●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●
●●
●●
●
●
●●●●
●●
●●
●●●●●
●●●
●●●●●
●●
●●
●●
●●
●●●
●●●●●
●●●●●
●●●●●●●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●
●●●
●●●●●●●●●●●●●●●●●●3lanning 6earch & constraint satisfaction 9ision & robotics0achine learning 0ultiagent systems 1atural/anguage3 roc
+euristic search & optimization +umans & $, .nowledge Uepresentation$pplications &ognitive modeling \*ame theory
1980 2000 2020 1980 2000 2020 1980 2000 2020−0.50.00.51.01.52.0
010010203040
0102030012
0123450.02.55.07.5
02468048
0510150102030
0102030Documents (%)
1980 2000 2020 1980 2000 2020 1980 20001980 2000 2020 1980 2000 2020 1980 2000
1980 2000 2020 1980 2000 2020 1980 2000
Figure 2: Documents included in AI topics (1970-2017) by area (topic “keyword”, Table 3). Legend as in Figure 1 (note that the x-axis is
different, with a much wider time span here).
the authors focus on views expressed about AI in the New
Y ork Times over a 30-year period in terms of public concernsas well as optimism, and [Moran et al., 2014 ], which focuses
on the venue keywords, including a cluster analysis on the2013 keyword set and a new series of keywords, which wereadapted by AAAI2014. Table 3 shows an integration of theirselection with the keywords of AAAI2014.
3 Historical Data: Analysis by Keywords
We used the keywords in Table 3 for a ﬁrst quantitative anal-ysis, using data obtained from two signiﬁcant sources:
•AAAI/IJCAI conferences (1997-2017). We obtaineddata of all the accepted papers for these two confer-ences from DBLP
5. This database represents informa-
tion about computer science (and thus AI) that comesmostly from their researchers (conference proceedingsand journals).
•AI topics documents (1905-2018). AI topics
6is an
archive kept by the AAAI, containing a variety of docu-ments (e.g. news, blog entries, conferences, journals andother repositories) that are collected automatically withNewsFinder [Buchanan et al., 2013 ].
With a mapping approach between terms and the categoriesin Table 3 as a representative list of subareas in AI, we sum-marised trends in a series of plots. For the AAAI/IJCAI con-ferences data, we used the keywords appearing in the pro-ceedings, and for AI topics we used the tags (substrings ap-
pearing in titles, abstracts and topics). Regarding confer-ences, other major venues (such as ECAI, ICML or NIPS)were not included due to the lack of keyword information in
5http://dblp.uni-trier.de/
6https://aitopics.org/misc/about.DBLP . Still, AAAI and IJCAI can be considered to be a rep-resentative basis for analysing AI trends proper.
Figure 1 shows the evolution of the areas in Table 3 for the
past 20 years using the AAAI/IJCAI data. Document countsare normalised to sum up to 100% per year. Standard errorsare also shown, where in some cases the thickness of the bandis very variable (e.g., “Human & AI” and “Game Theory”)due to data sparsity, or it cannot be computed due to insufﬁ-cient data available (e.g., “Heuristic search & optimisation”).
Figure 2 shows a similar evolution based on the AI topics
data. If we visually compare the same keywords between theplots found in Figures 1 and 2 (taking into account that axesare different), we see that both sources are rather consistentand highlight a few clear trends. For example, the categoriesheuristic search & optimisation and knowledge representa-
tion show a decreasing trend. Other categories seemed to
have peaks: cognitive modeling in the 1990s, strongly as-
sociated with the emergence of several cognitive architec-tures (e.g., ACT-R [Anderson et al., 1997 ], EPIC [Kieras
and Meyer, 1997 ]or SOAR [Newell, 1994 ]);planning meth-
ods around 2000, possibly due to the introduction of the ﬁrst
method for solving POMDP ofﬂine [Kaelbling et al., 1998 ],
jumpstarting its widespread use in robotics and automatedplanning; and multiagent systems around 2010, when they
were successfully applied to real world scenarios (e.g., au-tonomous vehicles [How et al., 2008 ]) and graphical applica-
tions (e.g., video games [Hagelb ¨ack and Johansson, 2008 ]).
Some others had valleys, such as natural language process-
ing (NLP) around 2000, showing a paradigm shift (from
deterministic phrase structure analysis before the 1990s tomore probabilistic NLP methods). Long Short-Term Memory(LSTM) recurrent neural net (RNN) models [Hochreiter and
Schmidhuber, 1997 ]have nowadays found rapid adoption dueProceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5182
to an increase in computational capacity in the 2010s. Only
machine learning had a clear increasing trend over time found
in both sources, where the steepest slope is located in the2010s and reveals the current relevance and attention, mostlycaused by deep learning and reinforcement learning. Over-all, this is in agreement with the general perception about theﬁeld.
While the plots show a conﬁrmatory evidence of the chang-
ing trend in AI, using area keywords may only present oneside of AI. The choice and relevance may be highly dependenton many factors such as the state of the art, the sociologicalperceptions or a moving target. Thus, we question whether itis possible to investigate the historical evoluation in a richerand more systematic manner.
4 A Faceted Analysis of the Evolution of AI
The two dimensions in Table 1 can be used as a basis for adifferent arrangement of tags and categories in our histori-cal analysis. However, instead of taking a dichotomous andmonolithic perspective about dimensions, we consider the useoffacets. This is motivated by realising that when choosing
any criterion for analysis, there is always a gradation, andsometimes this gradation does not follow a straight line be-tween two extremes, but an area among two or more edges,like a polygon. Hence, we use the term facet for this surface,
and the term edge for each of its boundaries. To illustrate this,
let us develop the ﬁrst two dimensions into facets:
•F1: The functionality facet (with edges ‘techniques’,
‘applications’ and ‘tasks’) analyses the functionality ofAI systems, such as knowledge representation, reason-ing, learning, communication, perception, action, etc.The processes of AI systems fall at the edge technique,
which relates to how AI systems “think”. We can alsocharacterise AI systems in terms of their behaviour (howthey “act”), leading to two different edges: the tasks they
solve and the application areas they are used in. This
facet can then be imagined as a triangle.
•F2: The referent facet (with edges ‘human’ and ‘uni-
versal’) distinguishes deﬁnitions or conceptions of AIsystems that go from an anthropocentric view to amore universal (theoretical) perspective. At the “hu-man” edge, AI could be characterised by being able tosolve all the tasks or by implementing all the intelli-gent processes humans are able to do. This view wouldbe closely related to what is known as human-like AI(see, e.g., [Lake et al., 2017 ]) or the view of AI as
pursuing human automation [Frey and Osborne, 2017;
Brynjolfsson and Mitchell, 2017 ]. On the other hand, if
AI is characterised at the “universal” edge, it would bedeﬁned in terms of a more theoretical set of problems orby implementing some abstract processes.
Using the AI topics data, for the functionality facet we can
look at several categories for each of the three edges: tech-niques, applications and tasks. We made tag-category map-pings for about 30 techniques, 20 applications and 30 tasks. Aselection of categories for each edge is shown here, althoughall the plots and results are presented in a separate link
7. Fig 3
7Data, code and plots, and an online R Shiny app are publicly●●●●
●●●●
●●●
●●
●●
●●●●●
●
●●●
●●
●
●●●●●●●●●●●●●●●●●●●
●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●
●●●●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●
●●●
●●
●
●●●
●●●●●●
●●●●●●
●●
●●●
●●●●
●●
●●●●●
●●
●●●
●●●
●●●●
●●●●
●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●
●●●●●
●
●●
●●●
●
●●
●●●
●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●●●●●●●●●●
●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●
●
●●●●
●●●●
●●●●
●●●●
●●●●●
●●●
●●
●●●
●●●
●●●●
●●●●●●●
●●●●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●
●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●
●
● ● ●●●
●●●
●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●
●●
●●●
●●●●●●●●●
●●●●●●●●●
●●●
●●●●
●●
●
●●
●
●●
●●
●●
●●●
●
●●●●●
●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●●
●
●●●●●●0achine learning(xpert system +euristics /isp
1980 2000 2020 1980 2000 2020 1980 2000 2020/ogic−based approaches2020 1980 2000 2020
3lanning & scheduling202004812
010203001020
01020304050−100102030
0102030Documents (%)1980 2000 1980 2000
(a) Top 6 techniques (in %) in AI topics
●●
●●●●
●
●●●●
●
●●●●●●●●
●●●●
●●●●
●
●●●
●●●
●●●
●●
●●●●
●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●
●●
●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●
●
●●
●●●●●●●
●
●●
●●●
●●
●
●●
●●
●●
●●●●●●●●
●
●●●●●●●●●●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●●
●●●●
●●●●●●●●●●●●●●
●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●
●●●
●●●●●●
●
●●●●●●
●●●
●●●
●●
●●●●
●
●●●●●●●
●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●
●●
●●
●●
●●
●●
●●
●●
●●●
●●●●●
●●●●●
●
●●
●●●
●●●
●●●
●●●●●●●●●
●●●●●●●●●
●●●●●
●●●●
●●●●
●●●●●●●●
●●●●
●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●
●●●●●●●●●●●
●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●
●●
●●●
●●●●
●●
●●
●●●●●●
●●
●●●●●
●●●●●
●●
●●
●●
●●●●●
●●
●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●
●●●
●
●●
●●●●●●
●●●
●
●●
●●●●
●
●●
●●●
●
●●
●●●●
●
●●
●
●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●
●
●●●●●●●
●●●●●●●●●●
●●●●●●●●
●.nowledge management 3ersonal assistant&onversational $, \*ames & videogames +ealth & medicine
1980 2000 2020 1980 2000 2020 1980 2000 20201980 2000 2020
/ogistics & transport2020 1980 2000 2020051015
●●●
●● ●●● ●5100510
0510152005101520
05101520 Documents (%)
01980 2000
(b) Top 6 applications (in %) in AI topics
●●●●●
●
●●
●●
●●●
●●●●●●
●
●●
●●
●●●●
●●
●●
●●
●●
●
●●
●●●●●●●
●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●
●●●●●●●●
●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●
●●●●
●●●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●
●●
●●●●
●
●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●
●●●●
●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●&hess 3oker 5obocup
1980 2000 2020 1980 2000 2020 1980 2000 20200102030
0.00.40.81.2
0510 Documents (%)
(c) Top 3 tasks (in %) in AI topics.
Figure 3: Functionality facet.
(a)shows the techniques with the highest peak in terms of
percentage. We see trends that are consistent with machine
learning taking more relevance, with roughly 4-5 times morecoverage over the 50 year period, and along the lines of whatwas seen in the previous section. Fig 3 (b)shows the six most
popular application areas. Here the trends are ﬂatter, althoughsome slight trends can be seen for health & medicine and per-
sonal assistants, in line with the insights reported in relatedwork [Fast and Horvitz, 2017 ]. Finally, Fig 3 (c)shows the
relevance of chess, especially in the 1980s and 1990s, andtheir decrease after Deep Blue beat Kasparov. Poker andRobocup are examples of tasks that became representativeover a small period of time because of either an algorithmicbreakthrough or the popularity of their competitions.
For the referent facet, we can also explore its edges. Fig 4
(a)shows six groups that are most associated with the edge
‘human’. The only relatively clear trends are a fall in psychol-
ogy and an increase in security, privacy and safety. While
the former may be due to the decline of cognitive modellingin general, the latter clearly underlines the rise of AI ethi-cal and privacy issues already pointed out by several govern-ments and agencies (e.g., [White House, 2016 ]), as well as the
appearance of new regulations (e.g., GPDR [EU Regulation,
2016 ]), ultimately triggered by public opinion and a concern
in the ﬁeld itself. Fig 4 (b)shows six groups that are most
available at: https://evoai.shinyapps.io/evoai/.Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5183
associated with the edge ‘universal’. The representations are
rather ﬂat for this edge, with very variable coverage over the
whole period under analysis, but no clear trends.
●●●●
●●●●●●●●●●
●●●●●
●●
●●
●●
● ●●●
●
●●●●
●●●●●
●
●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●
●●●
●●●●●●●●●●●●●
●●●●●●●●●●●●●●●
●●●●
●●
●●
●●●●●●
●●●
●●
●●●
● ●●●
●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●
●●●●
●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●
●●●
●●●●●
●●
●●
●●
●
●●
●
●●●●●●●●●
●
●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●
●●
●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●
●●
●
●●
●●●●
●●●
●●●
●
●●
●●
●
●●●
●●
●●●
●
●●
●●
●
●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●
●
●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●
●●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●
●●●●
●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●
●●●●●●●●●
●●●●●●●●●●●●●
●●●
●●
●●
●●●
●
●●
●●●●
●●●●
●
●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●,nterf & human−mach int 3sychology$utomation &ognitive sc ience +uman− level
1980 2000 2020 1980 2000 2020 1980 2000 20201980 2000 2020 1980 2000 2020
6ecurity, privDF\ safHW\2020036
051005101520
036−1012345
036Documents (%)1980 2000
(a) Top 6 on the human edge (in %) in AI topics
●●●
●●●●●●
●●●●●●●
●
●●
●●●●●
●●●●●●●
●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●
●●●●●●●●●●
●●●●●●●
●●
●●
●●
●●●●●●●●●●●●●●●●●●●●
●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●
●●
●●
●●●●●●
●●●
●
●●●
●●●
●
●●●●●
●●●●●●
●●
●●●
●●
●
●●
●●●
●●
●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●
●●
●
●
●●
●●●
●●●●
●
●●
●●●●●●
●●
●●●●●●
●●
●●
●
●
●●●●
●●●●●●
●●●●●
●●●●●●●●
●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●
●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●
● ● ●●●
●●●●●●●●
●
●●
● ●●●●●●
●●
●
●●
●
●●●●●
●●●●
●
●●
●
●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●
●●●●●
●
●●
● ●●●●●●
●●
●●
●
●●●●
●●●
●●●
●●●●
●●●●
●●
●●●●
●●●●●
●●
●●●●●●●●●●●●●●
●●●●●●●●●
●●●●●
●●
●
●●●●●●●●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●●●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●●●●●●/earning theory 7heoretical apprRDFKHV$utomata abs mach &omputability complex &orrect, opt verif
1980 2000 2020 1980 2000 2020 1980 2000 20201980 2000 2020
5ationality and logic2020 1980 2000 2020012345
05101504812
051015012345
−10123Documents (%)1980 2000
(b) Top 6 on the universal edge (in %) in AI topics.
Figure 4: Referent facet.
Both the functionality and the referent facets are usually
linked to a large number of terms and keywords, and they
give a broad overview that is familiar to AI researchers. How-ever, are these two facets sufﬁcient to characterise the object
and subject of AI? If we think of the elements of AI –its sys-
tems and services (the object)–, we need some other relevantfacets to characterise them, such as their generality, location
and embodiment. If we think of AI as a discipline (the sub-ject), we need to consider its paradigms, actors, character andnature. Let us describe these new seven facets:
•F3: The generality facet (with edges ‘speciﬁc’ and ‘gen-
eral’) considers whether AI is concerned with the cre-ation of speciﬁc systems (i.e., solving one particulartask) or the development of systems (and techniques)that solve (or can be applied to) a wide range of tasks.This dichotomy is usually referred to as narrow versusgeneral AI.
•F4: The location facet (with edges ‘integration’ and
‘distribution’) depicts where a system starts and ends.The very notion of centralised or distributed decisionmaking has been at the deﬁnitional level of agent for
a few decades, as well as the related notion of auton-
omy [Luck, 2017 ]. Also, there is an increasingly blurred
boundary on where human cognition ends and where it isassisted, extended or orthosed by AI [Ford et al., 2015 ]
(and vice versa, through human computation [Quinn and
Bederson, 2011 ]).
•F5: The embodiment facet (with edges ‘physical’ and
‘virtual’) distinguishes whether the AI system is linkedto a body or physical equipment or, on the contrary,is basically of algorithmic character, installed on de-vices, working on the cloud or migrating between differ-ent platforms, usually dealing with elements in a digitalworld.
•F6: The paradigm facet (with edges ‘discrete’, ‘contin-
uous’ and ‘probabilistic’) distinguishes the underlyingapproaches behind many principles and tools of AI. Atthe discrete edge, we see those problems and methodsseen in a combinatorial way, where a logical or evolu-tionary process combines or applies operators. At thecontinuous edge, we see quantitative optimisation prob-lems tackled with gradient descent, kernels and matrixoperations. And, on the probabilistic edge, problems areseen in a probabilistic, stochastic or statistical view.
•F7: The actor facet (with edges ‘academia’, ‘industry’,
‘government’ and ‘independent’) identiﬁes who are thedriving forces behind AI as a discipline. This facet con-
siders who is most relevant in –and ultimately steering–the discipline according to its current challenges, regu-lations and potential future advances.
•F8: The character facet (with edges ‘empirical’ and
‘theoretical’) determines whether AI is guided by exper-iments, like other empirical disciplines, or whether it isof a more theoretical nature. Note that this facet is differ-ent from the referent facet (e.g., a non-anthropocentricview can be very experimental).
•F9: The nature facet (with edges ‘technology’, ‘engi-
neering’, ‘science’ and ‘philosophy’) describes AI ac-cording to
what kind of discipline it is. This loosely cor-
responds respectively to whether it creates devices andproducts, solves problems, answers/asks questions.
We do not show individual categories for these facets due tospace limitations. Instead, we will look at the aggregationof categories by edges, assuming the edges are exclusive (so
their share is always 100%). In a way, this can be understoodas a non-monolithic view of polarities in sentiment analysis.Fig. 5 shows this share for the nine facets, where the data has
been smoothed with a moving average ﬁlter. We see trendsfor many of them: an increase of the relevance of applica-tions for F1 and the focus on more speciﬁc systems for F3 (thenumber, but also the diversiﬁcation, of applications may ex-plain this), more virtual systems for F5 (with the appearanceof many new AI experimentation and evaluation platforms
[Hern ´andez-Orallo et al., 2017 ]), more continuous paradigms
for F6 (given the success of deep learning and other methods
based on this paradigm), more industry for F7 (and mostly atthe cost of academia), a more empirical character for F8 anda view of the discipline in a more technological way for F9(which may be one possible reason for the current concern inAI safety and governance, usually harder to handle when theengineering and scientiﬁc perspectives are weak). Overall,we can also see that the trends in some facets, especially theshifts from the 2000s, may be strongly related (facets F1, F3,F7 and F9).
One important insight that we can gain from the visual out-
put shown in all these plots is that some trends peak, whilesome others are cyclic. Consequently, the plots have explana-tory and conﬁrmatory value about the relevance of differentareas and perspectives of how AI is deﬁned. However, wehave to be cautious and not use them for forecasting. In fact,Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5184

    6KDUHWHFKQLTXHV
DSSOLFDWLRQV
WDVNVhuman
universalShare

    specific
generalShare

integration
distributionShare

    physical
virtualShare

    GLVFUHWH
FRQWLQXRXVSUREDELOLVWLF6KDUH

DFDGHPLD
LQGXVWU\
JRYHUQPHQW6KDUHLQGHSHQGHQW

    empirical
theoreticalShare

    engineering
science
philosophyShare

    WHFKQRORJ\
Figure 5: Share of all edges per facet in AI topics (facets F1-F9,
ordered left to right, top to bottom).
from a governance perspective, there is a known bias towards
reinforcing the dominant view (the winner takes all) or evenconfounding the part with the whole (e.g., deep learning withmachine learning, and machine learning with AI). Therefore,this quantitative analysis could be used to inform compen-satory actions with some less popular but important areas, sothat the discipline is ready when dominant paradigms shift orsome of their pathways plateau.
5 Qualitative Analysis
The facets are also useful to analyse AI in a more qualitativemanner. For example, F1 to F5 are based on the object of AI
and can characterise either particular AI systems or AI as adiscipline. On the other hand, F6 to F9 focus on the subject,
being more methodological for the discipline as a whole.
Let us examine the ﬁrst ﬁve facets from the perspective of
the AI systems and components. In the end, if we set the goalof AI as building and understanding AI systems then the con-tours of the discipline will be clear as far as we have a clearnotion of what an AI system is. Nonetheless, the question re-mains: how can we characterise this in a stable way for theyears to come?
Looking forward, the referent facet (F2) seems to become
very relevant. We are aware of AI systems already exceed-ing some human capabilities and this will continue to be soduring the century. Considering human intelligence as a goalhas been a driving force (and will continue to be so in thenear future), but it is rather short-sighted. Instead, placing AIas exploring a more universal ‘intelligence landscape’ is notonly more inclusive about what AI is (systems solving taskshumans cannot solve would still be part of AI), but representsa Copernican view where humans are no longer at the centre(see Figure 6). For instance, the terms human-level machine
intelligence orhuman-level artiﬁcial intelligence have many
issues (is it for all tasks?, what is an average human?, how toextrapolate beyond human level?). Actually, the term –albeitnot the deﬁnition– is usually replaced by high-level machine
intelligence [M¨uller and Bostrom, ]or simply AGI (which
should rather refer to facet F3).
Of course, even if humans are not taken as a reference, they

Figure 6: Top: Anthropocentric AI. Bottom: AI in the ‘intelligence
landscape’.
still have to be put at the central position due to the interac-
tion and impact of AI on them. But this is usually referredto as human-centred artiﬁcial intelligence. For instance, ap-
plications must be prioritised according to human needs, andfor many of these applications we want more ‘human-like’ AI
[Lake et al., 2017; Marcus, 2018 ], more ‘human-beneﬁcial’
AI (i.e., safer and taking the values of humans into account[Russell et al., 2015; Amodei et al., 2016 ]) and also more
‘human-ethical’ AI [Goldsmith and Burton, 2017 ]. Note that
we do not want to replicate certain behaviours humans dis-
play (e.g., gender bias) in our AI systems. In many cases, itis not that AI should automate all tasks humans do, or per-form better than humans, but AI should also do other tasksor perform very differently. Consequently, there should beno reason why some tasks are excluded from AI when theyare solved in a different way. This –known as the ‘AI effect’
[McCorduck, 2004 ]– is partly motivated because of the spe-
cialisation of the solution, as we will discuss below.
The bottom view of Figure 6 opens the contours of AI but
requires the deﬁnition of the intelligence landscape. That
takes us back to the range around functionality facet and Ta-
ble 1 (right), between thinking and acting. We have seen inthe quantitative analysis that techniques, tasks and applica-tions are volatile, so any enumeration of them is going to beincomplete.
An alternative view based on skills and abilities can endure
changes much better [Hern ´andez-Orallo, 2016; 2017 ]. For in-
stance, perception, learning and planning have been consub-stantial in AI. Systems having some of these skills are recog-nised as AI without further information about the techniquesor the particular tasks the elements are applied to, in the sameway we do for non-human animals. This is actually the truecore of facet F1.
But skills and abilities can only be characterised by look-
ing at facets F3, F4 and F5. For instance, having ndifferent
systems for navigating ndifferent buildings would not con-
vince us that AI systems today have very good navigationskills (F3, generality). They would just be narrow systemsnot really having the skill. Similarly, the system might ac-tually be controlled by many subsystems on the Internet, us-ing real-time data from sensors around the building and evenfrom the devices the humans in the building are using (F4,location). Finally, it is not the same to physically navigate aProceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5185
real building than a virtual scenario (F5, embodiment).
In fact, we are heading towards the direction of AI ser-
vices instead of AI systems, which is typically known as
cognition as a service [Spohrer and Banavar, 2015 ].I n
cognitive services research, the focus is on bringing downthe overall cost and increasing general performance. How-ever, certain considerations must be taken into account, espe-cially in the context of automation [Frey and Osborne, 2017;
Brynjolfsson and Mitchell, 2017 ]. For example, what is
the difference between full automation and efﬁcient semi-automation? Furthermore, the blurred line between workcontributed by AI systems and humans makes it even harderto differentiate how performance can be attributed realisti-cally. Also, some tasks can be ﬁnally automated in wayswhere AI plays a secondary role, by changes in the logicalor physical conﬁguration. For instance, an intelligent robotcan be helped by a proper design of the body, usually referredto as morphological intelligence [Winﬁeld, 2017 ].
Interestingly, the phenomenon of AI being more powerful
because of the use of human data or human computation ismirrored by the extended mind [Clark and Chalmers, 1998 ].
The human mind is viewed as incorporating all of its mindtools, from pen and paper to cognitive assistants. The con-tours of where the human mind ends, and whether the sur-rounding tools and devices are included, is also important todetermine where the AI system ends too, and who really pro-vides the services. Hence, the facets F3 to F5 will becomemore relevant and sophisticated in the future. Perhaps insteadof autonomy, we will need to trace how much each part con-tributes to the whole ability of the whole system or service.
Finally, as we mentioned above, the four last facets have
a more methodological stance. For instance, it seems irrel-evant what paradigm from facet F6 is most important at the
moment. For instance, arcade games (such as the Atari ALEbenchmark) can now be played with relatively good perfor-mance using deep reinforcement learning [Mnih et al., 2015 ],
evolutionary programming [Kelly and Heywood, 2017 ]or
neuroevolution [Hausknecht et al., 2014 ]. However, if it is
the case that more and more AI systems are using continu-ous, gradient-descent, approaches rather than more discrete,combinatorial, approaches, this may have an impact on thecontours with neighbouring disciplines. AI would be clash-ing more often with algebra, optimisation and statistics (oreven physics), and would be pulled away from the traditionallogic and discrete mathematics (or even evolution). Simi-larly, whether approaches are more empirical or theoretical,according to facet F8, is not necessarily a reason to be con-
sidered more or less AI, but can still affect the perception ofthe ﬁeld, and the boundaries with some other disciplines.
Facets F7 (actors) and F9 (nature) are related, as it is mostly
academia that cares for scientiﬁc and philosophical questionsabout AI. If we look at Figure 6 again, all actors will be in-terested in building systems and services that cover the intel-ligence landscape for an increasing number of applications,considering AI as a technology and focusing on engineering.But it is mostly (or only) academia which is interested in whatthe intelligence landscape looks like, the evaluation of wheresystems are located in this space and what the implicationsare while covering this landscape. These and some other ar-eas will have to be prioritised by academia (and governmentfunding) in order to have more vision and governance withinthe ﬁeld of AI.
6 Conclusion
Artiﬁcial intelligence, as any other scientiﬁc discipline, ispartly a social phenomenon, and its deﬁnition and contourare highly inﬂuenced by its actors and stakeholders. Wewill always need to track and update the ﬁeld in many ways,from the use of self-reported questionnaires
8to data from the
venues and news related to AI.
In this paper, we presented a series of facets and associ-
ated edges to analyse the historical evolution of AI, and gath-
ered some insight into its future. The data of venues and AIrepositories were useful for quantitative analyses. Moreover,the data and the mapping between tags and categories arepublicly provided so that others can apply the same facetedframework to other sources of data about AI. Finally, thefacets represent a framework to discuss, in a more qualita-
tive way, the past, present and future of AI, and its relation toother disciplines.
Acknowledgements
We thank the support from the Leverhulme Trust via theLeverhulme Centre for the Future of Intelligence (CFI).F. Mart ´ınez-Plumed was supported by INCIBE (Ayudas para
la excelencia de los equipos de investigaci ´on avanzada
en ciberseguridad). J. H-Orallo received a Salvador deMadariaga grant (PRX17/00467) from the Spanish MECDfor a research stay at the CFI, Cambridge, and a BEST grant(BEST/2017/045) from the GV A for another research stay atthe CFI. This work was also supported by the EU (FEDER),and the Spanish MINECO under grant TIN 2015-69175-C4-1-R and the Generalitat V alenciana PROMETEOII/2015/013.
References
[Amodei et al., 2016 ]Dario Amodei, Chris Olah, Jacob Steinhardt,
Paul Christiano, John Schulman, and Dan Man ´e. Concrete prob-
lems in ai safety. arXiv preprint arXiv:1606.06565, 2016.
[Anderson et al., 1997 ]John R Anderson, Michael Matessa, and
Christian Lebiere. Act-r: A theory of higher level cognition
and its relation to visual attention. Human-Computer Interaction,
12(4):439–462, 1997.
[Boden, 2016 ]Margaret A Boden. AI: Its nature and future. Oxford
University Press, 2016.
[Brynjolfsson and Mitchell, 2017 ]Erik Brynjolfsson and Tom
Mitchell. What can machine learning do? Workforce implica-tions. Science, 358(6370):1530–1534, 2017.
[Buchanan et al., 2013 ]Bruce G Buchanan, Joshua Eckroth, and
Reid Smith. A virtual archive for the history of ai. AI Maga-
zine, 34(2):86, 2013.
[Buchanan, 2005 ]Bruce G Buchanan. A (very) brief history of ar-
tiﬁcial intelligence. Ai Magazine, 26(4):53, 2005.
[Clark and Chalmers, 1998 ]Andy Clark and David Chalmers. The
extended mind. analysis, 58(1):7–19, 1998.
8For example, [M¨uller and Bostrom, ]or http://agisi.org/Survey
intelligence.html.Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5186
[EU Regulation, 2016 ]General Data Protection EU Regulation.
Regulation (EU) 2016/679 on the protection of natural persons
with regard to the processing of personal data and on the freemovement of such data. Ofﬁcial Journal of the European Union
(OJ), 59:1–88, 2016.
[Fast and Horvitz, 2017 ]Ethan Fast and Eric Horvitz. Long-term
trends in the public perception of artiﬁcial intelligence. In AAAI,
pages 963–969, 2017.
[Fetzer, 1990 ]James H Fetzer. What is artiﬁcial intelligence?
pages 3–27, 1990.
[Ford et al., 2015 ]Kenneth M Ford, Patrick J Hayes, Clark Gly-
mour, and James Allen. Cognitive orthoses: toward human-centered ai. AI Magazine, 36(4):5–8, 2015.
[Frey and Osborne, 2017 ]Carl Benedikt Frey and Michael A Os-
borne. The future of employment: how susceptible are jobs tocomputerisation? T echnological F orecasting and Social Change,
114:254–280, 2017.
[Goldsmith and Burton, 2017 ]Judy Goldsmith and Emanuelle Bur-
ton. Why teaching ethics to ai practitioners is important. In AAAI,
pages 4836–4840, 2017.
[Hagelb ¨ack and Johansson, 2008 ]Johan Hagelb ¨ack and Stefan J
Johansson. Using multi-agent potential ﬁelds in real-time strat-egy games. In Proc. of the 7 Int. Joint Conf. on Autonomous
agents and multiagent systems-V olume 2, pages 631–638, 2008.
[Hausknecht et al., 2014 ]Matthew Hausknecht, Joel Lehman,
Risto Miikkulainen, and Peter Stone. A neuroevolution approachto general atari game playing. IEEE Trans. on Computational
Intelligence and AI in Games, 6(4):355–366, 2014.
[Hern ´andez-Orallo et al., 2017 ]Jose Hern ´andez-Orallo, Marco Ba-
roni, Jordi Bieger, Nader Chmait, David L Dowe, KatjaHofmann, Fernando Mart ´ınez-Plumed, Claes Stranneg ˚ard, and
Kristinn R Th ´orisson. A new ai evaluation cosmos: Ready to
play the game? AI Magazine, AAAI, 38(3):66–69, 2017.
[Hern ´andez-Orallo, 2016 ]Jos´e Hern ´andez-Orallo. The measur
 e of
all minds: evaluating natural and artiﬁcial intelligence. Cam-bridge University Press, 2016.
[Hern ´andez-Orallo, 2017 ]Jos´e Hern ´andez-Orallo. Evaluation in
artiﬁcial intelligence: from task-oriented to ability-oriented mea-surement. Artiﬁcial Intelligence Review, 48(3):397–447, Oct
2017.
[Hochreiter and Schmidhuber, 1997 ]Sepp Hochreiter and J ¨urgen
Schmidhuber. Long short-term memory. Neural computation,
9(8):1735–1780, 1997.
[How et al., 2008 ]Jonathan P How, Brett Behihke, Adrian Frank,
Daniel Dale, and John Vian. Real-time indoor autonomous vehi-cle test environment. IEEE control systems, 28(2):51–64, 2008.
[Kaelbling et al., 1998 ]Leslie Pack Kaelbling, Michael L Littman,
and Anthony R Cassandra. Planning and acting in partially ob-servable stochastic domains. Artiﬁcial intelligence, 101(1-2):99–
134, 1998.
[Kelly and Heywood, 2017 ]Stephen Kelly and Malcolm I Hey-
wood. Multi-task learning in atari video games with emergenttangled program graphs. In Proceedings of the Genetic and Evo-
lutionary Computation Conference, pages 195–202. ACM, 2017.
[Kieras and Meyer, 1997 ]David E. Kieras and David E. Meyer. An
overview of the epic architecture for cognition and performancewith application to human-computer interaction. Hum.-Comput.
Interact., 12(4):391–438, 1997.
[Lake et al., 2017 ]Brenden M Lake, Tomer D Ullman, Joshua B
Tenenbaum, and Samuel J Gershman. Building machines thatlearn and think like people. BBS, 40, 2017.
[Legg and Hutter, 2007 ]Shane Legg and Marcus Hutter. A collec-
tion of deﬁnitions of intelligence. Advances in Artiﬁcial General
Intelligence: Concepts, Architectures and Algorithms, in Fron-tiers in Artiﬁcial Intelligence and applications, 157:17–24, 2007.[Lehman-Wilzig, 1981 ]Sam N Lehman-Wilzig. Frankenstein un-
bound: towards a legal deﬁnition of artiﬁcial intelligence. Fu-
tures, 13(6):442–457, 1981.
[Luck, 2017 ]Michael Luck. IJCAI-2017 Special Track on AI &
Autonomy, 2017.
[Marcus, 2018 ]Gary Marcus.
 Deep learning: A critical appraisal.
arXiv preprint arXiv:1801.00631, 2018.
[McCarthy et al., 2006 ]John McCarthy, Marvin L Minsky,
Nathaniel Rochester, and Claude E Shannon. A proposal for theDartmouth summer research project on artiﬁcial intelligence,august 31, 1955. AI magazine, 27(4):12, 2006.
[McCarthy, 1998 ]John McCarthy. What is artiﬁcial intelligence?
1998.
[McCorduck, 2004 ]Pamela McCorduck. Machines who think: A
personal inquiry into the history and prospects of artiﬁcial intel-ligence. AK Peters Natick, MA, 2004.
[Minsky, 1968 ]M. L. Minsky. Semantic information processing.
1968.
[Mnih et al., 2015 ]V olodymyr Mnih, Koray Kavukcuoglu, David
Silver, Andrei A Rusu, Joel V eness, Marc G Bellemare, AlexGraves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostro-vski, et al. Human-level control through deep reinforcementlearning. Nature, 518(7540):529, 2015.
[Moor, 2006 ]James Moor. The Dartmouth college AI conference:
The next ﬁfty years. AI Magazine, 27(4):87, 2006.
[Moran et al., 2014 ]Kelly Moran, Byron C Wallace, and Carla E
Brodley. Discovering better AAAI keywords via clustering withcommunity-sourced constraints. In AAAI, pages 1265–1271,
2014.
[M¨uller and Bostrom, ]Vincent C M ¨uller and Nick Bostrom. Fu-
ture progress in AI: A survey of expert opinion. In Fundamental
issues of AI, pages=555–572, year=2016, publisher=Springer.
[Newell, 1994 ]Allen Newell. Uniﬁed theories of cognition. Har-
vard University Press, 1994.
[Nilsson, 2009 ]Nils J Nilsson. The quest for artiﬁcial intelligence.
Cambridge University Press, 2009.
[Niu et al., 2016 ]Jiqiang Niu, Wenwu Tang, Feng Xu, Xiaoyan
Zhou, and Yanan Song. Global research on AI from 1990–2014:Spatially-explicit bibliometric analysis. ISPRS Int. Journal of
Geo-Information, 5(5):66, 2016.
[Quinn and Bederson, 2011 ]Alexander
 J Quinn and Benjamin B
Bederson. Human computation: a survey and taxonomy of agrowing ﬁeld. In SIGCHI conf. on human factors in computing
systems, pages 1403–1412. ACM, 2011.
[Russell and Norvig, 2009 ]Stuart Jonathan Russell and Peter
Norvig. Artiﬁcial intelligence: a modern approach, 3rd Edition.
Prentice Hall, 2009.
[Russell et al., 2015 ]Stuart Russell, Daniel Dewey, and Max
Tegmark. Research priorities for robust and beneﬁcial artiﬁcialintelligence. Ai Magazine, 36(4):105–114, 2015.
[Shah et al., 2017 ]Nihar B Shah, Behzad Tabibian, Krikamol
Muandet, Isabelle Guyon, and Ulrike V on Luxburg. Designand analysis of the nips 2016 review process. arXiv preprint
arXiv:1708.09794, 2017.
[Solomonoff, 2017 ]Grace Solomonoff. Ray Solomonoff and the
Dartmouth summer research project in artiﬁcial intelligence,1956, 2017.
[Spohrer and Banavar, 2015 ]Jim Spohrer and Guruduth Banavar.
Cognition as a service: an industry perspective. AI Magazine,
36(4):71–86, 2015.
[White House, 2016 ]Executive Ofﬁce of the President
White House. Preparing for the Future of Artiﬁcial Intelli-
gence. CreateSpace, USA, 2016.
[Winﬁeld, 2017 ]Alan FT Winﬁeld. How intelligent is your intelli-
gent robot? arXiv preprint arXiv:1712.08878, 2017.Proceedings of the Twenty-Seventh International Joint Conference on Artiﬁcial Intelligence (IJCAI-18)
5187

Fernando Martínez-Plumed
Bao Sheng Loe
Peter Flach
Seán Ó hÉigeartaigh
Karina Vold
José Hernández-Orallo


Artificial General Intelligence

Cognitive Technologies
Managing Editors: D. M. Gabbay J. Siekmann
Editorial Board: A. Bundy J. G. Carbonell
M. Pinkal H. Uszkoreit M. V eloso W . W ahlster
M. J. W ooldridge
Advisory Board:
Luigia Carlucci Aiello
Franz Baader
W olfgang Bibel
Leonard BolcCraig Boutilier
Ron Brachman
Bruce G. BuchananAnthony CohnArtur d’Avila Garcez
Luis Fariñas del Cerro
Koichi FurukawaGeorg Gottlob
Patrick J. Hayes
James A. HendlerAnthony JamesonNick Jennings
Aravind K. Joshi
Hans KampMartin Kay
Hiroaki Kitano
Robert KowalskiSarit Kraus
Maurizio Lenzerini
Hector LevesqueJohn LloydAlan Mackworth
Mark Maybury
T om Mitchell
Johanna D. MooreStephen H. Muggleton
Bernhard Nebel
Sharon OviattLuis PereiraLu Ruqian
Stuart Russell
Erik SandewallLuc Steels
Oliviero Stock
Peter StoneGerhard StrubeKatia Sycara
Milind T ambe
Hidehiko T anakaSebastian Thrun
Junichi T sujii
Kurt VanLehnAndrei V oronkov
T oby W alsh
Bonnie W ebber
Ben Goertzel
Cassio Pennachin (Eds.)
Artiﬁcial
GeneralIntelligence
With 42 Figures and 16 T ables
123
Editors:
Ben Goertzel
Cassio Pennachin
AGIRI – Artiﬁcial General Intelligence Research Institute
1405 Bernerd PlaceRockville, MD 20851USA
ben@agiri.org
cassio@agiri.org
Managing Editors:
Prof. Dov M. Gabbay
Augustus De Morgan Professor of LogicDepartment of Computer Science, King’s College LondonStrand, London WC2R 2LS, UK
Prof. Dr. Jörg Siekmann
Forschungsbereich Deduktions- und Multiagentensysteme, DFKIStuhlsatzenweg 3, Geb. 43, 66123 Saarbrücken, Germany
Library of Congress Control Number: 2006937159
ACM Computing Classiﬁcation (1998): F .1, F .4, H.5, I.2, I.6
ISSN 1611-2482
ISBN-10 3-540-23733-X Springer Berlin Heidelberg New Y ork
ISBN-13 978-3-540-23733-4 Springer Berlin Heidelberg New Y ork
This work is subject to copyright. All rights are reserved, whether the whole or part of the material
is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microﬁlm or in any other way, and storage in data banks. Duplicationof this publication or parts thereof is permitted only under the provisions of the German Copyright
Law of September 9, 1965, in its current version, and permission for use must always be obtained from
Springer. Violations are liable for prosecution under the German Copyright Law.
Springer is a part of Springer Science+Business Media
springer.com
© Springer-V erlag Berlin Heidelberg 2007
The use of general descriptive names, registered names, trademarks, etc. in this publication does not
imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.
Cover Design: KünkelLopka, Heidelberg
Typesetting: by the EditorsProduction: LE-T EXJ e l o n e k ,S c h m i d t&V ö c k l e rG b R ,L e i p z i g
Printed on acid-free paper 45/3100/YL 5 4 3210
Preface
“Only a small community has concentrated on general intelligence. No
one has tried to make a thinking machine ...
The bottom line is that we really haven’t progressed too far toward a
truly intelligent machine. We have collections of dumb specialists in
small domains; the true majesty of general intelligence still awaits our
attack. ...We have got to get back to the deepest questions of AI and general
intelligence...”
–M a r v i nM i n s k y
as interviewed in Hal’s Legacy , edited by David Stork, 2000.
Our goal in creating this edited volume has been to ﬁll an apparent gap
in the scientiﬁc literature, by providing a coherent presentation of a body of
contemporary research that, in spite of its integral importance, has hitherto
kept a very low proﬁle within the scientiﬁc and intellectual community. This
body of work has not been given a name before; in this book we christen it“Artiﬁcial General Intelligence” (AGI). What distinguishes AGI work from
run-of-the-mill “artiﬁcial intelligence” research is that it is explicitly focused
on engineering general intelligence in the short term. We have been activeresearchers in the AGI ﬁeld for many years, and it has been a pleasure to
gather together papers from our colleagues working on related ideas from
their own perspectives. In the Introduction we give a conceptual overview of
the AGI ﬁeld, and also summarize and interrelate the key ideas of the papers
in the subsequent chapters.
Of course, “general intelligence” does not mean exactly the same thing
to all researchers. In fact it is not a fully well-deﬁned term, and one of the
issues raised in the papers contained here is how to deﬁne general intelligencein a way that provides maximally useful guidance to practical AI work. But,
VI Preface
nevertheless, there is a clear qualitative meaning to the term. What is meant
by AGI is, loosely speaking, AI system s that possess a reasonable degree of
self-understanding and autonomous self-control, and have the ability to solve
a variety of complex problems in a variety of contexts, and to learn to solve
new problems that they didnt know about at the time of their creation. A
marked distinction exists between practical AGI work and, on the other hand:
•Pragmatic but specialized “narrow AI” research which is aimed at cre-
ating programs carrying out speciﬁc tasks like playing chess, diagnosing
diseases, driving cars and so forth (most contemporary AI work falls into
this category.)
•Purely theoretical AI research, which is aimed at clarifying issues regarding
the nature of intelligence and cognition, but doesnt involve technical details
regarding actually realizing artiﬁcially intelligent software.
Some of the papers presented here come close to the latter (purely theo-
retical) category, but we have selected them because the theoretical notions
they contain seem likely to lead to such technical details in the medium-termfuture, and/or resonate very closely with the technical details of AGI designs
proposed by other authors.
The audience we intend to reach includes the AI community, and also the
broader community of scientists and students in related ﬁelds such as philoso-
phy, neuroscience, linguistics, psychology, biology, sociology, anthropology andengineering. Signiﬁcantly more so than narrow AI, AGI is interdisciplinary in
nature, and a full appreciation of the general intelligence problem and its
various potential solutions requires one to take a wide variety of diﬀerentperspectives.
Not all signiﬁcant AGI researchers are represented in these pages, but we
have sought to bring together a multiplicity of perspectives, including many
that disagree with our own. Bringing a diverse body of AGI research together
in a single volume reveals the common themes among various researchers work,and makes clear what the big open questions are in this vital and critical area
of research. It is our hope that this book will interest more researchers and
students in pursuing AGI research themselves, thus aiding in the progress ofscience.
In the three years that this book has been in the making, we have noticed
a signiﬁcant increase in interest in AGI-related research within the academic
AI community, including a number of small conference workshops with titles
related to “Human-Level Intelligence.” We consider this challenge to the over-whelming dominance of narrow-AI an extremely positive move; however, we
submit that “Artiﬁcial General Intelligence” is a more sensible way to concep-
tualize the problem than “Human-Level Intelligence.” The AGI systems andapproaches described in these pages are not necessarily oriented towards emu-
lating the human brain; and given the heterogeneity of the human mind/brain
and its highly various levels of competence at various sorts of tasks, it seems
very diﬃcult to deﬁne “Human-Level Intelligence” in any way that is generally
Preface VII
applicable to AI systems that are fundamentally non-human-like in concep-
tion. On the other hand, the work of Hutter and Schmidhuber reported hereprovides a reasonable, abstract mathematical characterization of general intel-
ligence which, while not in itself providing a practical approach to AGI design
and engineering, at least provides a conceptually meaningful formalization of
the ultimate goal of AGI work.
The grand goal of AGI remains mostly unrealized, and how long it will
be until this situation is remedied remains uncertain. Among scientists who
believe in the fundamental possibility of strong AI, the most optimistic se-
rious estimates we have heard are in the range of 5-10 years, and the mostpessimistic are in the range of centuries. While none of the articles contained
here purports to present a complete solution to the AGI problem, we believe
that they collectively embody meaningful conceptual progress, and indicate
clearly that the direct pursuit of AGI is an endeavor worthy of signiﬁcant
research attention.
Contents
Contemporary Approaches to Artiﬁcial General Intelligence
Cassio Pennachin, Ben Goertzel
1 AB r i e fH i s t o r yo fA G I........................................ 1
1 . 1 S o m eH i s t o r i c a lA G I - R e l a t e dP r oj e c t s...................... 2
2 What Is Intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1 The Psychology of Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 . 2 T h eT u r i n gT e s t......................................... 8
2.3 A Control Theory Approach to Deﬁning Intelligence . . . . . . . . . 82.4 Eﬃcient Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 The Abstract Theory of General Intelligence . . . . . . . . . . . . . . . . . . . . . 11
4 T o w a r daP r a g m a t i cL o g i c..................................... 1 5
5 E m u l a t i n gt h eH u m a nB r a i n................................... 1 7
6 E m u l a t i n gt h eH u m a nM i n d................................... 1 97 Creating Intelligence by Creating Life . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
8 The Social Nature of Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
9 I n t e g r a t i v eA p p r o a c h e s........................................ 2 61 0 T h eO u t l o o kf o rA G I ......................................... 2 7
A c k n o w l e d g m e n t s................................................ 2 8
R e f e r e n c e s ...................................................... 2 8
The Logic of Intelligence
Pei Wang
1 Intelligence and Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.1 To Deﬁne Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.2 A Working Deﬁnition of Intelligence. . . . . . . . . . . . . . . . . . . . . . . . 33
1 . 3 C o m p a r i s o nW i t hO t h e rD e ﬁ n i t i o n s........................ 3 51 . 4 L o g i ca n dR e a s o n i n gS y s t e m s ............................. 4 0
2 T h eC o m p o n e n t so fN A R S .................................... 4 3
2.1 Experience-Grounded Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2 . 2 I n h e r i t a n c eS t a t e m e n t.................................... 4 5
2 . 3 C a t e g o r i c a lL a n g u a g e .................................... 4 72 . 4 S y l l o g i s t i cI n f e r e n c eR u l e s ................................ 4 8
2 . 5 C o n t r o l l e dC o n c u r r e n c yi nD y n a m i cM e m o r y................ 5 0
3 T h eP r o p e r t i e so fN A R S ...................................... 5 2
3 . 1 R e a s o n a b l eS o l u t i o n s..................................... 5 2
XC o n t e n t s
3 . 2 U n i ﬁ e dU n c e r t a i n t yP r o c e s s i n g............................ 5 3
3 . 3 N A R Sa saP a r a l l e la n dD i s t r i b u t e dN e t w o r k................ 5 43 . 4 R e s o u r c e sC o m p e t i t i o n ................................... 5 6
3 . 5 F l e x i b l eB e h a v i o r s ....................................... 5 7
3 . 6 A u t o n o m ya n dC r e a t i v i t y................................. 5 8
4 C o n c l u s i o n s.................................................. 6 0
R e f e r e n c e s ...................................................... 6 0
The Novamente Artiﬁcial Intelligence Engine
Ben Goertzel, Cassio Pennachin
1 I n t r o d u c t i o n................................................. 6 3
1 . 1 T h eN o v a m e n t eA G IS y s t e m .............................. 6 41.2 Novamente for Knowledge Management and Data Analysis . . . . 65
2 E n a b l i n gS o f t w a r eT e c h n o l o g i e s ................................ 6 7
2.1 A Distributed Software Architecture for Integrative AI . . . . . . . 68
2.2 Database Integration and Knowledge Integration . . . . . . . . . . . . 70
3 What Is Artiﬁcial General Intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.1 What Is General Intelligence? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3 . 2 T h eI n t e g r a t i v eA p p r o a c ht oA G I.......................... 7 5
3.3 Experiential Interactive Learning and Adaptive Self-modiﬁcation 77
4 T h eP s y n e tM o d e lo fM i n d .................................... 8 0
5 T h eN o v a m e n t eA G ID e s i g n ................................... 8 3
5 . 1 A nI n t e g r a t i v eK n o w l e d g eR e p r e s e n t a t i o n................... 8 4
5 . 2 T h eM i n dO S ........................................... 8 8
5 . 3 A t o mT y p e s ............................................ 9 15 . 4 N o v a m e n t eM a p s ........................................ 9 4
5 . 5 M i n dA g e n t s............................................ 9 5
5 . 6 M a pD y n a m i c s .......................................... 9 65 . 7 F u n c t i o n a lS p e c i a l i z a t i o n ................................. 9 9
5 . 8 N o v a m e n t ea n dt h eH u m a nB r a i n..........................1 0 0
5 . 9 E m e r g e n tS t r u c t u r e s .....................................1 0 2
6 I n t e r a c t i n gw i t hH u m a n sa n dD a t aS t o r e s.......................1 0 4
6 . 1 D a t aS o u r c e s............................................1 0 56 . 2 K n o w l e d g eE n c o d i n g.....................................1 0 6
6 . 3 Q u e r y i n g ...............................................1 0 7
6 . 4 F o r m a lL a n g u a g eQ u e r i e s.................................1 0 86 . 5 C o n v e r s a t i o n a lI n t e r a c t i o n ................................1 0 9
6 . 6 R e p o r tG e n e r a t i o n.......................................1 0 9
6.7 Active Collaborative Filtering and User Modeling . . . . . . . . . . . . 110
7 E x a m p l eN o v a m e n t eA IP r o c e s s e s ..............................1 1 0
7.1 Probabilistic Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1127 . 2 N o n l i n e a r - D y n a m i c a lA t t e n t i o nA l l o c a t i o n ..................1 1 5
7 . 3 I m p o r t a n c eU p d a t i n g ....................................1 1 6
7 . 4 S c h e m aa n dP r e d i c a t eL e a r n i n g............................1 1 77 . 5 P a t t e r nM i n i n g..........................................1 2 0
Contents XI
7 . 6 N a t u r a lL a n g u a g eP r o c e s s i n g..............................1 2 2
8 C o n c l u s i o n ..................................................1 2 4Appendix: Novamente Applied to Bioinformatic Pattern Mining . . . . . . . 125
R e f e r e n c e s ......................................................1 2 7
Essentials of General Intelligence:
The Direct Path to Artiﬁcial General Intelligence
Peter Voss
1 I n t r o d u c t i o n.................................................1 3 12 General Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
2.1 Core Requirements for General Intelligence. . . . . . . . . . . . . . . . . . 133
2.2 Advantages of Intelligence Being General . . . . . . . . . . . . . . . . . . . 134
3 S h o r t c u t st oA G I.............................................1 3 5
4 Foundational Cognitive Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
5 A nA G Ii nt h eM a k i n g........................................1 4 4
5 . 1 A G IE n g i n eA r c h i t e c t u r ea n dD e s i g nF e a t u r e s...............1 4 5
6 From Algorithms to General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 147
6.1 Sample Test Domains for Initial Performance Criteria . . . . . . . . 148
6.2 Towards Increased Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
7 O t h e rR e s e a r c h ..............................................1 5 08 F a s t - t r a c kA G I :W h yS oR a r e ?.................................1 5 2
9 C o n c l u s i o n ..................................................1 5 5
R e f e r e n c e s ......................................................1 5 6
Artiﬁcial Brains
Hugo de Garis
1 I n t r o d u c t i o n.................................................1 5 9
2 E v o l v a b l eH a r d w a r e ..........................................1 6 1
2 . 1 N e u r a lN e t w o r kM o d e l s...................................1 6 2
3 T h eC A M - B r a i nM a c h i n e( C B M )...............................1 6 6
3 . 1 E v o l v e dM o d u l e s ........................................1 6 7
3 . 2 T h eK i t t e nR o b o t“ R o b o k i t t y ”............................1 6 8
4 S h o r t -a n dL o n g - T e r mF u t u r e..................................1 7 15 Postscript – July 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
R e f e r e n c e s ......................................................1 7 4
The New AI: General & Sound & Relevant for Physics
J¨urgen Schmidhuber
1 I n t r o d u c t i o n.................................................1 7 5
2 M o r eF o r m a l l y...............................................1 7 63 Prediction Using a Universal Algorithmic Prior Based on the
S h o r t e s tW a yo fD e s c r i b i n gO b j e c t s.............................1 7 7
4 Super Omegas and Generalizations of Kolmogorov Complexity &
Algorithmic Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
5 Computable Predictions Through the Speed Prior Based on the
F a s t e s tW a yo fD e s c r i b i n gO b j e c t s..............................1 8 1
XII Contents
6 S p e e dP r i o r - B a s e dP r e d i c t i o n sf o rO u rU n i v e r s e..................1 8 2
7 O p t i m a lR a t i o n a lD e c i s i o nM a k e r s..............................1 8 48 O p t i m a lU n i v e r s a lS e a r c hA l g o r i t h m s...........................1 8 5
9 O p t i m a lO r d e r e dP r o b l e mS o l v e r( O O P S ).......................1 8 6
1 0 O O P S - B a s e dR e i n f o r c e m e n tL e a r n i n g...........................1 9 0
11 The G¨ o d e lM a c h i n e...........................................1 9 1
1 2 C o n c l u s i o n ..................................................1 9 21 3 A c k n o w l e d g m e n t s ............................................1 9 4
R e f e r e n c e s ......................................................1 9 4
G¨odel Machines: Fully Self-Referential Optimal Universal
Self-improversJ¨urgen Schmidhuber
1 I n t r o d u c t i o na n dO u t l i n e......................................1 9 9
2 Basic Overview, Relation to Previous Work, and Limitations . . . . . . . 200
2 . 1 N o t a t i o na n dS e t - u p .....................................2 0 1
2.2 Basic Idea of G¨ o d e lM a c h i n e ..............................2 0 3
2.3 Proof Techniques and an O()-optimal Initial Proof Searcher. . . 203
2 . 4 R e l a t i o nt oH u t t e r ’ sP r e v i o u sW o r k........................2 0 4
2.5 Limitations of G¨ o d e lM a c h i n e s ............................2 0 5
3 Essential Details of One Representative G¨ o d e lM a c h i n e ...........2 0 6
3 . 1 P r o o fT e c h n i q u e s ........................................2 0 6
4 G l o b a lO p t i m a l i t yT h e o r e m ...................................2 1 2
4 . 1 A l t e r n a t i v eR e l a x e dT a r g e tT h e o r e m.......................2 1 2
5 B i a s - O p t i m a lP r o o fS e a r c h( B I O P S )............................2 1 3
5.1 How a Surviving Proof Searcher May Use Biops to Solve
R e m a i n i n gP r o o fS e a r c hT a s k s ............................2 1 4
6 D i s c u s s i o n&A d d i t i o n a lR e l a t i o n st oP r e v i o u sW o r k..............2 1 5
6.1 Possible Types of G¨ odel Machine Self-improvements . . . . . . . . . . 215
6 . 2 E x a m p l eA p p l i c a t i o n s....................................2 1 7
6.3 Probabilistic G¨ o d e lM a c h i n eH a r d w a r e .....................2 1 7
6.4 More Relations to Previous Work on Less General
S e l f - i m p r o v i n gM a c h i n e s..................................2 1 8
6.5 Are Humans Probabilistic G¨ o d e lM a c h i n e s ?.................2 2 0
6.6 G¨ o d e lM a c h i n e sa n dC o n s c i o u s n e s s.........................2 2 1
6 . 7 F r e q u e n t l yA s k e dQ u e s t i o n s...............................2 2 1
7 C o n c l u s i o n ..................................................2 2 2
8 A c k n o w l e d g m e n t s ............................................2 2 3
R e f e r e n c e s ......................................................2 2 3
Universal Algorithmic Intelligence: A Mathematical
Top→Down Approach
Marcus Hutter
1 I n t r o d u c t i o n.................................................2 2 7
2 Agents in Known Probabilistic Environments . . . . . . . . . . . . . . . . . . . . 230
Contents XIII
2 . 1 T h eC y b e r n e t i cA g e n tM o d e l..............................2 3 0
2 . 2 S t r i n g s .................................................2 3 22 . 3 A IM o d e lf o rK n o w nD e t e r m i n i s t i cE n v i r o n m e n t.............2 3 2
2.4 AI Model for Known Prior Probability . . . . . . . . . . . . . . . . . . . . . 233
2.5 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
2.6 Explicit Form of the AI µM o d e l ...........................2 3 6
2 . 7 F a c t o r i z a b l eE n v i r o n m e n t s................................2 3 82 . 8 C o n s t a n t sa n dL i m i t s ....................................2 3 9
2 . 9 S e q u e n t i a lD e c i s i o nT h e o r y ...............................2 4 0
3 U n i v e r s a lS e q u e n c eP r e d i c t i o n .................................2 4 1
3 . 1 I n t r o d u c t i o n ............................................2 4 1
3 . 2 A l g o r i t h m i cI n f o r m a t i o nT h e o r y...........................2 4 2
3.3 Uncertainty & Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
3.4 Algorithmic Probability & Universal Induction . . . . . . . . . . . . . . 244
3 . 5 L o s sB o u n d s&P a r e t oO p t i m a l i t y .........................2 4 5
4 T h eU n i v e r s a lA l g o r i t h m i cA g e n tA I X I..........................2 4 6
4.1 The Universal AI ξM o d e l.................................2 4 6
4 . 2 O nt h eO p t i m a l i t yo fA I X I................................2 4 94.3 Value Bounds and Separability Concepts. . . . . . . . . . . . . . . . . . . . 251
4.4 Pareto Optimality of AI ξ.................................2 5 4
4 . 5 T h eC h o i c eo ft h eH o r i z o n................................2 5 5
4 . 6 O u t l o o k ................................................2 5 7
4 . 7 C o n c l u s i o n s.............................................2 5 8
5 I m p o r t a n tP r o b l e mC l a s s e s....................................2 5 9
5 . 1 S e q u e n c eP r e d i c t i o n( S P ).................................2 5 9
5 . 2 S t r a t e g i cG a m e s( S G )....................................2 6 15 . 3 F u n c t i o nM i n i m i z a t i o n( F M )..............................2 6 5
5 . 4 S u p e r v i s e dL e a r n i n gf r o mE x a m p l e s( E X )...................2 6 9
5.5 Other Aspects of Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
6 T i m e - B o u n d e dA I X IM o d e l....................................2 7 2
6.1 Time-Limited Probability Distributions . . . . . . . . . . . . . . . . . . . . . 2736 . 2 T h eI d e ao ft h eB e s tV o t eA l g o r i t h m.......................2 7 5
6 . 3 E x t e n d e dC h r o n o l o g i c a lP r o g r a m s .........................2 7 6
6 . 4 V a l i dA p p r o x i m a t i o n s ....................................2 7 66.5 Eﬀective Intelligence Order Relation . . . . . . . . . . . . . . . . . . . . . . . 277
6.6 The Universal Time-Bounded AIXI tlA g e n t.................2 7 7
6 . 7 L i m i t a t i o n sa n dO p e nQ u e s t i o n s...........................2 7 8
6 . 8 R e m a r k s................................................2 7 9
7 D i s c u s s i o n...................................................2 8 0
7 . 1 G e n e r a lR e m a r k s ........................................2 8 0
7 . 2 O u t l o o k&O p e nQ u e s t i o n s ...............................2 8 2
7 . 3 T h eB i gQ u e s t i o n s.......................................2 8 37 . 4 C o n c l u s i o n s.............................................2 8 4
A n n o t a t e dB i b l i o g r a p h y ..........................................2 8 5
R e f e r e n c e s ......................................................2 8 7
XIV Contents
Program Search as a Path to Artiﬁcial General Intelligence
/suppressLukasz Kaiser
1 Intelligence and the Search for Programs . . . . . . . . . . . . . . . . . . . . . . . . 291
2 T h e o r e t i c a lR e s u l t s...........................................2 9 4
2 . 1 P r o g r a mS e a r c hi nt h eS t a n d a r dA IM o d e l..................2 9 5
2 . 2 S e l f - i m p r o v i n gP r o g r a mS e a r c h............................2 9 6
2 . 3 D i s c u s s i o no fE ﬃ c i e n c yD e ﬁ n i t i o n s.........................2 9 8
3 C o n v e n i e n tM o d e lo fC o m p u t a t i o n..............................2 9 9
3 . 1 E x t e n d e dP r o g r a mN o t a t i o n ..............................3 0 6
3.2 Compiling Typed Rewriting Systems . . . . . . . . . . . . . . . . . . . . . . . 311
4 R e a s o n i n gU s i n gG a m e s.......................................3 1 4
4 . 1 R e a s o na n dS e a r c hG a m ef o rT e r m s........................3 1 8
5 C o n c l u s i o n s..................................................3 2 4
R e f e r e n c e s ......................................................3 2 5
The Natural Way to Artiﬁcial Intelligence
Vladimir G. Red’ko1 I n t r o d u c t i o n.................................................3 2 7
2 T h eE p i s t e m o l o g i c a lP r o b l e m..................................3 2 8
3 Approaches to the Theory of Evolutionary Origin of Human
Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
3.1 “Intelligent Inventions” of Biological Evolution . . . . . . . . . . . . . . 331
3 . 2 M e t h o d o l o g i c a lA p p r o a c h e s ...............................3 3 4
3.3 Role of Investigations of “Artiﬁcial Life” and “Simulation of
A d a p t i v eB e h a v i o r ”......................................3 3 7
4 T w oM o d e l s .................................................3 3 8
4.1 Alife Model of Evolutionary Emergence of Purposeful
A d a p t i v eB e h a v i o r.......................................3 3 8
4 . 2 M o d e lo fE v o l u t i o no fW e bA g e n t s.........................3 4 3
5 Towards the Implementation of Higher Cognitive Abilities . . . . . . . . . 347
6 C o n c l u s i o n ..................................................3 4 9
7 A c k n o w l e d g e m e n t s ...........................................3 4 9
R e f e r e n c e s ......................................................3 4 9
3D Simulation: the Key to A.I.
Keith A. Hoyes
1 I n t r o d u c t i o n.................................................3 5 3
2 Pillars of Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
2 . 1 D e e pB l u e ..............................................3 5 4
2 . 2 V i r t u a lR e a l i t y ..........................................3 5 4
2 . 3 T h eH u m b l eE a r t h w o r m..................................3 5 4
3 C o n s c i o u s n e s s................................................3 5 5
3 . 1 F e e l i n ga n dQ u a l i a.......................................3 5 6
4 General Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358
4.1 Human Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
Contents XV
5 3 DS i m u l a t i o na n dL a n g u a g e ..................................3 6 3
6 E p i s t e m o l o g y ................................................3 6 67 I n s t a n t i a t i o n :t h eH e a r to fC o n s c i o u s n e s s........................3 6 7
8 I naN u t s h e l l ................................................3 7 0
9 R e a l - W o r l dA I ...............................................3 7 4
9 . 1 E x a m p l e sa n dM e t a p h o r s.................................3 7 8
9 . 2 M a t ha n dS o f t w a r e.......................................3 8 09 . 3 B a r c o d eE x a m p l e........................................3 8 0
9 . 4 S o f t w a r eD e s i g n .........................................3 8 3
1 0 C o n c l u s i o n ..................................................3 8 5R e f e r e n c e s ......................................................3 8 6
Levels of Organization in General Intelligence
Eliezer Yudkowsky
1 Foundations of General Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
2 Levels of Organization in Deliberative General Intelligence . . . . . . . . . 397
2 . 1 C o n c e p t s :A nI l l u s t r a t i o no fP r i n c i p l e s......................3 9 72 . 2 L e v e l so fO r g a n i z a t i o ni nD e l i b e r a t i o n......................4 0 7
2 . 3 T h eC o d eL e v e l .........................................4 0 9
2 . 4 T h eM o d a l i t yL e v e l......................................4 1 62 . 5 T h eC o n c e p tL e v e l.......................................4 2 6
2 . 6 T h eT h o u g h tL e v e l ......................................4 4 4
2 . 7 T h eD e l i b e r a t i o nL e v e l ...................................4 6 1
3 S e e dA I.....................................................4 7 6
3 . 1 A d v a n t a g e so fM i n d s - i n - G e n e r a l...........................4 8 03 . 2 R e c u r s i v eS e l f - e n h a n c e m e n t...............................4 8 4
3.3 Infrahumanity and Transhumanity: “Human-Equivalence” as
A n t h r o p o c e n t r i s m .......................................4 8 9
4 C o n c l u s i o n s..................................................4 9 3
R e f e r e n c e s ......................................................4 9 6
Index .......................................................... 503