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Introduction
Cite this article: Bundy A, Chater N,
Muggleton S. 2023 Introduction to ‘Cognitive
articial intelligence’. Phil.Trans.R.Soc.A381:
20220051.
https://doi.org/10.1098/rsta.2022.0051
Received: 5 April 2023
Accepted: 5 April 2023
One contribution of 11 to a discussion meeting
issue ‘Cognitive articial intelligence’.
Subject Areas:
articial intelligence
Author for correspondence:
Alan Bundy
e-mail: bundy@ed.ac.uk
Introduction to ‘Cognitive
articial intelligence’
Alan Bundy1,NickChater
2and Stephen Muggleton3
1The University of Edinburgh, Edinburgh, Edinburgh, UK
2Warwick Business School, University of Warwick, Coventry, West
Midlands, UK
3Computational Bioinformatics Laboratory, Imperial College
London, UK
AB, 0000-0002-0578-6474;NC,0000-0002-9745-0686;
SM, 0000-0001-6061-6104
1. Introduction
There is an increasing excitement concerning the
potential of artificial intelligence to both transform
human society and to understand cognition in humans
and other animals. This meeting addressed the leading
edge of research intersection of artificial intelligence and
cognitive science, an area we are calling cognitive artificial
intelligence. Topics covered include:
— Improving the interaction between humans and
machines, including how machines can explain
their reasoning to humans, might be more
socially aware and understand a human’s beliefs
and intentions.
— Contrasting how machines and humans learn,
and showing how machines might emulate
humans in learning from only a few examples
and how machines can aid the teaching of
humans.
— How reasoning and learning interact, including
how failures of reasoning trigger the evolution
of models of the environment.
— The contrast between symbolic and subsymbolic
reasoning, especially the role of large language
models, such as ChatGPT, in generating natural
language and serving as a model of human
cognition.
This special issue is the proceedings of a Royal Society
Hooke Meeting on Cognitive artificial intelligence.
2023 The Authors. Published by the Royal Society under the terms of the
Creative Commons Attribution License http://creativecommons.org/licenses/
by/4.0/, which permits unrestricted use, provided the original author and
source are credited.
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The meeting was proposed by the EPSRC Network+on Human-Like Computing (HLC). According
to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars),
this new area represents the third wave of artificial intelligence (3AI, 2020s–2030s), and is being
actively investigated in the USA, Europe and China. The HLC Network was one of the first
internationally to initiate and support research specifically in this area. Starting activities in 2018,
the Network represents around 60 leading UK groups of artificial intelligence and cognitive
scientists involved in the development of the inter-disciplinary area of HLC. The research of
network groups aims to address key unsolved problems at the interface between Psychology
and Computer Science.
2. Objectives
The key fields brought together at this meeting are artificial intelligence and cognitive science.
This meeting helped forge better understanding and interactions in a joint area which we refer
to as cognitive artificial intelligence. Artificial intelligence and machine learning are becoming
centrally relevant to a variety of sciences in supporting the construction of complex models from
data. Furthermore, within society at large artificial intelligence is viewed as having both immense
potential for enabling human societies, while simultaneously presenting dangers for weakening
the social fabric. It is clear that advances in understanding of how to build automated learning
systems which are compatible with human understanding, planning and reasoning has immense
potential for beneficial effects in many areas. However, interactions of cognitive scientists with
leading edge artificial intelligence research requires many advances and new experimental work
in psychology to further understand the cognitive and social constraints of human beings when
interacting with machines. The meeting allowed presentations on the latest results from leading
laboratories in this area, as well as encouraging discussion on key topics for joint research between
artificial intelligence and cognitive science groups.
3. Signicance
Both artificial intelligence and cognitive science have a variety of large-scale annual conferences.
However, researchers within each of these areas typically have limited understanding of advances
in each other’s fields. The meeting helped bring together leading scientists from artificial
intelligence and cognitive science to inform each other of key open questions that joint work
could help address.
4. Social implications
In recent years, there has been increasing public concern about the application of artificial
intelligence. Such concerns were documented within the House of Commons and Lords Select
Committee Reports and the Royal Society Report on Machine Learning. Key issues raised
included those of the need for (a) transparent decision making, (b) accountability and its related
legal implications, (c) safety of automated systems in control tasks and (d) the threat to jobs.
Research in the new area of cognitive artificial intelligence will aim to advance fundamental
understanding for the key artificial intelligence technologies being developed. This meeting has
the potential to promote understanding of the advances required to go beyond the development
of simple black-box decision makers to allow the development of systems which take account
of our understanding human modes of perception and social interaction. Such advances have
potential for wide-ranging social benefit.
5. Points of view
Since both artificial intelligence and cognitive science are both well-established fields, there is
considerable diversity of viewpoints within each field. Within artificial intelligence, this tends to
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be related to the choice of representation used for representing knowledge, while in Cognitive
Science, there are a wide variety of differences in methodology and experimental techniques. The
meeting was devised to include representative from these diverse communities and viewpoints,
with the aim of encouraging wide-ranging discussion.
6. Overview of the contributions
This issue of Cognitive artificial intelligence consists of 11 substantive papers, drawn roughly
equally from the artificial intelligence and cognitive science communities, but each drawing on
and having relevance to both.
We begin with Muggleton’s [1] paper ‘Hypothesizing an algorithm from one example: the role
of specificity’ which argues that while the dominant methods of Statistical Machine Learning
achieve high accuracy, they require large numbers of examples to do so. By contrast, humans
typically learn new concepts from as few as one example. However, the high data efficiency
of human learning cannot be explained by existing standard formal frameworks for machine
learning. Muggleton shows that this disparity can be resolved by introducing a revised Bayesian
framework for expected error, and shows that highly specific concepts, as typified by computer
algorithms, can be learned within this theoretical framework with high accuracy from a single
example, by using a preference for specificity combined with minimality. Experiments with
Muggleton’s implementation of this approach, called DeepLog, indicate that such an approach
can be used in practice to efficiently construct relatively complex logic programs from a single
randomly selected example.
Wahlster’s [2] paper ‘Understanding computational dialogue understanding’ first explains
why human-like dialogue understanding is so difficult for AI. It discusses various methods
for testing the understanding capabilities of dialogue systems. It reviews the development of
dialogue systems over five decades, focusing on the transition from closed-domain to open-
domain systems and their extension to multimodal, multiparty and multilingual dialogues. From
being somewhat of a niche topic in artificial intelligence research for the first 40 years, it has made
newspaper headlines in recent years and is now being discussed by political leaders at events such
as the World Economic Forum in Davos. It asks whether large language models are super-parrots
or a milestone towards human-like dialogue understanding and how they relate to what we know
about language processing in the human brain. Using ChatGPT as an example, it presents some
limitations of this approach to dialogue systems. Finally, it presents some lessons learnt from
40 years of research in this field about system architecture principles: symmetric multimodality,
no presentation without representation and anticipation feedback loops. It concludes with a
discussion of grand challenges such as satisfying conversational maxims and the European
Language Equality Act through massive digital multilinguality—perhaps enabled by interactive
machine learning with human trainers.
The next article, ‘Symbols and grounding in large language models’ by Pavlick [3], considers
the practical and theoretical significance of large language models, which have in the last few
years been shown to carry out a large number of open-ended natural language tasks with often
close to human levels of performance on some measures. These models consist of very large
deep neural networks trained on a substantial fraction of the entire contents of the World Wide
Web. Within the cognitive science community, many have argued that models trained purely
on a large amount of language data, however impressive their performance, are inevitably
restricted in their relevance to human cognition. Pavlick takes up two specific charges against
the cognitive relevance of such models and argues for a verdict of ‘not proven’ in both cases. The
first issue she addresses is that large language models are not endowed with structured symbolic
representations, which are widely presumed to underpin perception, thought and language in
humans. But she notes, drawing on her research, that the internal representations learned by
large language models may actually have a distinctly symbolic character, and that the nature of
such representations can only be determined by sophisticated analysis of how large language
models work. The second issue is that large language models are sometimes presumed not to
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be ‘grounded’ through the perceptuo-motor interaction with the world (although links between
large language models and models of visual processing are relatively advanced). But in any case,
Pavlick argues that modern philosophy of language assumes that the grounding of linguistic
symbols is a collective achievement, at the level of the entire language community, rather than
operating individual by individual. Thus, the lack of direct grounding may raise no special
difficulties for large language models, even if these are not integrated with modules for perception
and action. They may thus inherit grounded symbols from the human language on which they
are trained.
We next move from language to mathematical and scientific cognition. In ‘DreamCoder:
growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning’,
Ellis et al.[4] introduce a system that learns to solve a wide range of representationally
challenging problems by learning to write programs. Combining symbolic and neural network
methods, it learns programming languages for capturing concepts relevant to the target domain,
and uses neural networks to guide the search process to create appropriate programs using
these languages. They apply a ‘wake-sleep’ algorithm, which interleaves the extension of
the programming language with new symbolic abstractions and training the neural network
on imagined and past problems. Dreamcoder can be applied successfully to a wide range
of problems, from drawing pictures and building scenes, to rediscovering the fundamentals
of functional programming, vector algebra and classical physics, including Newton’s and
Coulomb’s Laws. Learning operates by successively creating new abstractions from previous
abstractions, creating rich systems of representation which transfer effectively across task
domains.
Goodman & Poesia [5] continue the theme of how machines can learn to engage in rich,
structured representation and reasoning, now focusing on mathematics, in their paper ‘Peano:
learning formal mathematical reasoning’. They note that while mathematics is created slowly,
involving a huge collective intellectual effort over many centuries, it can relatively rapidly
be taught afresh to each generation of students, who can learn to apply it successfully also
from a very limited set of training examples. Goodman & Poesia argue that fundamental to
mathematical discovery and learning is the ability to create and reason over representations
at ever-increasing levels of abstraction. The computational model, Peano, is a theorem proving
environment which has the power to represent a wide range of aspects of mathematics. They
show that attempting to learn mathematical regularities using traditional reinforcement learning
methods is unsuccessful; but adding the ability to learn reusable abstractions (which they call
‘tactics’) from past problem-solving attempts, allows the agent to make cumulative progress.
The way in which these abstractions are generated sheds light on the ‘natural’ order in which
such abstractions should most helpfully be presented human learners—and this order agrees to a
substantial degree with the order in which ideas are introduced in learning curricula for human
learners, such as that used in the Kahn Academy. Their work raises the possibility that deeper
understanding of learning mathematics using automated methods may shed substantial might
both on the process by which humans learn mathematical concepts, and the optimal design of
mathematical curricula.
Bundy & Li’s [6] paper ‘Representational change is integral to reasoning’ proposes a
mechanism by which language evolves in response to reasoning failures. For instance, concepts
may be split (mother into birth mother and step mother) or merged (Morning Star and Evening
Star into Venus) when current theories either predict things observed to be false or fail to predict
things observed to be true. They start by illustrating that concept evolution occurs even in
mathematics. An examination of Imre Lakatos’s classic rational reconstruction of the history of
Euler’s Theorem (V+F−E=2) about polyhedra shows that the initial concept of polyhedron was
not fully defined and that potential counter-examples can be included or excluded depending
on how this initial definition is refined. They then discuss their ABC system that evolves logical
theories by a combination of abduction, belief revision and conceptual change which reconcile an
initial theory’s predictions with conflicting observations of the environment, leading to a revised
theory.
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The next article, ‘Argument and explanation’ by Hahn & Tesic [7] considers the relationship
between the concepts of argumentation and explanation in the context of philosophy of science
and common-sense reasoning. While the philosopher Carl Hempel saw scientific explanation as
a type of argument, Hahn and Tesic stress that arguments typically play a role in dialogue, in
trying to convince others (and perhaps also oneself) of the truth or rightness of some contested
matter. Here, arguments are in the service of the broader goal of persuasion, and factors beyond
the argument itself (such as who was its source) are crucial. But explanations can often apply
when there is no issue of doubt about the point to be explained: thus, facts ranging from, say, the
blueness of the sky or that a piece of kitchen cheese has been nibbled, may not be in doubt, but still
may stand in need of explanation. A crucial issue here is what makes an explanation satisfying—
what distinguishes chains of reasoning to a particular conclusion that provides a sense of insight
and understanding. Hahn & Tesic provide a review of the state-of-the-art in psychological and
atificial intelligence approaches to both argument and explanation, and point the way for future
research.
Gweon et al.[8] in their paper ‘Beyond imitation: machines that understand and are
understood by humans’ focus on a particular, and especially fundamental, aspect of explanation:
the human ability to infer and reason about the mental states of others from observing their
behaviour. Such inferences may be crucial when attempting to learn from another person; and
equally is crucial from the point of view of the teacher, attempting to infer what the learner
already knows and which actions or words will best help them learn successfully. This type
of social intelligence develops early in humans, but seems difficult to replicate in machines:
artificial intelligence systems currently have a limited ability to understand, or be understood
by, humans with which they interact. Gweon et al. argue that a central goal of AI should be the
creation of genuinely socially intelligent machines, that model and consider the minds of people
they interact with, rather than more superficial social niceties, such as mimicking human facial
expressions, gestures, or patterns of speech. They survey work on the development of human
social intelligence, and human–machine interaction, and argue that this could provide crucial
clues for how to create a new generation of machines that can engage in rich social interactions
with people. Indeed, they argue that integrating cognitive science and artificial intelligence
approaches to understanding social intelligence is likely to advance both our understanding of
ourselves, and the creation of socially intelligent machines which can interact naturally with
people.
A particularly critical aspect of the challenge of building computational models of other
minds—inferring emotional states—is taken up by Houlihan et al.[9] in their paper ‘Emotion
prediction as inference over a generative theory of mind’. They describe a computational model
of emotion prediction, the Inferred Appraisals model, that uses inverse planning to infer mental
states, which can include individual objectives but also ‘social preferences’ such as preference for
equity or the desire to maintain a good reputation in the eyes of others. They show how it is
possible to learn a mapping between these appraisals and 20 labels for emotions (including joy,
relief, guilt and envy), so that the model can quantitatively match human predictions concerning
these predictions in high-stakes game-like interactions. The model shows how social preferences
turn out to be important in predicting almost every emotion; and also captures the flexibility
of human emotion attribution. This work provides a starting point for computational models of
social interaction, crucial both for understanding the nature of human social behaviour and for
building socially sensitive artificial systems.
Continuing the theme of social interaction, in the final contribution to this issue, Chater [10]
asks ‘How could we make a social robot?’ He argues that human intelligence is inherently social
and that the spectacular achievements of our species arise through our ability to cooperate,
collaborate and cumulatively creating languages, social norms, organizational and political
structure, legal and financial systems, and the mathematics science and technology. A genuinely
social robot therefore would be an artificial agent able to ‘join in’ fluently with human projects
and activities, learning, collaboration and contributing alongside us. Chater argues that the
cognitive foundation of this process is a style of reasoning known as ‘virtual bargaining’ according
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to which a pair of intelligent agents is able to coordinate their thoughts and actions by each
asking not merely ‘what should Ithink or do?’ but ‘what should we think or do?’ Chater argues
that answering this question successfully involves simulating what the agent will agree were
they able to engage in prior communication—each party needs to successfully simulate the
outcome of hypothetical bargaining process. Chater illustrates the approach by drawing on prior
experimental work in which people are shown to be astonishingly successful, and highly flexible,
in the use of novel and very restricted communicative signals. Here, the challenge of virtual
bargaining is to agree what a novel signal would most naturally be interpreted to mean. He
argues that this process of virtual bargaining underpins communication ‘in the moment’, and
that distinctive human ability to engage in virtual bargaining underpins the gradual creation of
natural language and complex systems of societal conventions. Successive communicative and
collaborative improvizations, each of which provides useful precedents for the next, explains
the gradual emergence of increasingly systematic patterns in language and behaviour, through
processes of spontaneous order. Thus, the complex machinery underpinning human society
arises, to paraphrase the words of Scottish Enlightenment philosopher Adam Ferguson, through
human action but not by human design. Chater suggests that by apparently skipping over
the subtle process of virtual bargaining that underpins human communication, large language
models (as discussed by Pavlick) may currently be missing out what may be a crucial step in
understanding human social behaviour, and how it may be replicated in machines.
Overall, the contribution to this special issue on Cognitive artificial intelligence highlights
convergent and overlapping research in both cognitive science and in artificial intelligence which
is likely to be crucial to building both the next generation of increasingly human-like artificial
systems and also providing a deeper understanding of the human mind.
Data accessibility. This article has no additional data.
Authors’ contributions. A.B.: writing—original draft, writing—review and editing; N.C.: writing—original draft,
writing—review and editing; S.M.: writing—original draft, writing—review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed
therein.
Conict of interest declaration. This theme issue was put together by the Guest Editor team under supervision
from the journal’s Editorial staff, following the Royal Society’s ethical codes and best-practice guidelines.
The Guest Editor team invited contributions and handled the review process. Individual Guest Editors were
not involved in assessing papers where they had a personal, professional or financial conflict of interest with
the authors or the research described. Independent reviewers assessed all papers. Invitation to contribute did
not guarantee inclusion.
Funding. We received no funding for this study.
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