Cognitive Design for Artificial Minds

137 Pages·2021·3.74 MB·English

by  Antonio Lieto

COGNITIVE DESIGN FOR
ARTIFICIAL MINDS
Cognitive Design for Artificial Minds explains the crucial role that human cogni-
tion research plays in the design and realization of artificial intelligence systems,
illustrating the steps necessary for the design of artificial models of cognition.
It bridges the gap between the theoretical, experimental, and technological
issues addressed in the context of AI of cognitive inspiration and computational
cognitive science.
Beginning with an overview of the historical, methodological, and t echnical
issues in the field of cognitively inspired artificial intelligence, Lieto illustrates how
the cognitive design approach has an important role to play in the development
of intelligent AI technologies and plausible computational models of cognition.
Introducing a unique perspective that draws upon Cybernetics and early AI prin-
ciples, Lieto emphasizes the need for an equivalence between cognitive processes
and implemented AI procedures, in order to realize biologically and cognitively
inspired artificial minds. He also introduces the Minimal Cognitive Grid, a
pragmatic method to rank the different degrees of biological and cognitive accu-
racy of artificial systems in order to project and predict their explanatory power
with respect to the natural systems taken as a source of inspiration.
Providing a comprehensive overview of cognitive design principles in
constructing artificial minds, this text will be essential reading for students and
researchers of artificial intelligence and cognitive science.
Antonio Lieto is a researcher in Artificial Intelligence at the Department of
Computer Science of the University of Turin, Italy, and a research associate at
the ICAR-CNR in Palermo, Italy. He is the current Vice-President of the Italian
Association of Cognitive Science (2017–2022) and an ACM Distinguished
Speaker on the topics of cognitively inspired AI and artificial models of cognition.

COGNITIVE DESIGN FOR
ARTIFICIAL MINDS
Antonio Lieto
First published 2021
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
52 Vanderbilt Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2021 Antonio Lieto
The right of Antonio Lieto to be identified as author of this work has
been asserted by him in accordance with sections 77 and 78 of the
Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced
or utilised in any form or by any electronic, mechanical, or other
means, now known or hereafter invented, including photocopying and
recording, or in any information storage or retrieval system, without
permission in writing from the publishers.
Trademark notice: Product or corporate names may be trademarks
or registered trademarks, and are used only for identification and
explanation without intent to infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
Names: Lieto, Antonio, 1983– author.
Title: Cognitive design for artificial minds / Antonio Lieto.
Description: Abingdon, Oxon ; New York, NY : Routledge, 2021. |
Includes bibliographical references and index.
Identifiers: LCCN 2020048017 (print) | LCCN 2020048018 (ebook)
Subjects: LCSH: Cognition. | Artificial intelligence.
Classification: LCC BF311 .L536 2021 (print) | LCC BF311 (ebook) |
DDC 153.3—dc23
LC record available at https://lccn.loc.gov/2020048017
LC ebook record available at https://lccn.loc.gov/2020048018
ISBN: 978-1-138-20792-9 (hbk)
ISBN: 978-1-138-20795-0 (pbk)
ISBN: 978-1-315-46053-6 (ebk)
Typeset in Bembo
by codeMantra
CONTENTS
List of illustrations vii
Introduction ix
Acknowledgements xiii
1 Cognitive science and artificial intelligence: death and
rebirth of a collaboration 1
When Cognitive Science was AI 1
From the general problem-solver to the society of mind: cognitivist
insights from the early AI era 2
Heuristics and AI eras 9
Modelling paradigms and AI eras: cognitivist and emergentist perspectives 10
Death and rebirth of a collaboration 18
2 Cognitive and machine-oriented approaches to
intelligence in artificial systems 20
Nature- vs. machine-inspired approaches to artificial systems 20
Functionalist vs. structuralist design approaches 21
Levels of analysis of computational systems 24
The space of cognitive systems 30
Functional and structural neural systems 32
Functional and structural symbolic systems 34
3 Principles of the cognitive design approach 37
Classical, bounded, and bounded-rational models of cognition 37
Resource-rationality models 40
Kinds of explanations 43
Levels of plausibility and the minimal cognitive grid (MCG) 48
vi Contents
4 Examples of cognitively inspired systems and
application of the Minimal Cognitive Grid 52
Modern AI systems: cognitive computing? 52
Cognitive architectures 57
SOAR 61
ACT-R 63
Two problems for the knowledge level in cognitive architectures 64
Knowledge size and knowledge heterogeneity in SOAR and ACT-R 69
DUAL PECCS 72
5 Evaluating the performances of artificial systems 77
“Thinking” machines and Turing Test(s) 77
The Chinese Room 83
The Newell test for a theory of cognition 85
The Winograd Schema Challenge 87
DARPA challenges, RoboCup, and RoboCup@Home 89
Comparison 90
6 The next steps 93
The road travelled 93
The way forward 95
Towards a standard model of mind/common model of cognition 103
Community 104
References 107
Index 117
ILLUSTRATIONS
Figures
1.1 Overview of the internal dynamics of physical symbol systems
(adapted from Vernon, 2014) 12
1.2 Brooks’ Subsumption Architecture (adapted from Brooks, 1999) 17
2.1 Enriched 2D space of cognitive systems 30
4.1 The SOAR cognitive architecture, from Laird (2012), with
permission from MIT Press 62
4.2 The ACT-R cognitive architecture (permission from Elsevier) 64
4.3 An example of the hybrid conceptual architecture in
DUAL PECCS 73
5.1 A pictorial representation of the “Imitation game” 79
Tables
4.1 Newell’s timescale of human actions 63
4.2 Prototype models vs exemplar models 68
5.1 Comparative table of the different evaluation approaches 91

INTRODUCTION
This book is about (re)building a bridge between two different “sciences of the
artificial” – Artificial Intelligence and Cognitive Science – that, nowadays, apart
from some notable exceptions, do not talk as much with each other as they
should. Here, I review some of the main themes that have characterized the
historical paths of these two disciplines and argue that the technological matu-
rity reached in several domains now calls for a renewed joint enterprise aimed
at addressing more substantial challenges that these two disciplines have to face
from a scientific viewpoint.
The book explicitly targets a multidisciplinary audience. As such, it is mainly
an act of courage (or probably of irresponsibility), since experts in the specific
subfields of AI and Cognitive Science will have for sure much more to say and
would surely be able to communicate their own ideas in a better way than I can.
However, as mentioned, this book privileges the breath of the connections be-
tween the disciplines rather than the depths of the exploration within each single
discipline. As such, it is not a manual or a handbook since it presupposes the
knowledge of same basic elements of each discipline that will be touched upon by
our arguments. Of course, scholars and students of the diverse fields have knowl-
edge of different pieces of the entire puzzle and need to be briefly introduced to
the aspects they do not know. This service is provided in the book, though we
direct the reader towards specialized literature for details.
One of the main goals of this manuscript is to show the reader that the so-
called “cognitive design approach” has still an important role to play in the de-
velopment of intelligent AI technologies as well as in the context of development
of plausible computational models of cognition. In other words, the study of the
“Cognitive Design” principles for building “Artificial Minds” will be hopefully
a useful instrument for current and future generations of AI and cognitive science
scholars and students. In this respect, a caveat is necessary: in the philosophical