Invited talk for Workshop on MetaReasoning: Thinking about Thinking at AAAI'08, Washington, July 2008.

Revised (shorter) version published (with style conventions I dislike -- e.g. no section numbers) in
Metareasoning: Thinking about thinking,
Eds. Michael T. Cox and Anita Raja, MIT Press, Cambridge, MA, 2011, pp 307-323.
Table of contents and sample chapters.

See also COSY-TR-0802

Some AI researchers aim to make useful machines, including robots. Others aim to understand general principles of information-processing machines whether natural or artificial, often with special emphasis on humans and human-like systems: They primarily address scientific and philosophical questions rather than practical goals. However, the tasks required to pursue scientific and engineering goals overlap considerably, since both involve building working systems to test ideas and demonstrate results, and the conceptual frameworks and development tools needed for both overlap. This paper, partly based on requirements analysis in the CoSy robotics project, surveys varieties of meta-cognition and draws attention to some types that appear to play a role in intelligent biological individuals (e.g. humans) and which could also help with practical engineering goals, but seem not to have been noticed by most researchers in the field. There are important implications for architectures and representations.

The presentation is available online at http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#aaai08

Filename: sloman-on-boden-1983.pdf
Title: Commentary on Boden on "Artificial Intelligence and Animal Psychology"
Authors: Aaron Sloman
Date Published: 1983
Date Installed: 16 Dec 2008

Where published:

New Ideas in Psychology
vol. 1, no = 1 pp. 41--50. Online here

Abstract: (Introduction to article)

Having discussed these issues with the author over many years, I was not surprised to find myself agreeing with nearly everything in the paper, and admiring the clarity and elegance of its presentation. All I can offer by way of commentary, therefore, is a collection of minor quibbles, some reformulations to help readers for whom the computational approach is very new, and a few extensions of the discussion.

---

WHAT IS ARTIFICIAL INTELLIGENCE?

I'll start with a few explanatory comments on the nature of A.I., to supplement the section of the paper "A.I. as the Study of Representation". Cognitive Science has three main classes of goals (a) theoretical (the study of possible minds, possible forms of representation and computation), (b) empirical (the study of actual minds and mental abilities of humans and other animals), (c) practical (the attempt to help individuals and society by alleviating problems (i.e. learning problems, mental disorders) and designing new useful intelligent machines).

Activities pursuing these three goals are most fruitful when the goals are interlinked, providing opportunities for feedback between theoretical, empirical and applied work. Artificial Intelligence is a subdiscipline of Cognitive Science which straddles the theoretical approach (studying general properties of possible computational systems) and applications (designing new systems to help in education, industry, commerce, medicine, entertainment). Its empirical content is mostly based not on specialised research, but on common knowledge of many of the things people can do - such as using and understanding language, seeing things, making plans, solving problems, playing games. This knowledge of what people can do sets design goals for both the theoretical and the applied work. In particular, an important aspect of A.I. research is task analysis: given that people can perform a certain task, what are the computational resources required, and what are the trade-offs between different representations and processing strategies? This sort of analysis is relevant to the study of other animals insofar as many human abilities are shared with other animals.

Filename:wpe-abstract-sloman.pdf
Filename:wpe-abstract-sloman.html
Title: Virtual Machines in Philosophy, Engineering & Biology

Author: Aaron Sloman
Date Installed: 3 Nov 2008 Extended Abstract for Workshop On Philosophy and Engineering, 10-12 November, 2008, London, UK

See also:

http://www.cs.bham.ac.uk/research/projects/cogaff/09.html#901
What Cognitive Scientists Need to Know about Virtual Machines}
Proceedings Cognitive Science Conference, Amsterdam 2009.

http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk86
Supervenience and Causation in Virtual Machinery (and related presentations).

Abstract:

Keywords
Architecture, causation, implementation, informationprocessing, biology,
philosophy, psychology, robots, selfawareness, self-control,
supervenience, vertical modularity, virtual machine, virtual machine
functionalism .

1.INTRODUCTION

A machine is a complex enduring entity with parts that
interact causally with one another as they change their properties and
relationships. Most machines are also embedded in a complex environment
with which they interact. A virtual machine (VM) has non-physical parts,
relationships, events and processes, such as parse trees, pattern
matching, moves in a game, goals, plans, decisions, predictions,
explanations and proofs.

The concept of a virtual machine, invented in the 20th Century, (not to
be confused with virtual reality) is important (a) for many engineering
applications, (b) for theoretical computer science, (c) for
understanding some of the major products of biological evolution (e.g.
animal minds), and (d) for gaining new insights into several old
philosophical problems, e.g. about the mind-body relationship, about
qualia, and how to analyse concepts of mind by adopting the design
stance in combination with the notion of an information processing
architecture [1,2]. Analysing relations between different sets of
requirements (niches) and designs for meeting the requirements exposes a
space of possible minds (for animals and artifacts), raising new
questions about evolution, about future intelligent machines, and about
how concepts of mind should be understood.

Most philosophers, biologists, psychologists and neuroscientists
completely ignore VMs, despite frequently (unwittingly) using them: e.g.
for email, spreadsheets, text processing, or web-browsing. Academic
philosophers generally ignore or misunderstand the philosophical
significance of VMs (in part because many assume VMs are finite state
machines). Pollock [3] is a rare exception. Dennett often mentions
virtual machines, but claims they are merely a useful fiction [e.g. 4,
note 10]. Events in useful fictions cannot cause email to be sent or
airliners to crash. The idea of a VM can significantly extend our
thinking about problems in several disciplines and pose new problems for
future empirical and philosophical research.

in Simulating the Mind: A Technical Neuropsychoanalytical Approach
Eds. Dietrich, D.; Fodor, G.; Zucker, G.; Bruckner, D. 2009, (Actually available 2008)
Springer,
ISBN: 978-3-211-09450-1
http://www.springer.com/springerwiennewyork/computer+science/book/978-3-211-09450-1

Also published in 5th IEEE International Conference on Industrial Informatics, 23-27 June 2007
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4384956
DOI:10.1109/INDIN.2007.4384956

This was an invited paper for ENF07' 2007 Emulating the Mind
1st international Engineering and Neuro-Psychoanalysis Forum
Vienna, July 2007
http://www.indin2007.org/enf/

Slides for the presentation are available here
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#enf07

Abstract:

This paper summarises ideas I have been working on over the last 35 years or so, about relations between the study of natural minds and the design of artificial minds, and the requirements for both sorts of minds. The key idea is that natural minds are information-processing virtual machines produced by evolution. What sort of information-processing machine a human mind is requires much detailed investigation of the many kinds of things minds can do. At present, it is not clear whether producing artificial minds with similar powers will require new kinds of computing machinery or merely much faster and bigger computers than we have now. Some things once thought hard to implement in artificial minds, such as affective states and processes, including emotions, can be construed as aspects of the control mechanisms of minds. This view of mind is largely compatible in principle with psychoanalytic theory, though some details are very different. The therapeutic aspect of psychoanalysis is analogous to run-time debugging of a virtual machine. In order to do psychotherapy well we need to understand the architecture of the machine well enough to know what sorts of bugs can develop and which ones can be removed, or have their impact reduced, and how. Otherwise treatment will be a hit-and-miss affair.

Keywords: architecture, artificial-intelligence, autonomy, behaviour, design-based, emotion, evolution, ghost in machine, information-processing, language, machine, mind, robot, philosophy, psychotherapy, virtual machine.

Filename: sloman-floridi.pdf
Title: How a Philosopher Became an Information-Scientist
Answers to Luciano Floridi's Five Questions

In PHILOSOPHY OF INFORMATION: 5 Questions
Ed. Luciano Floridi, July 2008
ISBN 8792130097
Automatic Press/VIP
Interviews with Margaret A. Boden, Valentino Braitenberg, Brian Cantwell-Smith, Gregory Chaitin, Daniel C. Dennett, Keith Devlin, Fred Dretske, Hubert L. Dreyfus, Luciano Floridi, Tony Hoare, John McCarthy, John R. Searle, Aaron Sloman, Patrick Suppes, Johan van Benthem, Terry Winograd, Stephen Wolfram

Editor's Preface

Abstract:

This paper consists of the author's extended answers to five questions presented by Luciano Floridi to various people.
The answers are all included in a book published by Automatic Press/VIP.

Contents
1 How did it start?

1.1 High level overview
1.2 A longer version of the story

1.2.1 DPhil Research
1.2.2 Meeting Max Clowes
1.2.3 A formative year in Edinburgh
1.2.4 Working on vision at Sussex and Birmingham
1.2.5 Growing COGS at Sussex
1.2.6 Working on robotics at Birmingham
1.2.7 Evolution, development and GLs
1.2.8 The central importance of architectures
1.2.9 Thinking like a designer about emotions and other forms of

affect 2 Examples

2.1 My own work
2.2 Work of others

3 The proper role?
4 Neglected topics
5 Open problems
6 References

BACK TO CONTENTS LIST

NOTE

See also the School of Computer Science Web page.

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Email A.Sloman@cs.bham.ac.uk