PROJECT WEB DIRECTORY
PAPERS ADDED IN THE YEAR 2008 (APPROXIMATELY)
PAPERS 2008 CONTENTS LIST
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This file is
Maintained by Aaron Sloman -- who does not respond to Facebook requests.
It contains an index to files in the Cognition and Affect Project's FTP/Web directory produced or published in the year 2008. Some of the papers published in this period were produced earlier and are included in one of the lists for an earlier period. Some older papers recently digitised may also be included. http://www.cs.bham.ac.uk/research/cogaff/0-INDEX.html#contents
A list of PhD and MPhil theses was added in June 2003
This file Last updated: 3 Nov 2008; 22 Oct 2010; 13 Nov 2010; 2 Aug 2011
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See Entries for 2008 in the CoSy project
In Artificial Intelligence (December 2008)ABSTRACT:
The third 'hottest' AIJ article October-December 2008
This paper complements McCarthy's "The well designed child" (published in the same issue of AIJ), in part by putting it in a broader context, the space of possible well designed progeny, and in part by relating design features to development of mathematical competence.
I first moved into AI in an attempt to understand myself, especially hoping to understand how I could do mathematics. Over the ensuing four decades, my interactions with AI and other disciplines led to: design-based, cross-disciplinary investigations of requirements, especial those arising from interactions with a complex environment; a draft partial ontology for describing spaces of possible architectures, especially virtual machine architectures, for behaving systems (including our precursors); investigations of varied forms of representation and how they are suited to different functions; analysis of biological nature/nurture tradeoffs and their relevance to future machines; studies of control issues in a complex architecture; and showing how the states and processes possible in such an architecture relate to our (simplified) intuitive concepts of motivation, feeling, preferences, emotions, attitudes, values, moods, consciousness, etc. In 1971 I thought working models of human vision could lead to models of visual/spatial reasoning that would help to support Kant's view of mathematics, against Hume's. This has not yet happened, but I am still exploring requirements for such models, partly motivated by the hypothesis that human mathematical abilities are a natural extension of abilities produced by biological evolution that are not yet properly understood, and have barely been noticed by psychologists and neuroscientists. Some aspects of our ability to interact with complex 3-D structures and processes extend Gibson's ideas concerning action affordances, to include proto-affordances, epistemic affordances and deliberative affordances. Some of what a child learns about structures and processes starts as empirical then as a result of reflective processes can be transformed to the status of necessary (e.g. mathematical) truths. These processes normally develop unnoticed in young children, but provide the basis for much creativity in behaviour, as well as leading, in some, to development of an interest in mathematics. We still need to understand what sort of (possibly self-extending) architecture, and what forms of representation, are required to make this possible. This paper does not presuppose that all mathematical learners can do logic, though some fairly general form of reasoning seems to be required.
The paper includes a discussion of the importance of virtual machines for engineering and biology.
Filename: sloman-compmod07.pdf (PDF)
TITLE: Architectural and representational requirements for seeing processes, proto-affordances and affordances.
AUTHOR: Aaron Sloman
DATE INSTALLED: 22 Feb 2008 (Updated 30 May 2008)
Presented with the titleABSTRACT: This paper, combining the standpoints of philosophy and Artificial Intelligence with theoretical psychology, summarises several decades of investigation by the author of the variety of functions of vision in humans and other animals, pointing out that biological evolution has solved many more problems than are normally noticed. For example, the biological functions of human and animal vision are closely related to the ability of humans to do mathematics, including discovering and proving theorems in geometry, topology and arithmetic. Many of the phenomena discovered by psychologists and neuroscientists require sophisticated controlled laboratory settings and specialised measuring equipment, whereas the functions of vision reported here mostly require only careful attention to a wide range of everyday competences that easily go unnoticed. Currently available computer models and neural theories are very far from explaining those functions, so progress in explaining how vision works is more in need of new proposals for explanatory mechanisms than new laboratory data. Systematically formulating the requirements for such mechanisms is not easy. If we start by analysing familiar competences, that can suggest new experiments to clarify precise forms of these competences, how they develop within individuals, which other species have them, and how performance varies according to conditions. This will help to constrain requirements for models purporting to explain how the competences work. For example, Gibson's theory of affordances needs a number of extensions, including allowing affordances to be composed in several ways from lower level proto-affordances. The paper ends with speculations regarding the need for new kinds of information-processing machinery to account for the phenomena."WHAT ARE WE TRYING TO DO, AND HOW DO LOGIC AND PROBABILITY FIT INTO THE BIGGER PICTURE?at Dagstuhl workshop on "Logic and Probability for Scene Interpretation", 24th-29th Feb 2008,
Understanding the functions of animal vision".
This is an expanded version of contribution to Proceedings of BBSRC-funded Computational Modelling Workshop, Birmingham 2007, edited Dietmar Heinke
Closing the gap between neurophysiology and behaviour:
A computational modelling approach
University of Birmingham, United Kingdom
May 31st-June 2nd 2007
Invited talk for Workshop on MetaReasoning: Thinking about Thinking at AAAI'08, Washington, July 2008.ABSTRACT:
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
Title: Commentary on Boden on "Artificial Intelligence and Animal Psychology"
Authors: Aaron Sloman
Date Published: 1983
Date Installed: 16 Dec 2008
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.Extracts
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.
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
What Cognitive Scientists Need to Know about Virtual Machines}
Proceedings Cognitive Science Conference, Amsterdam 2009.
Supervenience and Causation in Virtual Machinery (and related presentations).
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  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 ApproachAuthors: Aaron Sloman
Eds. Dietrich, D.; Fodor, G.; Zucker, G.; Bruckner, D. 2009, (Actually available 2008)
Also published in 5th IEEE International Conference on Industrial Informatics, 23-27 June 2007
This was an invited paper for ENF07' 2007 Emulating the Mind
1st international Engineering and Neuro-Psychoanalysis Forum
Vienna, July 2007
Slides for the presentation are available here
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.
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Title: How a Philosopher Became an Information-Scientist
Answers to Luciano Floridi's Five Questions
PHILOSOPHY OF INFORMATION: 5 Questions
Ed. Luciano Floridi, July 2008
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
Author: Aaron Sloman
Date Installed: 29 Jun 2008
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.
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
2.1 My own work
3 The proper role?
2.2 Work of others
4 Neglected topics
5 Open problems
Older files in this directory (pre 2008) are accessible via the main index
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See also the School of Computer Science Web page.
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