PROJECT WEB DIRECTORY
PAPERS ADDED IN THE YEAR 2010 (APPROXIMATELY)
Maintained by Aaron Sloman.
It contains an index to files in the Cognition and Affect Project's FTP/Web directory produced or published in the year 2010. 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
This file Last updated: 12 Jan 2010; 28 Jun 2010; 2 Jul 2010;
13 Nov 2010; 22 Mar 2011; 7 Jul 2012
Issue 75 of the NCETM Secondary Magazine,
The Interview - Aaron Sloman (Interviewed by Mary Pardoe, NCETM)
Until 2005 Aaron was Professor of Artificial Intelligence and Cognitive Science in the School of Computer Science at the University of Birmingham. He believes that too many computer experts are products of a disastrous educational mistake, and have no idea what has been lost.
POPLOG's Two-level Virtual Machine Support for Interactive Languages
Authors: Robert Smith, Aaron Sloman and John Gibson
5 Jan 2018 Moved to http://www.cs.bham.ac.uk/research/projects/cogaff/81-95.html#65
Author: Aaron Sloman
Date Installed: 2 Jul 2010
Invited (500 word) Commentary on:
"The Symbol Grounding Problem Has Been Solved: Or Maybe Not?"
by Angelo Cangelosi
In AMD Newsletter (PDF), Vol 7, No. 1, 2010.
Editor: Pierre-Yves Oudeyer, pp. 2-3.
This commentary is on pp.7-8.
See also http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk49
Why symbol-grounding is both impossible and unnecessary, and why theory-tethering is more powerful anyway.
(Introduction to key ideas of semantic models, implicit definitions and symbol tethering through theory tethering.)
Title: How Virtual Machinery Can Bridge the "Explanatory Gap", In Natural and Artificial Systems
Author: Aaron Sloman
Date Installed: 19 May 2010
Invited talk for SAB2010, August 2010.
This paper is included in the Conference proceedings:
S. Doncieux et al. (Eds.): SAB 2010, LNAI 6226, pp. 13--24. Springer, Heidelberg (2010
The original will be available at www.springerlink.com
We can now show in principle how evolution could have produced the ``mysterious'' aspects of consciousness if, like engineers in the last six or seven decades, it had to solve increasingly complex problems of representation and control by producing systems with increasingly abstract, but effective, mechanisms, including self-observation capabilities, implemented in non-physical virtual machines which, in turn, are implemented in lower level physical mechanisms. For this, evolution would have had to produce far more complex virtual machines than human engineers have so far managed, but the key idea might be the same. However it is not yet clear whether the biological virtual machines could have been implemented in the kind of discrete technology used in computers as we know them.
Architecture, Body, Causation, Cognition, Consciousness, Darwin, Designer Stance, Evolution, Explanatory Gap, Huxley, Mind, Virtual Machinery
DRAFT: to be revised
Title: The Design-Based Approach to the Study of Mind
(in humans, other animals, and machines)
Including the Study of Behaviour Involving Mental Processes
Author: Aaron Sloman
Date Installed: 18 Mar 2010
Paper written for AIIB Symposium 2010 on "AI-Inspired Biology", at AISB 2010 convention.
Abstract. There is much work in AI that is inspired by natural intelligence, whether in humans, other animals or evolutionary processes. In most of that work the main aim is to solve some practical problem, whether the design of useful robots, planning/scheduling systems, natural language interfaces, medical diagnosis systems or others. Since the beginning of AI there has also been an interest in the scientific study of intelligence, including general principles relevant to the design of machines with various sorts of intelligence, whether biologically inspired or not. The first explicit champion of that approach to AI was John McCarthy, though many others have contributed, explicitly or implicitly, including Alan Turing, Herbert Simon, Marvin Minsky, Ada Lovelace a century earlier, and others. A third kind of interest in AI, which is at least as old, and arguably older, is concerned with attempting to search for explanations of how biological systems work, including humans, where the explanations are sufficiently deep and detailed to be capable of inspiring working designs. That design-based attempt to understand natural intelligence, in part by analysing requirements for replicating it, is partly like the older mathematics-based attempt to understand physical phenomena. However a difference is that there is no requirement for an adequate mathematical model to be capable of replicating the phenomena to be explained: Newton's equations did not produce a new solar system, though they helped to explain and predict observed behaviours in the old one. This paper attempts to explain some of the main features of the design-based approach to understanding natural intelligence, many of them already well known, though not all. The design based approach makes heavy use of what we have learnt about computation since Ada Lovelace. But it should not be restricted to forms of computation that we already understand and which can be implemented on modern computers. We need an open mind as to what sorts of information-processing systems can exist and which varieties were produced by biological evolution. We also need to be on guard against erroneous attempts to use AI to inspire biology.
Keywords: Design, niche, virtual machinery, physical, mental, model, explanation, biology, AI, commone-sense.
Author: Aaron Sloman
Date: Installed: 12 Jan 2010
for Symposium on Mathematical Practice and Cognition
29th - 30th March, 2010, De Montfort University, Leicester
Part of AISB 2010 Convention
This is the latest progress report on a long term quest to defend Kant's philosophy of mathematics. In humans, and other species with competences that evolved to support interactions with a complex, varied and changing 3-D world, some competences go beyond discovered correlations linking sensory and motor signals. Dealing with novel situations or problems requires abilities to work out what can, cannot, or must happen in the environment, under certain conditions. I conjecture that in humans these products of evolution form the basis of mathematical competences. Mathematics grows out of the ability to use, reflect on, characterise, and systematise both the discoveries that arise from such competences and the competences themselves. So a "baby" human-like robot, with similar initial competences and meta-competences, could also develop mathematical knowledge and understanding, acquiring what Kant called synthetic, non-empirical knowledge. I attempt to characterise the design task and some ways of making progress, in part by analysing transitions in child or animal intelligence from empirical learning to being able to "work things out". This may turn out to include a very general phenomenon involved in so-called "U-shaped" learning, including the language learning that evolved later. Current techniques in AI/Robotics are nowhere near this. A long term collaborative project investigating the evolution and development of such competences may contribute to robot design, to developmental psychology, to mathematics education and to philosophy of mathematics. There is still much to do.
See also the School of Computer Science Web page.