PROJECT WEB DIRECTORY
PAPERS ADDED IN THE YEAR 2012 (APPROXIMATELY)
PAPERS 2012 CONTENTS LIST
RETURN TO MAIN COGAFF INDEX FILE
This file is
http://www.cs.bham.ac.uk/research/projects/cogaff/12.html
Maintained by Aaron Sloman -- who does
not respond to Facebook requests.
It contains an index to files in the Cognition and Affect Project's Web directory
produced or published in the year 2012. 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.
Main contents list for the CogAff web site is: 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: 22 Apr 2012; 2 Jun 2012; 20 Dec 2012; 9 Mar 2013
JUMP TO DETAILED LIST (After Contents)
Filename: sloman-on-schmidhuber.pdf (PDF)
Filename: sloman-on-schmidhuber.html (HTML)
Title:
Chapter 4A. Aaron Sloman on Schmidhuber's "New Millennium AI and the Convergence of History 2012"Author: Aaron SlomanCommentary on Chapter 4: New Millennium AI and the Convergence of History: Update of 2012
By Jürgen Schmidhuber
Included in the volume mentioned below. Available online here:
https://docs.google.com/file/d/0BwK0OPe_m9QNbW4yUldmREx1VGs/edit?pli=1
Where published:
In The Singularity Hypotheses: A Scientific and Philosophical Assessment
Eds. Amnon H. Eden, James H. Moor, Johnny H. Soraker and Eric Steinhart, pp 79--80.
Springer-Verlag. Berlin, Heidelberg The Frontiers Collection 2013
Table of contents and some online chapters
Abstract (extracts from short article):
.....
I have problems both with the style and the content of this paper,....
.....
I do not doubt that the combination of technical advances by the author and increases
in computer power have made possible new impressive demonstrations including
out-performing rival systems on various benchmark tests.However, it is not clear to me that those tests have much to do with animal or human
intelligence or that there is any reason to believe this work will help to bridge the
enormous gaps between current machine competences and the competences of squirrels,
nest-building birds, elephants, hunting mammals, apes, and human toddlers.
.....
Filename:
vision-purposes-sloman.pdf
(PDF)
More details: What are the purposes of vision?
Title: What are the purposes of vision?
Based on invited presentation at Fyssen Foundation Workshop on
Vision,
Versailles France, March 1986, Organiser: M. Imbert
(The proceedings were never published.)
Author:
Aaron Sloman
Date Installed: 8 Oct 2012 (Written circa 1986)
Abstract (Extract from Introduction):
The richness, variety and speed of human and many animal visual processes are a constant source of amazement to those who try to design artificial visual systems. By comparison, machine vision still limps along far more slowly and with significantly less functionality. This could be because we don't yet know much about human vision and therefore don't really know what we should be trying to simulate, or it could simply be that the engineering tasks are very difficult, e.g. because we can't yet make cheap highly parallel computers available and we haven't solved enough of the mathematical or programming problems. It could be both. I suspect the former is the main reason, so that until we have a much clearer understanding of what is required, technology will not begin to catch up. A good theory of human vision should describe the interface between visual processes and other kinds of processes, sensory, cognitive, affective, motor, or whatever. This requires some knowledge of the tasks performed by the visual subsystem. Does it feed information only to a central database, where other sub-systems can access it, or does it feed information direct to a variety of sub-systems? What sorts of information does it feed - is it mostly a set of descriptions of spatial properties of the environment, or are there other sorts of descriptions, and other outputs besides descriptions? Is there a sharp boundary between vision and cognition? What sorts of input does the visual subsystem use? I shall attempt to survey the uses of human vision, with the hope of deriving some design constraints and requirements both for theories about biological visual systems and for machine vision. I shall propose a very broad view of the functions of vision in human beings, and suggest some design principles for mechanisms able to fulfil this role, though many details remain unspecified.
File:
http://www.cs.bham.ac.uk/research/projects/cogaff/62-80.html#1980-01
Title: What kind of indirect process is visual perception?
In:
Open Peer Commentary on Shimon Ullman: `Against Direct Perception'
Brain and Behavioural Sciences Journal,3, PP.401-404 1980
Author:
Aaron Sloman
Date Installed: Published 1980 (in BBS),
added here 28 Sep 2012
Filename:sloman-ecai-anniv.pdf
Title: Biological, computational and robotic connections with
Kant's
theory of mathematical knowledge
Author: Aaron
Sloman
Date Installed: 4 Sep 2012 (Presented 29 Aug 2012)
Last Updated: 6 Dec 2012; 13 Apr 2013 (To be expanded later)
Where published:
This is a freely available, (to be expanded), version of an invited paper for the
"Turing and Anniversary Session" at ECAI2012
http://www2.lirmm.fr/ecai2012/.
PDF slides for the conference presentation are here:
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#anniv
A version of this paper will also appear in a speciall issue of the journal AI Communications
The freely available online version here will be modified from time to time, and will have
more references to related work (the journal version is space-restricted).
I am not able to write an authoritative overview of the history of any portion of AI,
or European AI, so this is merely a personal account of some problems that got me
into AI around 1971, but which I think have not yet been addressed adequately.
This version will go on being revised in response to comments, criticisms,
suggestions and afterthoughts, like most of my publications.Closely related web pages
Abstract:
In my research I meander through various disciplines, using fragments of AI that I regard as relevant, willing to learn from anyone whose ideas contribute. This makes me unfit to write the history of European collaboration on some area of AI research as originally intended for the ECAI event and special issue of the Journal AI Communications. However, by interpreting the topic rather loosely, I can regard some European philosophers who were interested in Philosophy of mathematics as early AI researchers from whom I learnt much, such as Kant and Frege. Hume's work is also relevant. Moreover, more recent work by Annette Karmiloff-Smith, begun in Geneva with Piaget then developed independently, helps to identify important challenges for AI (and theoretical neuroscience), that also connect with philosophy of mathematics and the future, rather than the history, of robotics. So this paper presents an idiosyncratic survey of a subset of AI stretching back in time, and deep into other disciplines, including philosophy, psychology and biology, and possibly also deep into the future.Keywords:
Kant, Frege, Mathematics, Representational-Redescription, Empirical knowledge,
Necessary truths, Geometry, Robots
Filename: sloman-computational-mind.pdf (PDF)
Title: Towards a Computational Theory of Mind
Originally in Artificial Intelligence - Human Effects, (Eds) M. Yazdani and A. Narayanan,Author: Aaron Sloman
Ellis Horwood, Chichester, 1984. pp 173--182
Abstract:
(From the introduction to the chapter.)Cognitive Science has three interrelated aspects: theoretical, applied and empirical. Work in all three areas depends on and feeds back into the other two. Theoretical work explores possible computational systems, possible mental processes and structures, attempting to understand what sorts of mechanisms and representational systems are possible, how they differ, what their strengths and weaknesses are, etc. Empirical work studies existing intelligent systems, e.g. humans and other animals. Applied work is both concerned with problems relating to existing minds (e.g. learning difficulties, psychopathology) and also the design of new useful computational systems. This paper sketches some of the assumptions underlying much of the theoretical work, and hints at some of the practical applications. In particular, education and psychotherapy are both activities in which the computational processes in the mind of the pupil or patient are altered. In order to understand what they are doing, educationalists and psychotherapists require a computational theory of mind. This is not the dehumanising notion it may at first appear to be.
Filename: sloman-3cgi-2012-final.pdf
Title:
Meta-morphogenesis and the Creativity of Evolution
Final (shortened) version of paper for proceedings of ECAI 2012 Workshop on
Computational Creativity, Concept Invention, and General Intelligence,
Longer (unpublished) version below ( work
in progress: likely to be further expanded).
This is part of the Meta-Morphogenesis project:
http://tinyurl.com/M-M-Gen
Author: Aaron Sloman
Date Installed: 30 Jun 2012; updated 16 Jul 2012; 20 Dec 2012 (Better
structured)
Abstract:
Whether the mechanisms proposed by Darwin and others suffice to explain the achievements of biological evolution remains open. One problem is the difficulty of knowing exactly what needs to be explained. Evolution of information-processing capabilities and supporting mechanisms is much harder to detect than evolution of physical form, and physical behaviours in part because much goes on inside the organism, and in part because it often has abstract forms whose physical manifestations do not enable us to identify the abstractions easily. Moreover, we may not yet have the concepts required for looking at or thinking about the right things. AI should collaborate with other disciplines in attempting to identify the many important transitions in information processing capabilities, ontologies, forms of representation, mechanisms and architectures that have occurred in biological evolution, in individual development (epigenesis) and in social/cultural evolution - including processes that can modify later forms of evolution and development: meta- morphogenesis. Conjecture: The cumulative effects of successive phases of meta-morphogenesis produce enormous diversity among living information processors, explaining how evolution came to be the most creative process on the planet. Keywords: Architecture, biological information-processing, computation, developmental affordance, evolutionary affordance, learning, morphogenesis, meta-morphogenesis, ontologies, representation, representational redescription, toddler-theorems.
Filename: sloman-3cgi-2012.pdf
Title:
Meta-morphogenesis and the Creativity of Evolution (EXPANDED
VERSION installed 20 Dec 2012)
(Work in progress: may change.)
Part of the Meta-Morphogenesis project:
http://tinyurl.com/M-M-Gen
Author: Aaron Sloman
Date Installed: 16 Jul 2012
Where published:
This is an expanded version of paper for ECAI 2012 Workshop on
``Computational Creativity, Concept Invention, and General Intelligence'',
Monday 27th August 2012, Montpellier.
http://www.cogsci.uni-osnabrueck.de/~c3gi
NOTE: the published version had to be shorter than this version. See above.
Abstract:
Whether the mechanisms proposed by Darwin and others suffice to explain the achievements of biological evolution remains open. Variation in heritable features can occur spontaneously, and Darwinian natural selection can explain why some new variants survive longer than others. But that does not satisfy Darwin's critics and also worries supporters who understand combinatorial search spaces. One problem is the difficulty of knowing exactly what needs to be explained: Most research has focused on evolution of physical form, and physical competences and behaviours, in part because those are observable features of organisms. What is much harder to observe is evolution of information-processing capabilities and supporting mechanisms (architectures, forms of representation, algorithms, etc.). Information-processing in organisms is mostly invisible, in part because it goes on inside the organism, and in part because it often has abstract forms whose physical manifestations do not enable us to identify the abstractions easily. Compare the difficulty of inferring thoughts, percepts or motives from brain measurements, or decompiling computer instruction traces. Moreover, we may not yet have the concepts required for looking at or thinking about the right things: we may need more than the vast expansion of our conceptual tools for thinking about information processing capabilities and mechanisms in the last half century. However, while continually learning what to look for, we can collaborate in attempting to identify the many important transitions in information processing capabilities, ontologies, forms of representation, mechanisms and architectures that have occurred on various time-scales in biological evolution, in individual development (epigenesis) and in social/cultural evolution - including processes that can modify later forms of evolution and development: meta-morphogenesis. Conjecture: The cumulative effects of successive phases of meta-morphogenesis produce enormous diversity among living information processors, explaining how evolution came to be the most creative process on the planet. Progress in AI depends on understanding the products of this process. Keywords: Architecture, biological information-processing, computation, developmental affordance, evolutionary affordance, learning, morphogenesis, meta-morphogenesis, ontologies, representation, representational redescription, toddler-theorems.
Filename: sloman-jmc-aisb.pdf
Title: John McCarthy - Some Reminiscences
Author: Aaron Sloman
Date Installed: 8 Dec 2011; Updated 2 Jun 2012; 6 Jun 2012
Where published:
A personal memoir, published in the AISB Quarterly, no 133, January 2012, pp 7-10.
This is an expanded version of a pre-publication draft, including some afterthoughts.
See also The AISB Quarterly Archive
Abstract:
[Extract from opening paragraphs]John McCarthy died aged 84 on 24th October 2011. Since then, much has been written about his life and work (e.g. search for his name and "homage", or "obituary"), and no doubt there will be much more. I shall not attempt to emulate or compete with any of the formal obituaries. Instead, I'll offer a few personal recollections and reflections. There is also much to read on his web site, since he was one of the people who led the way in making everything he wrote freely available to all. It was from him that I learnt to cross out any part of a publisher's copyright agreement that restricted my right to post versions of my papers on my web site. Only one publisher has ever objected (so I withdrew the paper). One of the most important events in my academic life occurred when Max Clowes, then the leading AI researcher at Sussex university, introduced me to AI, allowed me to attend his programming tutorials, and gave me things to read, by Simon, Newell, Minsky, McCarthy and others. It quickly became clear that AI was very relevant to old philosophical problems, especially in the papers I read by Minsky and McCarthy. One day Max suggested that I should read the 1969 paper by McCarthy and Hayes, and lent me his copy. I found it very interesting, especially the distinction between metaphysical, epistemological and heuristic adequacy of forms of representation of the world (echoing, but different from, the three kinds of adequacy in (Chomsky, 1965)). ....
Filename: sloman-aslib83.pdf
Title:
An Overview Of Some Unsolved Problems In Artificial Intelligence
Author: Aaron Sloman
Date: 1983 (installed here 19 Mar 2012)
Where published:
Intelligent Information Retrieval: Informatics 7, 1983 (pp.3--14)
Ed. Kevin P. Jones
Proceedings Cambridge Aslib Informatics 7 Conference, Cambridge 22-23 March 1983.
Abstract (Extract from Introduction):
It is rash for the first speaker at a conference to offer to talk about unsolved problems: the risk is that subsequent papers will present solutions. To minimise this risk, I resolved to discuss only some of the really hard long term problems. Consequently, I'll have little to say about solutions! These long-term problems are concerned with the aim of designing really intelligent systems. Of course, it is possible to quibble endlessly about the definition of 'intelligent', and to argue about whether machines will ever really be intelligent, conscious, creative, etc. I want to by-pass such semantic debates by indicating what I understand by the aim of designing intelligent machines. I shall present a list of criteria which I believe are implicitly assumed by many workers in Artificial Intelligence to define their long term aims. Whether these criteria correspond exactly to what the word 'intelligent' means in ordinary language is an interesting empirical question, but is not my present concern. Moreover, it is debatable whether we should attempt to make machines which meet these criteria, but for present purposes I shall take it for granted that this is a worthwhile enterprise, and address some issues about the nature of the enterprise. Finally, it is not obvious that it is possible to make artefacts meeting these criteria. For now I shall ignore all attempts to prove that the goal us unattainable. Whether it is attainable or not, the process of attempting to design machines with these capabilities will teach us a great deal, even if we achieve only partial successes.
See also the School of Computer Science Web page.
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