PAPERS ADDED IN THE YEAR 2017 (APPROXIMATELY)
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This file Last updated: 25 May 2017
Author: Aaron Sloman
Date installed: 22 Sep 2017
15th Annual Meeting of the International Conference on Cognitive Modelling
University of Warwick, UK. July 2017
List of abstracts: http://mathpsych.org/conferences/2017/file/MP_ICCM2017_Abstract_Booklet.pdf
The Turing-inspired Meta-morphogenesis project begun in 2011 was partly motivated by deep gaps in our understanding of mathematical cognition and other aspects of human and non-human intelligence and our inability to model them. The project attempts to identify previously unnoticed evolutionary transitions in biological information processing related to gaps in our current understanding of cognition. Analysis of such transitions may also shed light on gaps in current AI. This is very different from attempts to study human mathematical cognition directly, e.g. via observation, experiment, neural imaging, etc. Fashionable ideas about "embodied cognition", "enactivism", and "situated cognition", focus on shallow products of evolution, ignoring pressures to evolve increasingly disembodied forms of cognition to meet increasingly complex and varied challenges produced by articulated physical forms, multiple sensory capabilities, geographical and temporal spread of important information and other resources, and "other-related meta-cognition" concerning mental states, processes and capabilities of other individuals. Computers are normally thought of as good at mathematics: they perform logical, arithmetical and statistical calculations and manipulate formulas, at enormous speeds, but still lack abilities in humans and other animals to perceive and understand geometrical and topological possibilities and constraints that (a) are required for perception and use of affordances, and (b) play roles in mathematical, and proto-mathematical, discoveries made by ancient mathematicians, human toddlers and other intelligent animals. Neurally inspired, statistics-based (e.g."deep learning") models cannot explain recognition and understanding of mathematical necessity or impossibility. A partial (neo-Kantian) analysis of types of evolved biological information processing capability still missing from our models may inspire new kinds of research helping to fill the gaps. Had Turing lived long enough to develop his ideas on morphogenesis, he might have done this.
Keywords: Archimedes; Euclid; Kant; geometry; topology; vision; evolution; biological information processing; limita- tions of current computational models evolution as a blind mathematician.
Authors: Ron Chrisley and Aaron Sloman
Date Installed: 21 Sep 2017
Proceedings of EUCognition 2016 Cognitive Robot Architectures
European Association for Cognitive Systems
Vienna, 8-9 December, 2016
Abstract--This paper develops, in sections I-III, the virtual machine architecture approach to explaining certain features of consciousness first proposed in  and elaborated in , in which particular qualitative aspects of experiences (qualia) are proposed to be particular kinds of properties of components of virtual machine states of a cognitive architecture. Specifically, they are those properties of components of virtual machine states of an agent that make that agent prone to believe the kinds of things that are typically believed to be true of qualia (e.g., that they are ineffable, immediate, intrinsic, and private). Section IV aims to make it intelligible how the requirements identified in sections II and III could be realised in a grounded, sensorimotor, cognitive robotic architecture.
Filename: sloman-aisb17-CandP.pdf (PDF)
Progress report on the Turing-inspired Meta-Morphogenesis project
Author: Aaron Sloman
Date Installed: 10 Jul 2017
Expanded preprint for AISB17 Symposium on Computing and Philosophy,
20th April 2017, Bath University, UK
The Turing-inspired Meta-Morphogenesis project was proposed in the final commentary in Alan Turing - His Work and Impact a collection of papers by and about Turing published (by Elsevier) on the occasion of his centenary. The project was also summarised in a keynote talk at AISB2012, suggesting that an attempt to fill gaps in our knowledge concerning evolution of biological information processing may give clues regarding forms of computation in animal brains that have not yet been re-invented by AI researchers, and this may account for some of the enormous gaps between current AI and animal intelligence, including gaps between ancient mathematicians, such as Euclid and current AI systems. Evolution of information processing capabilities and mechanisms is much harder to study than evolution of physical forms and physical behaviours, e.g. because fossil records can provide only very indirect evidence regarding information processing in ancient organisms. Moreover it is very hard to study all the internal details of information processing in current organisms. Some of the reasons will be familiar to programmers who have struggled to develop debugging aids for very complex multi-component AI virtual machines. The paper presents challenges both for the theory of evolution and for AI researchers aiming to replicate natural intelligence, including mathematical intelligence. This is a partial progress report on attempts to meet the challenges by studying evolution of biological information processing, including evolved construction-kits.
Author: Aaron Sloman
Date Installed: 7 Jul 2017
Where published: AISB 2017 Invited talk for Emotions Symposium
This is a summary of some of the ideas in my invited talk for the Symposium on "Computational modelling of emotion: theory and applications" at AISB 2017. A deep understanding of human (or animal) minds requires a broad and deep understanding of the types of information processing functions and information processing mechanisms produced by biological evolution, and how those functions and mechanisms are combined in architectures of increasing sophistication and complexity over evolutionary trajectories leading to new species, and how various kinds of evolved potential are realised by context-sensitive mechanisms during individual development. Some aspects of individual development add context-specific detail to products of the evolutionary history, partly because evolution cannot produce pre-packaged specifications for complete information processing architectures, except for the very simplest organisms. Instead, for more complex organisms, including humans, different architectural layers develop at different times during an individual's life, partly under the influence of the genome and partly under the influence of what the individual has so far experienced, learnt, and developed. This is particularly obvious in language development in humans, but that is a special case of a general biological pattern (identified in joint work with Jackie Chappell, partly inspired by theories of Annette Karmiloff-Smith, among others). This paper complements a paper presented in the Symposium on Computing and Philosophy at AISB 2017, which develops more general ideas about evolution of information processing functions and mechanisms, partly inspired by Turing's work on morphogenesis:
Filename: incomputable-kits-sloman.pdf (PDF)
Title: Construction Kits for Biological Evolution
Pre-publication version Dec 2016, Published by Springer, 2017
Author: Aaron Sloman
Date Installed: 25 May 2017
Where published:Invited contribution to: The Incomputable: Journeys Beyond the Turing Barrier
Eds. Mariya Soskova and S Barry Cooper, 2017
(Note: Barry Cooper died in October 2016, before the book went to press.)
Abstract:This is part of the Turing-inspired Meta-Morphogenesis project, which aims to identify transitions in information processing since the earliest proto-organisms, in order to provide new understanding of varieties of biological intelligence, including the mathematical intelligence that produced Euclid's Elements. (Explaining evolution of mathematicians is much harder than explaining evolution of consciousness!) Transitions depend on "construction kits", including the initial "Fundamental Construction Kit" (FCK) based on physics, and Derived Construction Kits (DCKs) produced by evolution, development, learning and culture. Some construction kits (e.g. Lego, Meccano, plasticine, sand) are concrete: using physical components and relationships. Others (e.g. grammars, proof systems and programming languages) are abstract: producing abstract entities, e.g. sentences, proofs, and new abstract construction kits. Mixtures of the two are hybrid kits. Some are meta-construction kits: they are able to create, modify or combine construction kits. Construction kits are generative: they explain sets of possible construction processes and possible products, with mathematical properties and limitations that are mathematical consequences of properties of the kit and its environment. Evolution and development both make new construction kits possible. Study of the FCK and DCKs can lead us to new answers to old questions, e.g. about the nature of mathematics, language, mind, science, and life, exposing deep connections between science and metaphysics. Showing how the FCK makes its derivatives, including all the processes and products of natural selection, possible is a challenge for science and philosophy. This is a long-term research programme with a good chance of being progressive in the sense of Lakatos. Later, this may explain how to overcome serious current limitations of AI (artificial intelligence), robotics, neuroscience and psychology.
For more information about the Meta-Morphogenesis project, see
Extract from Editor's introduction to the volume (Mariya I. Soskova)The final chapter in this book is a special one. Aaron Sloman reports on his "Meta-Morphogenesis project". This project takes the ideas from Turing's original paper and transforms them to a whole new plane of topics: the evolution of biological and human intelligence. The idea for this project was born when Barry Cooper asked Aaron Sloman to contribute to the book "Alan Turing: His Work and Impact" with a chapter related to Turing's work on morphogenesis. Aaron Sloman, whose prior work was most significantly in artificial intelligence, responded to Barry's challenge with this novel idea and has been working on it ever since, motivated by his intuition that this project can lead to answers to fundamental questions: about the nature of mathematics, language, mind, science, life and on how to overcome current limitations of artificial intelligence.NOTE:
This paper was basically frozen early in 2016. A few minor improvements were included in the published version (after removal of a host of errors and unwanted "improvements" made by Springer Copy-editors). Meanwhile, work on the theory of evolved construction kits continues, and the most recent update can be found here
Date Installed: 25 May 2017
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