Aaron Sloman

In lieu of an abstract, here is a brief excerpt of the content:Response to the CommentariesIan Wright, Aaron Sloman, and Luc BeaudoinWe are very grateful for the care with which the commentators have read our paper, and the sympathy with which they treated what we acknowledged to be at best a preliminary attempt to make sense of a range of phenomena involving grief and other emotions in terms of our draft architecture. We are fortunate to have commentators that are so much in sympathy with what we have written.The comments of Castelfranchi and Miceli draw attention to a problem that we expect many readers to bring up, namely that there is a notion of “information processing” that cannot carry the explanatory weight that we attach to our information processing architecture. We believe that there is a concept of “information processing” such as is used by engineers who design software systems to perform such tasks as controlling an aircraft. We claim that within an appropriate system architecture, information processing states can have all the causal powers that we require for states like pain, pleasure, and the like. However this is not obvious, and defending this claim will have to await another occasion.Lloyd helpfully illustrates the relevance of our proposed architecture even to the effects of imagining the death of a loved one. He also makes the useful observation that second-order states can arise when a person who is suffering because events violate some important motivator, discovers that nothing can be done about the cause of the suffering, and then suffers even more because of feeling helpless in that situation. We acknowledge that these are phenomena that the architecture will have to accommodate, and accept the challenge to demonstrate this in due course.We believe that there is enough commonality between the commentators and ourselves to support our view that the research community is on the verge of an important new breakthrough in the study of mind, in which philosophers, psychologists, therapists, brain scientists, and AI researchers can collaborate fruitfully, and from which profound practical and theoretical consequences will follow.Related ArticlesFeature Article: Towards a Design-Based Analysis of Emotional EpisodesCommentary: Commentary by LloydCommentary: Commentary by Miceli and CastelfranchiCommentary: Commentary by BodenResponse: Response to the CommentariesCopyright © 1996 The Johns Hopkins University Press...

Test domains for AI can have a deep impact on research. The polyflap domain is proposed for testing complex AI theories about architectures, mechanisms and forms of representation involved in features of human and animal intelligence that evolved to enable perception, action, and learning in diverse environments containing things that we can perceive and manipulate, and many complex processes involving objects that differ in shape, materials, causal properties, and relations to one another. We need a test environment that is rich enough to provide some of that variety of structures, processes and affordances, yet simple enough to be within reach of robotics research in the not too distant future.

Most philosophers appear to have ignored the distinction between the broad concept of Virtual Machine Functionalism (VMF) described in Sloman&Chrisley (2003) and the better known version of functionalism referred to there as Atomic State Functionalism (ASF), which is often given as an explanation of what Functionalism is, e.g. in Block (1995). One of the main differences is that ASF encourages talk of supervenience of states and properties, whereas VMF requires supervenience of machines that are arbitrarily complex networks of causally interacting (virtual, but real) processes, possibly operating on different time-scales, examples of which include many different procesess usually running concurrently on a modern computer performing various tasks concerned with handling interfaces to physical devices, managing the file system, dealing with security, providing tools, entertainments, and games, and possibly processing research data. Another example of VMF would be the kind of functionalism involved in a large collection of possibly changing socio-economic structures and processes interacting in a complex community, and yet another is illustrated by the kind of virtual machinery involved in the many levels of visual processing of information about spatial structures, processes, and relationships (including percepts of moving shadows, reflections, highlights, optical-flow patterns and changing affordances) as you walk through a crowded car-park on a sunny day: generating a whole zoo of interacting qualia. (Forget solitary red patches, or experiences thereof.) Perhaps VMF should be re-labelled "Virtual MachinERY Functionalism" because the word 'machinery' more readily suggests something complex with interacting parts. VMF is concerned with virtual machines that are made up of interacting concurrently active (but not necessarily synchronised) chunks of virtual machinery which not only interact with one another and with their physical substrates (which may be partly shared, and also frequently modified by garbage collection, metabolism, or whatever) but can also concurrently interact with and refer to various things in the immediate and remote environment (via sensory/motor channels, and possible future technologies also). I.e. virtual machinery can include mechanisms that create and manipulate semantic content, not only syntactic structures or bit patterns as digital virtual machines do. Please note: Click on the title above or the link below to read the paper. I prefer to keep all my papers freely accessible on my web site so that I can correct mistakes and add improvements. http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html This is now part of the Meta-Morphogenesis project: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html

This paper distinguishes two versions of Ryle's notion of 'logical geography'. Logical geography: The network of relationships between current uses of a collection of concepts. (Probably what Ryle meant by the term.) Logical topography Features of the portion of reality, or types of portions of reality, related to a given set of concepts, where the reality may be capable of being divided up in different ways using different networks of relationships between concepts. Studying/analysing logical topography includes evaluating the alternative divisions in terms of the theories or applications that are supported by the different sets of concepts, or perhaps some other uses of the concepts. On re-reading Appendices III and IV of my Oxford DPhil thesis "Knowing and Understanding Relations between meaning and truth, meaning and necessary truth, meaning and synthetic necessary truth",, online here, I found to my surprise that several of the topics discussed below had been discussed in those two Appendices, and that the distinction between logical topography and logical geography was made in Appendix IV, discussing conceptual analysis, even though I did not use these words

Updates Open Letter to my MP: Lynne Jones Why large IT development projects are problematic The mathematics of searching for a design Richard Feynman wrote: Getting requirements right from the start is impossible Are problems unique to IT projects? Physical constraints Implications for Government policy What can be done? Some suggested prerequisites: requirements for openness A precedent for this proposal: The internet How the internet grew Implications for government policy Are some projects exceptions? Concluding Comment NOTE: Related comment.

Jablonka & Lamb (J&L) refer only implicitly to aspects of cognitive competence that preceded both evolution of human language and language learning in children. These aspects are important for evolution and development but need to be understood using the design-stance, which the book adopts only for molecular and genetic processes, not for behavioural and symbolic processes. Design-based analyses reveal more routes from genome to behaviour than J&L seem to have considered. This both points to gaps in our understanding of evolution and epigenetic processes and may lead to possible ways of filling the gaps

This document explains, from the viewpoint of a philosopher/scientist atheist, why intelligent design should be taught alongside standard evolutionary theory. I have been very disappointed by things I have read by scientists recommending suppression of this topic, and even in one case arguing that the worst arguments in favour of ID should be collected together and refuted, which is a prescription for scientific dishonesty. An honest attack would present the best arguments, as cogently as possible, before exposing their flaws.

The aim of the thesis is to show that there are some synthetic necessary truths, or that synthetic apriori knowledge is possible. This is really a pretext for an investigation into the general connection between meaning and truth, or between understanding and knowing, which, as pointed out in the preface, is really the first stage in a more general enquiry concerning meaning. After the preliminaries, in which the problem is stated and some methodological remarks made, the investigation proceeds in two stages. First there is a detailed inquiry into the manner in which the meanings or functions of words occurring in a statement help to determine the conditions in which that statement would be true. This prepares the way for the second stage, which is an inquiry concerning the connection between meaning and necessary truth. The first stage occupies Part Two of the thesis, the second stage Part Three. In all this, only a restricted class of statements is discussed, namely those which contain nothing but logical words and descriptive words, such as "Not all round tables are scarlet" and "Every three-sided figure is three-angled".

This position paper presents the beginnings of a general theory of representations starting from the notion that an intelligent agent is essentially a control system with multiple control states, many of which contain information (both factual and non-factual), albeit not necessarily in a propositional form. The paper attempts to give a general characterisation of the notion of the syntax of an information store, in terms of types of variation the relevant mechanisms can cope with. Similarly concepts of semantics pragmatics and inference are generalised to apply to information-bearing sub-states in control systems. A number of common but incorrect notions about representation are criticised (such as that pictures are in some way isomorphic with what they represent).

For over half a century I have been interested in the role of intuitive spatial reasoning in mathematics. My Oxford DPhil Thesis (1962) was an attempt to defend Kant's philosophy of mathematics, especially his claim that mathematical proofs extend our knowledge (so the knowledge is "synthetic", not "analytic") and that the discoveries are not empirical, or contingent, but are in an important sense "a priori" (which does not imply "innate") and also necessarily true. I had made my views clear in courses on philosophy of science and mathematics when teaching at Sussex University (from 1964) which was why one of our former students, Mary Pardoe (then Mary Ensor) who had become a mathematics teacher informed me, while visiting the university, that she had found a new diagrammatic proof of the triangle sum theorem. I reported her proof in some papers and presentations on methods of representation and reasoning, e.g. here, but neither she nor I has encountered anyone else who knew about the proof.

How can a virtual machine X be implemented in a physical machine Y? We know the answer as far as compilers, editors, theorem-provers, operating systems are concerned, at least insofar as we know how to produce these implemented virtual machines, and no mysteries are involved. This paper is about extrapolating from that knowledge to the implementation of minds in brains. By linking the philosopher's concept of supervenience to the engineer's concept of implementation, we can illuminate both. In particular, by showing how virtual machines can be implemented in causally complete physical machines, and still have causal powers, we remove some philosophical problems about how mental processes can be real and can have real effects in the world even if the underlying physical implementation has no causal gaps. This requires a theory of ontological levels.

This is a contribution to construction of a research roadmap for future cognitive systems, including intelligent robots, in the context of the euCognition network, and UKCRC Grand Challenge 5: Architecture of Brain and Mind. A meeting on the euCognition roadmap project was held at Munich Airport on 11th Jan 2007. This document was in part a response to discussions at that meeting. An explanation of why specifying requirements is a hard problem, and why it needs to be done, along with some suggestions for making progress, can be found in this presentation: http://www.cs.bham.ac.uk/research/projects/cosy/papers/#pr0701 "What's a Research Roadmap For? Why do we need one? How can we produce one?" Working on that presentation made me realise that certain deceptively familiar words and phrases frequently used in this context (e.g. "robust". "flexible", "autonomous") appear not to need explanation because everyone understands them, whereas in fact they have obscure semantics that needs to be elucidated. Only then can we understand what the implications are for research targets. In particular, they need explanation and analysis if they are to be used to specify requirements and research goals, especially for publicly funded projects. First draft analyses are presented here. In the long term I would like to expand and clarify those analyses, and to provide many different examples to illustrate the points made. This will probably have to be a collaborative research activity.

This paper is an attempt to summarise and justify critical comments I have been making over several decades about research on consciousness by philosophers, scientists and engineers. This includes (a) explaining why the concept of "phenomenal consciousness" (P-C), in the sense defined by Ned Block, is semantically flawed and unsuitable as a target for scientific research or machine modelling, whereas something like the concept of "access consciousness" (A-C) with which it is often contrasted refers to phenomena that can be described and explained within a future scientific theory, and (b) explaining why the "hard problem" is a bogus problem, because of its dependence on the P-C concept. It is compared with another bogus problem, "the 'hard' problem of spatial identity" introduced as part of a tutorial on semantically flawed concepts. Different types of semantic flaw and conceptual confusion not normally studied outside analytical philosophy are distinguished. The semantic flaws of the "zombie" argument, closely allied with the P-C concept are also explained. These topics are related both to the evolution of human and animal minds and brains and to requirements for human-like robots. The diversity of the phenomena related to the concept "consciousness" as ordinarily used makes it a polymorphic concept, partly analogous to concepts like "efficient", "sensitive", and "impediment" all of which need extra information to be provided before they can be applied to anything, and then the criteria of applicability differ. As a result there cannot be one explanation of consciousness, one set of neural associates of consciousness, one explanation for the evolution of consciousness, nor one machine model of consciousness. We need many of each. I present a way of making progress based on what McCarthy called "the designer stance", using facts about running virtual machines, without which current computers obviously could not work. I suggest the same is true of biological minds, because biological evolution long ago "discovered" a need for something like virtual machinery for self-monitoring and self-extending information-processing systems, and produced far more sophisticated versions than human engineers have so far achieved.

This paper outlines a design-based methodology for the study of mind as a part of the broad discipline of Artificial Intelligence. Within that framework some architectural requirements for human-like minds are discussed, and some preliminary suggestions made regarding mechanisms underlying motivation, emotions, and personality. A brief description is given of the `Nursemaid' or `Minder' scenario being used at the University of Birmingham as a framework for research on these problems. It may be possible later to combine some of these ideas with work on synthetic agents inhabiting virtual reality environments.

When scientists discuss experimental observations, they often, unfortunately, use language that evolved for informal discourse among people engaged in every day social interaction, like this: What does the infant/child/adult/chimp/crow (etc) perceive/understand/learn/intend (etc)? What is he/she/it conscious of? What does he/she/it experience/enjoy/desire? What is he/she/it attending to? Similar comments can be made about the terminology used in many philosophical discussions about minds, cognition, language, and the relationships between evolution and learning.

What is the relation between intelligence and computation? Although the difficulty of defining `intelligence' is widely recognized, many are unaware that it is hard to give a satisfactory definition of `computational' if computation is supposed to provide a non-circular explanation for intelligent abilities. The only well-defined notion of `computation' is what can be generated by a Turing machine or a formally equivalent mechanism. This is not adequate for the key role in explaining the nature of mental processes, because it is too general, as many computations involve nothing mental, nor even processes: they are simply abstract structures. We need to combine the notion of `computation' with that of `machine'. This may still be too restrictive, if some non-computational mechanisms prove to be useful for intelligence. We need a theory-based taxonomy of {\em architectures} and {\em mechanisms} and corresponding process types. Computational machines my turn out to be a sub-class of the machines available for implementing intelligent agents. The more general analysis starts with the notion of a system with independently variable, causally interacting sub-states that have different causal roles, including both `belief-like' and `desire-like' sub-states, and many others. There are many significantly different such architectures. For certain architectures (including simple computers), some sub-states have a semantic interpretation for the system. The relevant concept of semantics is defined partly in terms of a kind of Tarski-like structural correspondence (not to be confused with isomorphism). This always leaves some semantic indeterminacy, which can be reduced by causal loops involving the environment. But the causal links are complex, can share causal pathways, and always leave mental states to some extent semantically indeterminate.