Aaron Sloman

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.

he design-based approach is a methodology for investigating mechanisms capable of generating mental phenomena, whether introspectively or externally observed, and whether they occur in humans, other animals or robots. The study of designs satisfying requirements for autonomous agency can provide new deep theoretical insights at the information processing level of description of mental mechanisms. Designs for working systems (whether on paper or implemented on computers) can systematically explicate old explanatory concepts and generate new concepts that allow new and richer interpretations of human phenomena. To illustrate this, some aspects of human grief are analysed in terms of a particular information processing architecture being explored in our research group. We do not claim that this architecture is part of the causal structure of the human mind; rather, it represents an early stage in the iterative search for a deeper and more general architecture, capable of explaining more phenomena. However even the current early design provides an interpretative ground for some familiar phenomena, including characteristic features of certain emotional episodes, particularly the phenomenon of perturbance (a partial or total loss of control of attention). The paper attempts to expound and illustrate the design-based approach to cognitive science and philosophy, to demonstrate the potential effectiveness of the approach in generating interpretative possibilities, and to provide first steps towards an information processing account of `perturbant', emotional episodes.

My favourite leading question when teaching Philosophy of Mind is ‘Could a goldfish long for its mother?’ This introduces the philosophical technique of ‘conceptual analysis’, essential for the study of mind (Sloman 1978, ch. 4). By analysing what we mean by ‘A longs for B’, and similar descriptions of emotional states we see that they inv olve rich cognitive structures and processes, i.e. computations. Anything which could long for its mother, would have to hav e some sort of representation of its mother, would have to believe that she is not in the vicinity, would have to be able to represent the _possibility _of being close to her, would have to desire that possibility, and would have to be to some extent pre-occupied or obsessed with that desire. That is, it should intrude into and interfere with other activities, like admiring the scenery, catching smaller fish, etc. If the desire were there, but could be calmly put aside, whilst other interests were pursued, then it would not be truly a state of longing. It might be a state of preferring. Thus longing involves computational interrupts. The same seems to be true of all emotions

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.

Response to "Predictive Policies" by R.S.McGowan Mr. McGowan has assumed that there is a clear distinction between inductive inferences and others, that we all know how to make the distinction, that we all agree that the inductive ones are somehow better or more reasonable than the alternatives, and I have criticised all of these assumptions. Further he hasformulated the philosophical problem of induction as the problem of showing why the inductive ones are better, and he has attempted to show that inductive policiesas represented in his flow chart are better than non-inductive and counter-inductive ones. I have criticised some of the details of his argument and put forward the counter-claim that policies based on the weaker principles of desistence are better at avoiding contradictions and conform to past experience more closely than policies based on his strong principle of persistence. Accordingly, some modifications of his predictive rules have been suggested. Perhaps most importantly of all, I have argued that the assertion that one policy or inference is better or more rational than another is an incomplete assertion until a basis of comparison has been specified, since different policies may be better or more rational in relation to different bases, and I have indicated some possible approaches for further investigation of this point. A final line of investigation which should be mentioned is the problem of deciding which of two bases of comparison is better relative to some higher-order basis of comparison, a problem which may turn out to be very important in connexion with justifications of predictive policies. It seems that I have asked more questions than I have answered. Perhaps formulating them will help someone more familiar with the field than I am to find interesting answers.

This paper attempts to characterise a unifying overview of the practice of software engineers, AI designers, developers of evolutionary forms of computation, designers of adaptive systems, etc. The topic overlaps with theoretical biology, developmental psychology and perhaps some aspects of social theory. Just as much of theoretical computer science follows the lead of engineering intuitions and tries to formalise them, there are also some important emerging high level cross disciplinary ideas about natural information processing architectures and evolutionary mechanisms and that can perhaps be unified and formalised in the future. There is some speculation about the evolution of human cognitive architectures and consciousness.

The profound influence of Frege's work on the development of formal logic and philosophy of mathematics is now well known. However, his philosophical writings, which, unlike his technical achievements, are still of more than historical interest, appear to be comparatively little known or ill-understood, at least by philosophers who are not primarily interested in the foundations of mathematics. This is a great pity, for Frege's thought is precise, penetrating, subtle and often relevant to philosophical problems which are still under discussion. Perhaps a wider recognition of his importance will be hastened by the translation of more of his works into English, and, it is to be hoped, by the publication of papers reported to be still in the possession of his literary executors. (Two books on Frege are reviewed)

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.

There is now a huge amount of interest in consciousness among scientists as well as philosophers, yet there is so much confusion and ambiguity in the claims and counter-claims that it is hard to tell whether any progress is being made. This ``position paper'' suggests that we can make progress by temporarily putting to one side questions about what consciousness is or which animals or machines have it or how it evolved. Instead we should focus on questions about the sorts of architectures that are possible for behaving systems and ask what sorts of capabilities, states and processes, might be supported by different sorts of architectures. We can then ask which organisms and machines have which sorts of architectures. This combines the standpoint of philosopher, biologist and engineer. If we can find a general theory of the variety of possible architectures (a characterisation of ``design space'') and the variety of environments, tasks and roles to which such architectures are well suited (a characterisation of ``niche space'') we may be able to use such a theory as a basis for formulating new more precisely defined concepts with which to articulate less ambiguous questions about the space of possible minds. For instance our initially ill-defined concept (``consciousness'') might split into a collection of more precisely defined concepts which can be used to ask unambiguous questions with definite answers. As a first step this paper explores a collection of conjectures regarding architectures and their evolution. In particular we explore architectures involving a combination of coexisting architectural levels including: (a) reactive mechanisms which evolved very early, (b) deliberative mechanisms which evolved later in response to pressures on information processing resources and (c) meta-management mechanisms that can explicitly inspect evaluate and modify some of the contents of various internal information structures. It is conjectured that in response to the needs of these layers, perceptual and action subsystems also developed layers, and also that an ``alarm'' system which initially existed only within the reactive layer may have become increasingly sophisticated and extensive as its inputs and outputs were linked to the newer layers. Processes involving the meta-management layer in the architecture could explain the origin of the notion of ``qualia''. Processes involving the ``alarm'' mechanism and mechanisms concerned with resource limits in the second and third layers gives us an explanation of three main forms of emotion, helping to account for some of the ambiguities which have bedevilled the study of emotion. Further theoretical and practical benefits may come from further work based on this design-based approach to consciousness. A deeper longer term implication is the possibility of a new science investigating laws governing possible trajectories in design space and niche space, as these form parts of high order feedback loops in the biosphere