Aaron Sloman

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. ...&quot

John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong AI. This paper explores some of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs.

Animals and robots perceiving and acting in a world require an ontology that accommodates entities, processes, states of affairs, etc., in their environment. If the perceived environment includes information - processing systems, the ontology should reflect that. Scientists studying such systems need an ontology that includes the first - order ontology characterising physical phenomena, the second - order ontology characterising perceivers of physical phenomena, and a third order ontology characterising perceivers of perceivers, including introspectors. We argue that second - and third - order ontologies refer to contents of virtual machines and examine requirements for scientific investigation of combined virtual and physical machines, such as animals and robots. We show how the CogAff architecture schema, combining reactive, deliberative, and meta - management categories, provides a first draft schematic third - order ontology for describing a wide range of natural and artificial agents. Many previously proposed architectures use only a subset of CogAff, including subsumption architectures, contention - scheduling systems, architectures with Ôexecutive functionsÕ and a variety of types of ÔOmegaÕ architectures. Adding a multiply - connected, fastacting ÔalarmÕ mechanism within the CogAff framework accounts for several varieties of emotions. H - CogAff, a special case of CogAff, is postulated as a minimal architecture specification for a human - like system. We illustrate use of the CogAff schema in comparing H - CogAff with Clarion, a well known architecture. One implication is that reliance on concepts tied to observation and experiment can harmfully restrict explanatory theorising, since what an information processor is doing cannot, in general, be determined by using the standard observational techniques of the physical sciences or laboratory experiments. Like theoretical physics, cognitive science needs to be highly speculative to make progress. Ó 2004 Published by Elsevier B. V.

"The Emperor's New Mind" by Roger Penrose has received a great deal of both praise and criticism. This review discusses philosophical aspects of the book that form an attack on the "strong" AI thesis. Eight different versions of this thesis are distinguished, and sources of ambiguity diagnosed, including different requirements for relationships between program and behaviour. Excessively strong versions attacked by Penrose (and Searle) are not worth defending or attacking, whereas weaker versions remain problematic. Penrose (like Searle) regards the notion of an algorithm as central to AI, whereas it is argued here that for the purpose of explaining mental capabilities the architecture of an intelligent system is more important than the concept of an algorithm, using the premise that what makes something intelligent is not what it does but how it does it. What needs to be explained is also unclear: Penrose thinks we all know what consciousness is and claims that the ability to judge Go "del's formula to be true depends on it. He also suggests that quantum phenomena underly consciousness. This is rebutted by arguing that our existing concept of "consciousness" is too vague and muddled to be of use in science. This and related concepts will gradually be replaced by a more powerful theory-based taxonomy of types of mental states and processes. The central argument offered by Penrose against the strong AI thesis depends on a tempting but unjustified interpretation of Goedel's incompleteness theorem. Some critics are shown to have missed the point of his argument. A stronger criticism is mounted, and the relevance of mathematical Platonism analysed. Architectural requirements for intelligence are discussed and differences between serial and parallel implementations analysed.

This is a 5 page summary with three diagrams of the main objectives and some work in progress at the University of Birmingham Cognition and Affect project. involving: Professor Glyn Humphreys (School of Psychology), and Luc Beaudoin, Chris Paterson, Tim Read, Edmund Shing, Ian Wright, Ahmed El-Shafei, and (from October 1994) Chris Complin (research students). The project is concerned with "global" design requirements for coping simultaneously with coexisting but possibly unrelated goals, desires, preferences, intentions, and other kinds of motivators, all at different stages of processing. Our work builds on and extends seminal ideas of H.A.Simon (1967). We are exploring "broad and shallow" architectures combining varied capabilities most of which are not implemented in great depth. The poster summarises some ideas about management and meta-management processes, attention filtering, and the relevance to emotional states involved "perturbances", where there is partial loss of control of attention.

Emotions involve complex processes produced by interactions between motives, beliefs, percepts, etc. E.g. real or imagined fulfilment or violation of a motive, or triggering of a 'motive-generator', can disturb processes produced by other motives. To understand emotions, therefore, we need to understand motives and the types of processes they can produce. This leads to a study of the global architecture of a mind. Some constraints on the evolution of minds are disussed. Types of motives and the processes they generate are sketched.

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.

Since the 1970s AI as a science has progressively fragmented into many activities that are very narrowly focused. It is not clear that work done within these fragments can be combined in the design of a human-like integrated system – long held as one of the goals of AI as science. A strategy is proposed for reintegrating AI based around a backward-chaining analysis to produce a roadmap with partially ordered milestones, based on detailed scenarios, that everyone can agree are worth achieving, even when they disagree about means.

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...