Much discussion of emotions and related topics is riddled with confusion because different authors use the key expressions with different meanings. Some confuse the concept of "emotion" with the more general concept of "affect", which covers other things besides emotions, including moods, attitudes, desires, preferences, intentions, dislikes, etc. Moreover researchers have different goals: some are concerned with understanding natural phenomena, while others are more concerned with producing useful artifacts, e.g. synthetic entertainment agents, sympathetic machine interfaces, and the like. We address this confusion by showing how "architecture-based" concepts can extend and refine our pre-theoretical concepts in ways that make them more useful both for expressing scientific questions and theories, and for specifying engineering objectives. An implication is that different information-processing architectures support different classes of emotions, different classes of consciousness, different varieties of perception, and so on. We start with high level concepts applicable to a wide variety of types of natural and artificial systems, including very simple organisms, namely concepts such as "need", "function", "information-user", "affect", "informationprocessing architecture". For more complex architectures, we offer the CogAff schema as a generic framework which distinguishes types of components that may be in a architecture, operating concurrently with different functional roles. We also sketch H-Cogaff, a richlyfeatured special case of CogAff, conjectured as a type of architecture that can explain or replicate human mental phenomena. We show how the concepts that are definable in terms of such architectures can clarify and enrich research on human emotions. If successful for the purposes of science and philosophy the architecture is also likely to be useful for engineering purposes, though many engineering goals can be achieved using shallow concepts and shallow theories, e.g., producing "believable" agents for computer entertainments. The more human-like robot emotions will emerge, as they do in humans, from the interactions of many mechanisms serving different purposes, not from a particular, dedicated "emotion mechanism".

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This is not a scholarly research paper, but a ‘position paper’ outlining an approach to the study of mind which has been gradually evolving (at least in my mind) since about 1969 when I first become acquainted with work in Artificial Intelligence through Max Clowes. I shall try to show why it is more fruitful to construe the mind as a control system than as a computational system (although computation can play a role in control mechanisms).

This paper is about how to give human-like powers to complete agents. For this the most important design choice concerns the overall architecture. Questions regarding detailed mechanisms, forms of representations, inference capabilities, knowledge etc. are best addressed in the context of a global architecture in which different design decisions need to be linked. Such a design would assemble various kinds of functionality into a complete coherent working system, in which there are many concurrent, partly independent, partly mutually supportive, partly potentially incompatible processes, addressing a multitude of issues on different time scales, including asynchronous, concurrent, motive generators. Designing human like agents is part of the more general problem of understanding design space, niche space and their interrelations, for, in the abstract, there is no one optimal design, as biological diversity on earth shows.

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Some issues concerning requirements for architectures, mechanisms, ontologies and forms of representation in intelligent human-like or animal-like robots are discussed. The tautology that a robot that acts and perceives in the world must be embodied is often combined with false premises, such as the premiss that a particular type of body is a requirement for intelligence, or for human intelligence, or the premiss that all cognition is concerned with sensorimotor interactions, or the premiss that all cognition is implemented in dynamical systems closely coupled with sensors and effectors. It is time to step back and ask what robotic research in the past decade has been ignoring. I shall try to identify some major research gaps by a combination of assembling requirements that have been largely ignored and design ideas that have not been investigated -partly because at present it is too difficult to make significant progress on those problems with physical robots, as too many different problems need to be solved simultaneously. In particular, the importance of studying some abstract features of the environment about which the animal or robot has to learn (extending ideas of J.J.Gibson) has not been widely appreciated.