There is much shallow thinking about emotions, and a huge diversity of definitions of “emotion ” arises out of this shallowness. Too often the definitions and theories are inspired either by a mixture of introspection and selective common sense, or by a misdirected neo-behaviourist methodology, attempting to define emotions and other mental states in terms of observables. One way to avoid such shallowness, and perhaps eventually achieve convergence, is to base concepts and theories on an information processing architecture, which is subject to various constraints, including evolvability, implementability, coping with resource-limited physical mechanisms, and human-like functionality. Within such an architecture-based theory we can distinguish (at least) primary emotions, secondary emotions, and tertiary emotions, and produce a coherent theory which explains a wide range of phenomena and also partly explains the diversity of theories: most theorists focus on only a subset of types of emotions.

Abstract. A growing number of research projects in academia and industry have recently started to develop lifelike agents as a new metaphor for highly personalised human-machine communication. A strong argument in favour of using such characters in the interface is the fact that they make humancomputer interaction more enjoyable and allow for communication styles common in human-human dialogue. Our earlier work in this area has concentrated on the development of animated presenters that show, explain, and verbally comment textual and graphical output on a window-based interface. Even though first empirical studies have been very encouraging and revealed a strong affective impact of our Personas [23], they also suggest that simply embodying an interface agent is insufficient. To come across as believable, an agent needs to incorporate a deeper model of personality and emotions, and in particular directly connect these two concepts.

A problem which bedevils the study of emotions, and the study of consciousness, is that we assume a shared understanding of many everyday concepts, such as `emotion', `feeling', `pleasure', `pain', `desire', `awareness', etc. Unfortunately, these concepts are inherently very complex, ill-defined, and used with different meanings by different people. Moreover this goes unnoticed, so that people think they understand what they are referring to even when their understanding is very unclear. Consequently there is much discussion that is inherently vague, often at cross-purposes, and with apparent disagreements that arise out of people unwittingly talking about different things. We need a framework which explains how there can be all the diverse phenomena that different people refer to when they talk about emotions and other affective states and processes. The conjecture on which this paper is based is that adult humans have a type of information-processing architecture, with components whi...

In the last decade and a half, the amount of work on affect in general and emotion in particular has grown, in empirical psychology, cognitive science and AI, both for scientific purposes and for the purpose of designing synthetic characters, e.g. in games and entertainments. Such work understandably starts from concepts of ordinary language (e.g. "emotion", "feeling", "mood", etc.). However, these concepts can be deceptive: the words appear to have clear meanings but are used in very imprecise and systematically ambiguous ways. This is often because of explicit or implicit pre-scientific theories about mental states and process. More sophisticated theories can provide a basis for deeper and more precise concepts, as has happened in physics and chemistry. In the Cognition and Affect project we have been attempting to explore the benefits of developing architecture-based concepts, i.e. starting with specifications of architectures for complete agents and then finding out what so...

We summarize selected recent developments and promising directions for improving the quality of models of human performance in synthetic environments. The potential uses and goals for behavioral models in synthetic environments are first summarized. Within that context, we examine relevant, current work related to modeling more complete performance, for example, on cognitive modeling of emotion, advanced techniques for testing and building models of behavior, new cognitive architectures, and agent and Belief, Desires and Intentions (BDI) technology. The report also considers the usability of these systems as an important but neglected aspect of their performance. A list of projects with high payoff for modeling human performance in synthetic environments is noted.

There are evolutionary trajectories in two different but related spaces, design space and niche space. Co-evolution occurs in parallel trajectories in both spaces, with complex feedback loops linking them. As the design of one species evolves, that changes the niche for others and vice versa. In general there will never be a unique answer to the question: does this change lead to higher fitness? Rather there will be tradeoffs: the new variant is better in some respects and worse in others. Where large numbers of mutually interdependent species (designs) are co-evolving, understanding the dynamics can be very difficult. If intelligent organisms manipulate some of the mechanisms, e.g. by mate selection or by breeding other animals or their own kind, the situation gets even more complicated. It may be possible to show how some aspects of the evolution of human minds are explained by all these mechanisms.

The OCC (Ortony, Clore, & Collins, 1988) model has established itself as the standard model for emotion synthesis. A large number of studies employed the OCC model to generate emotions for their embodied characters. Many developers of such characters believe that the OCC model will be all they ever need to equip their character with emotions. This paper points out what the OCC model is able to do for an embodied emotional character and what it does not. Missing features include a history function, a personality designer and the interaction of the emotional categories.

There are many approaches to the study of mind, and much ambiguity in the use of words like `emotion' and `consciousness'. This paper adopts the design stance and attempts to understand human minds as information processing virtual machines with a complex multi-level architecture whose components evolved at different times and perform different sorts of functions. A multi-disciplinary perspective combining ideas from engineering as well as several sciences helps to constrain the proposed architecture. Variations in the architecture should accommodate infants and adults, normal and pathological cases, and also animals. An analysis of states and processes that each architecture supports provides a new framework for systematically generating concepts of various kinds of mental phenomena. This framework can be used to refine and extend familiar concepts of mind, providing a new, richer, more precise theory-based collection of concepts. Within this unifying framework we hope to explain the ...

Introduction Affective states (such as emotions, motivations, desires, pleasures, pains, attitudes, preferences, moods, values, etc.) and their relations to agent architectures have been receiving increasing attention in AI and Cognitive Science. Detailed analyses of these subspecies of affect should include descriptions of their functional roles in contributing to useful capabilities within agent architectures , complemented by empirical research on affect in biological organisms and concrete experiments with synthetic agent architectures, to confirm that the proposed architectures have the claimed properties. Our approach contrasts with most evolutionary AI research, which attempts to discover what can evolve from given inital states. Instead, we explore "neighbourhoods" and "mini-trajectories" in design space, by starting with examples of agent architectures, then explicitly provide possible extensions with evolutionary operators that can se

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