The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions. – Marvin Minsky, (Minsky, 1986) p. 163 In every art form it is the emotional content that makes the difference between mere technical skill and true art.

With the recent rapid growth of interest in MultiAgent Systems, both in artificial intelligence and software engineering, has come an associated difficulty concerning basic terms and concepts. In particular, the terms agency and autonomy are used with increasing frequency to denote different notions with different connotations. In this paper welay the foundations for a principled theory of agency and autonomy, and specify the relationship between them. Using the Z specification language, we describe a three-tiered hierarchy comprising objects, agents and autonomous agents where agents are viewed as objects with goals, and autonomous agents are agents with motivations.

This technical definition will only make sense toe reader by Ch. 4, once goals and management processes have been described. All that matters forrs section is that a difference between goals and perturbance be noted by the reader. Astate perturbance is not a goal, but it arises out of the processing of goals. In Ch. 7, arelation00 perturbance and "emotion" is discussed. 43 . Sloman says of certain moods that they are "persistent states with dispositional power to color and modify a host of other states and processes. Such moodscan39061-6 be caused by cognitive events with semantic content, though they need not be.[...]0-64000 their control function does not require specific semantic content, though theycan0371-62 cognitive processes that do involve semantic content." (Sloman, 1992b Section 6).A 39642 view is taken in (Oatley, 1992). To be more precise, moods are temporary control stateswhich9881-5 the prominence of some motivators while decreasing others. In particular, they affectthe 41330-5 that certain "goal generators" are triggered. Moreover, moods affect the valenceofce 39476 evaluations, and the likelihood of affective evaluations (perhaps by modifying thresholdsofsholds 42 that trigger evaluations). It is not yet clear whether moods as defined here are9531 - or whether they merely emerge as side-effects of functional processes. . A reflex is a ballistic form of behaviour that can be specified by a narrow setw rules based on input integration and a narrow amount of internal state. There aretwo0981 of reflexes: simple reflexes and fixed action patterns. A simple reflex involves oneaction,-43000 a fixed action pattern involves a collection of actions. Usually, at most only asmall-4120 of perceptual feedback influences reflex action. This would require a definit...

Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at a decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfields of psychology, they show that emotional reactions to risky situations often diverge from cognitive assessments of those risks. When such divergence occurs, emotional reactions often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive-consequentialist terms.

This is the manuscript that was published, with only minor copy-editing alterations, as: Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review. 108, 814-834 Copyright 2001, American Psychological Association To obtain a reprint of the final type-set article, please go through your library’s journal services, or contact the author directly Research on moral judgment has been dominated by rationalist models, in which moral judgment is thought to be caused by moral reasoning. Four reasons are given for considering the hypothesis that moral reasoning does not cause moral judgment; rather, moral reasoning is usually a post-hoc construction, generated after a judgment has been reached. The social intuitionist model is presented as an alternative to rationalist models. The model is a social model in that it de-emphasizes the private reasoning done by individuals, emphasizing instead the importance of social and cultural influences. The model is an intuitionist model in that it states that moral judgment is generally the result of quick, automatic evaluations (intuitions). The model is more consistent than rationalist models with recent findings in social, cultural, evolutionary, and biological psychology, as well as anthropology and primatology. Author notes

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

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.

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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...

Who knows what I want to do? Who knows what anyone wants to do? How can you be sure about something like that? Isn't it all a question of brain chemistry, signals going back and forth, electrical energy in the cortex? How do you know whether something is really what you want to do or just some kind of nerve impulse in the brain. Some minor little activity takes place somewhere in this unimportant place in one of the brain hemispheres and suddenly I want to go to Montana or I don't want to go to Montana. (White Noise, Don DeLillo)