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- Daniel C. Dennett (1989). Cognitive Ethology. In Goals, No-Goals and Own Goals. Unwin Hyman.The field of Artificial Intelligence has produced so many new concepts--or at least vivid and more structured versions of old concepts--that it would be surprising if none of them turned out to be of value to students of animal behavior. Which will be most valuable? I will resist the temptation to engage in either prophecy or salesmanship; instead of attempting to answer the question: "How might Artificial Intelligence inform the study of animal behavior?" I will concentrate on the obverse: "How might the study of animal behavior inform research in Artificial Intelligence?".
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Few areas of scientific investigation have spawned more alternative approaches than animal behavior: comparative psychology, ethology, behavioral ecology, sociobiology, behavioral endocrinology, behavioral neuroscience, neuroethology, behavioral genetics, cognitive ethology, developmental psychobiology—the list goes on. Add in the behavioral sciences focused on the human animal, and you can continue the list with ethnography, biological anthropology, political science, sociology, psychology (cognitive, social, developmental, evolutionary, etc.), and even that dismal science, economics. Clearly, no reasonable-length chapter can do justice to such a varied collection. We have opted therefore to focus on three of these subdisciplines and to provide a somewhat historical tour of them, mentioning along the way the philosophical points that are of particular interest to us, but allowing the development of these points to be limited only by the imaginations of our readers. For readers seeking a more-traditional historical survey, see Dewsbury (1984a, b) and Burghardt (1985a). Our chosen brief is to write about comparative psychology, ethology, and cognitive ethology, although other approaches, especially neuroscience, will be mentioned where appropriate. These sciences are philosophically significant because they are enmeshed in ancient philosophical questions about the nature of mind and purposeful action and about the differences between humans and other animals. These sciences are also clustered because of their attention to mechanistic explanations of individual animal behavior as opposed to attempting to capture regularities at a population level, such as the game-theoretic strategic models popular among behavioral ecologists.
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In 1963 Niko Tinbergen published a paper, "On Aims and Methods of Ethology," dedicated to his friend Konrad Lorenz. Here Tinbergen defines ethology as "the biological study of behavior," and seeks to demonstrate "the close affinity between Ethology and the rest of Biology." Tinbergen identifies four major areas of ethology: causation, survival value, evolution, and ontogeny. Our goal is to attempt for cognitive ethology what Tinbergen succeeded in doing for ethology: to clarify its aims and methods, to distinguish some of its varieties, and to defend the fruitfulness of the research strategies that it has spawned.
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Artificial Intelligence has become big business in the military and in many industries. In spite of this growth there still remains no consensus about what AI really is. The major factor which seems to be responsible for this is the lack of agreement about the relationship between behavior and intelligence. In part certain ethical concerns generated from saying who, what and how intelligence is determined may be facilitating this lack of agreement.
Cognitive ethology cannot be done well unless its proximate philosophical underpinnings are got straight; this paper tries to help with that. Cognitive attributions are essentially explanatory—if they did not explain behavior, there would be no justification for them—but it doesn’t follow that they explain by providing causes for events that don’t have physical causes. To understand how mentalistic attributions do work, we need to focus on the quartet: sensory input, belief, desire, and behavioral output. We also need to be able to study classes of sensory inputs—one-shot deals are uninterpretable. The crucial guiding rule is, roughly: The animal’s behavior shouldn’t be explained by attributing to it the belief that P unless the behavior occurs in sensory circumstances belonging to a class whose members are marked off in some way that involves the concept of P and not in any way that is lower than that. The higher/lower distinction can be understood so that the guiding rule is helpful not only in deciding what thoughts to attribute to an animal but also in deciding whether to attribute any thoughts at all.
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This book brings together his essays on the philosphy of mind, artificial intelligence, and cognitive ethology that appeared in inaccessible journals from 1984...
Cognitive ethology began with Donald R. Griffin's 1976 publication of The Question of Animal Awareness. More recently mutual influences can be found between cognitive ethology and comparative, developmental, experimental and cognitive psychology and philosophy of science and of mind. Present scientific work emphasizes: 1) animal cognitive capacities including discrimination, categorization, spatial knowledge, predator/prey relations such as "injury feigning" by birds, deception and attribution of intention, 2) communication, both natural systems and artificial "language" and cognition projects undertaken with apes, birds, and sea mammals and 3) the possibility of animal consciousness. For the future, one hopes for developments in those areas, more field research, conceptual and methodological bridges to other disciplines, and philosophical work on the theoretical foundations of cognitive ethology and naturalizing intentionality.
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Cognitive ethology is the comparative study of animal cognition from an evolutionary perspective. As a sub-discipline of biology it shares interest in questions concerning the immediate causes and development of behavior. As a part of ethology it is also concerned with questions about the function and evolution of behavior. I examine some recent work in cognitive ethology, and I argue that the notions of mental content and representation are important to enable researchers to answer questions and state generalizations about the function and volution of behavior.
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