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2,006
Steering Behaviors For Autonomous Characters
, 1999
"... This paper presents solutions for one requirement of autonomous characters in animation and games: the ability to navigate around their world in a life-like and improvisational manner. These "steering behaviors" are largely independent of the particulars of the character's means of lo ..."
Abstract
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Cited by 325 (1 self)
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of locomotion. Combinations of steering behaviors can be used to achieve higher level goals (For example: get from here to there while avoiding obstacles, follow this corridor, join that group of characters...) This paper divides motion behavior into three levels. It will focus on the middle level of steering
Simple statistical gradient-following algorithms for connectionist reinforcement learning
- Machine Learning
, 1992
"... Abstract. This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinfor ..."
Abstract
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Cited by 449 (0 self)
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reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reinforcement tasks, and they do this without explicitly computing gradient estimates or even storing information from which such estimates could be computed. Specific examples of such algorithms are presented, some
Behavior-Based Control: Examples from Navigation, Learning, and Group Behavior
- Journal of Experimental and Theoretical Artificial Intelligence
, 1997
"... This paper describes the main properties of behavior-based approaches to control. Different approaches to designing and using behaviors as basic units for control, representation, and learning are illustrated on three empirical examples of robots performing navigation and path-finding, group behavio ..."
Abstract
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Cited by 224 (38 self)
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This paper describes the main properties of behavior-based approaches to control. Different approaches to designing and using behaviors as basic units for control, representation, and learning are illustrated on three empirical examples of robots performing navigation and path-finding, group
Voice puppetry
, 1999
"... Frames from a voice-driven animation, computed from a single baby picture and an adult model of facial control. Note the changes in upper facial expression. See figures 5, 6 and 7 for more examples of predicted mouth shapes. We introduce a method for predicting a control signal from another related ..."
Abstract
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Cited by 298 (0 self)
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signal, and apply it to voice puppetry: Generating full facial animation from expressive information in an audio track. The voice puppet learns a facial control model from computer vision of real facial behavior, automatically incorporating vocal and facial dynamics such as co-articulation. Animation
Developments in the Measurement of Subjective Well-Being
- Psychological Science.
, 1993
"... F or good reasons, economists have had a long-standing preference for studying peoples' revealed preferences; that is, looking at individuals' actual choices and decisions rather than their stated intentions or subjective reports of likes and dislikes. Yet people often make choices that b ..."
Abstract
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Cited by 284 (7 self)
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that bear a mixed relationship to their own happiness. A large literature from behavioral economics and psychology finds that people often make inconsistent choices, fail to learn from experience, exhibit reluctance to trade, base their own satisfaction on how their situation compares with the satisfaction
Efficient Distribution-free Learning of Probabilistic Concepts
- Journal of Computer and System Sciences
, 1993
"... In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior---thus, the same input may sometimes be classified as a positive example and sometimes as a negative example. Such probabilistic c ..."
Abstract
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Cited by 214 (8 self)
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In this paper we investigate a new formal model of machine learning in which the concept (boolean function) to be learned may exhibit uncertain or probabilistic behavior---thus, the same input may sometimes be classified as a positive example and sometimes as a negative example. Such probabilistic
Rules and Exemplars in Category Learning
- Journal of Experimental Psychology: General
, 1998
"... haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows fro ..."
Abstract
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Cited by 203 (11 self)
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from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection
- In Proceedings of the Fourteenth International Conference on Machine Learning
, 1997
"... Adding examples of the majority class to the training set can have a detrimental effect on the learner's behavior: noisy or otherwise unreliable examples from the majority class can overwhelm the minority class. The paper discusses criteria to evaluate the utility of classifiers induced f ..."
Abstract
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Cited by 234 (1 self)
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from such imbalanced training sets, gives explanation of the poor behavior of some learners under these circumstances, and suggests as a solution a simple technique called one-sided selection of examples. 1 Introduction The general topic of this paper is learning from examples described by pairs
(Guest Editors) Abstract Crowds by Example
"... We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world e ..."
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We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real
Behavioral theories and the neurophysiology of reward,
- Annu. Rev. Psychol.
, 2006
"... ■ Abstract The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can ..."
Abstract
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Cited by 187 (0 self)
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are two prominent examples of such behavioral theories and constitute the basis for this review. REWARD FUNCTIONS DEFINED BY ANIMAL LEARNING THEORY This section will combine some of the central tenets of animal learning theories in an attempt to define a coherent framework for the investigation of neural
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