Modeling Behavior Cycles as a Value System for Developmental Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Virtual worlds as cultural models
ACM Transactions on Intelligent Systems and Technology (TIST)
Achievement, affiliation, and power: Motive profiles for artificial agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A computational model of achievement motivation for artificial agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Evolution of intrinsic motives in multi-agent simulations
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Curiosity: From psychology to computation
ACM Computing Surveys (CSUR)
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Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments the goal being to understand how machines can develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected changes in their environment. This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended virtual world. Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems in particular multiuser, online games.