The focus of this book is on three influentialcognitive motives: achievement, affiliation, and power motivation.Incentive-based theories of achievement, affiliation and power motivation arethe basis for competence-seeking behaviour, relationship-building, leadership,and resource-controlling behaviour in humans. In this book we show how thesemotives can be modelled and embedded in artificial agents to achievebehavioural diversity. Theoretical issues are addressed for representing andembedding computational models of motivation in rule-based agents, learningagents and evolutionary agents. Practical issues are addressed for defininggames, mini-games or in-game scenarios for virtual worlds in whichcomputer-controlled, motivated agents can participate alongside human players.
The book is structured into four parts: game playingin virtual worlds by humans and agents; comparing human and artificial motivesin rule-based agents; game scenarios for motivated learning agents; andevolution and the future of motivated game playing agents. It will provide gameprogrammers, and those with an interest in artificial intelligence, with theknowledge required to develop diverse, believable game playing agents forvirtual worlds.
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