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Computational models of motivation for game playing agents

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational models of motivation for game playing agents/ by Kathryn E. Merrick.
作者:	Merrick, Kathryn E.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xvii, 213 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Intelligent agents (Computer software) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-33459-2
ISBN:	9783319334592

Computational models of motivation for game playing agents
Merrick, Kathryn E.

Computational models of motivation for game playing agents[electronic resource] /by Kathryn E. Merrick. - Cham :Springer International Publishing :2016. - xvii, 213 p. :ill., digital ;24 cm.

From Player Types to Motivation -- Computational Models of Achievement, Affiliation, and Power Motivation -- Game Playing Agents and Non-player Characters -- Achievement Motivation -- Profiles of Achievement, Affiliation, and Power Motivation -- Enemies -- Pets and Partner Characters -- Support Characters -- Evolution of Motivated Agents -- Conclusion and Future Work.

The focus of this book is on three influential cognitive motives: achievement, affiliation, and power motivation. Incentive-based theories of achievement, affiliation and power motivation are the basis for competence-seeking behaviour, relationship-building, leadership, and resource-controlling behaviour in humans. In this book we show how these motives can be modelled and embedded in artificial agents to achieve behavioural diversity. Theoretical issues are addressed for representing and embedding computational models of motivation in rule-based agents, learning agents, crowds and evolution of motivated agents. Practical issues are addressed for defining games, mini-games or in-game scenarios for virtual worlds in which computer-controlled, motivated agents can participate alongside human players. The book is structured into four parts: game playing in virtual worlds by humans and agents; comparing human and artificial motives; game scenarios for motivated agents; and evolution and the future of motivated game-playing agents. It will provide game programmers, and those with an interest in artificial intelligence, with the knowledge required to develop diverse, believable game-playing agents for virtual worlds.

ISBN: 9783319334592

Standard No.: 10.1007/978-3-319-33459-2doiSubjects--Topical Terms:

558701
Intelligent agents (Computer software)

LC Class. No.: QA76.76.I58

Dewey Class. No.: 006.3

Computational models of motivation for game playing agents
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505 0 $a From Player Types to Motivation -- Computational Models of Achievement, Affiliation, and Power Motivation -- Game Playing Agents and Non-player Characters -- Achievement Motivation -- Profiles of Achievement, Affiliation, and Power Motivation -- Enemies -- Pets and Partner Characters -- Support Characters -- Evolution of Motivated Agents -- Conclusion and Future Work.
520 $a The focus of this book is on three influential cognitive motives: achievement, affiliation, and power motivation. Incentive-based theories of achievement, affiliation and power motivation are the basis for competence-seeking behaviour, relationship-building, leadership, and resource-controlling behaviour in humans. In this book we show how these motives can be modelled and embedded in artificial agents to achieve behavioural diversity. Theoretical issues are addressed for representing and embedding computational models of motivation in rule-based agents, learning agents, crowds and evolution of motivated agents. Practical issues are addressed for defining games, mini-games or in-game scenarios for virtual worlds in which computer-controlled, motivated agents can participate alongside human players. The book is structured into four parts: game playing in virtual worlds by humans and agents; comparing human and artificial motives; game scenarios for motivated agents; and evolution and the future of motivated game-playing agents. It will provide game programmers, and those with an interest in artificial intelligence, with the knowledge required to develop diverse, believable game-playing agents for virtual worlds.
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