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.
"Merrick's book helps game designers, programmers and researchers to create Non-Player Characters (NPCs, i.e. agents) that have human like motives. ? Throughout the book the reader will find a wealth of modelling approaches and scenarios that can be used directly and applied to real computer games. ? Concluding, Computational Models of Motivation for Game-Playing Agents will make a nice addition to the bookshelf of game designers, undergraduate students on computer game development courses and professional game researchers." (Spyridon Samothrakis, Genetic Programming and Evolvable Machines, Vol. 19 (4), December 2018)
"This is an interesting book on an interesting topic. It would be of interest to many in the field of AI, particularly those in multiagent systems and game playing." (Computing Reviews, June, 2017)
