By Kathryn E. Merrick
The concentration of this e-book is on 3 influential cognitive explanations: fulfillment, association, and gear motivation. Incentive-based theories of accomplishment, association and gear motivation are the foundation for competence-seeking behaviour, relationship-building, management, and resource-controlling behaviour in people. during this e-book we convey how those explanations might be modelled and embedded in synthetic brokers to accomplish behavioural variety. Theoretical matters are addressed for representing and embedding computational types of motivation in rule-based brokers, studying brokers, crowds and evolution of inspired brokers. useful matters are addressed for outlining video games, mini-games or in-game eventualities for digital worlds during which computer-controlled, inspired brokers can take part along human players.
The e-book is dependent into 4 components: online game taking part in in digital worlds via people and brokers; evaluating human and synthetic reasons; video game situations for stimulated brokers; and evolution and the way forward for influenced game-playing brokers. it's going to supply video game programmers, and people with an curiosity in man made intelligence, with the data required to enhance varied, plausible game-playing brokers for digital worlds.
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Extra resources for Computational Models of Motivation for Game-Playing Agents
1 Ways in which individuals may estimate probability of success at a goal Mastery-oriented estimates Performance-oriented estimates Self-based estimates from individual experience: how well an individual has performed on previous attempts at the goal Task-based estimates or absolute standards: for example, distance from a target in a shooting or throwing game (see Chap. 4) Norm-based estimates or social comparison standards: how many other people can solve the goal (see Chap. 2 Parameters of the achievement motivation model in Eq.
An imperial motive proﬁle of high power motivation, with low achievement and afﬁliation motivation might appear as shown in Fig. 10b. We use Eq. 9 to model motivation in the experiments in Chap. 5. 5 Parameters of motivation for an agent with a proﬁle of achievement, afﬁliation and power motivation as deﬁned in Eq. 2 Turning point of power avoidance þ qpow À qpow Gradient of power approach Spow Relative motivation strength for power motivation Gradient of power avoidance Modelling the Dominant Motive Only Alternatively, we can further simplify the calculation of motivation by considering only the curve for the dominant motive.
Pers. 41, 121–139 (1973) 25. J. McClelland, Power: The Inner Experience (Irvington, New York, 1975) 26. J. McClelland, R. E. Boyatzis, The leadership motive pattern and long term success in management. J. Appl. Psychol. 39 (1982) 27. D. McFarland, Animal Behaviour: Psychobiology, Ethology and Evolution (Pearson Education Limited, Harlow, England, 1999) 28. K. L. Maher, Motivated Reinforcement Learning: Curious Characters for Multiuser Games (Springer, Berlin, 2009) 29. K. Merrick, K. Shaﬁ, Achievement, afﬁliation and power: Motive proﬁles for artiﬁcial agents.