Computational Models of Motivation for Game-Playing Agents by Kathryn E. Merrick

By Kathryn E. Merrick

The concentration of this ebook is on 3 influential cognitive reasons: success, association, and gear motivation. Incentive-based theories of feat, 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 exhibit how those explanations may be modelled and embedded in synthetic brokers to accomplish behavioural variety. Theoretical matters are addressed for representing and embedding computational versions of motivation in rule-based brokers, studying brokers, crowds and evolution of influenced brokers. Practical issues are addressed for outlining video games, mini-games or in-game situations for digital worlds during which computer-controlled, inspired brokers can take part along human players.

The booklet is dependent into 4 elements: online game taking part in in digital worlds by means of people and brokers; evaluating human and synthetic reasons; online game eventualities for influenced brokers; and evolution and the way forward for encouraged game-playing brokers. it's going to offer online game programmers, and people with an curiosity in synthetic intelligence, with the information required to improve diversified, plausible game-playing brokers for digital worlds. 

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This means that, if we assume we can create artificial agents that can obtain or calculate goal incentive I s ðGÞ, then we can create agents with an artificial motive profile. We do this in three ways. The first method, in Sect. 1, models profiles of three motives explicitly. The second method, in Sect. 2, models the dominant motive only. The third method, in Sect. 3, models only the incentive that maximises the dominant motive. 1 Modelling Profiles of Achievement, Affiliation and Power We model profiles of achievement, affiliation and power motivation by combining Eqs.

Individuals were then broken into four groups as follows: • H-L: high motivation to approach success and low motivation to avoid failure, • H-H: high motivation both to approach success and to avoid failure, • L-L: low motivation both to approach success and to avoid failure • L-H: low motivation to approach success and high motivation to avoid failure. In this study, Atkinson and Litwin [2] found that failure-motivated individuals do not select very easy or very difficult tasks to the extent predicted by their original 30 2 Computational Models of Achievement, Affiliation … þ Fig.

Some experimental evidence with ach biological motives for hunger satiation and pain avoidance [5] suggests the gradient of approach is often less than the gradient of avoidance. More recent work [8] connects approach-avoidance motivation more broadly to concepts of appetition, reward and incentive (approach) as well as aversion, punishment and threat (avoidance). In the case of achievement motivation, gradient of approach less than gradient þ of avoidance would imply qach \qÀ ach . Two examples of such scenarios are shown in Fig.

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