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A.I. Techniques for Modelling Anger in Emotional AgentsThe research presented here, attempts to review a range of techniques commonly categorized under the umbrella of artificial intelligence (A.I.) that could be applied when developing agents with emotions in a range of applications. The paper focuses on anger (and its related emotions), an emotion strongly linked with aggression which of course forms the basis of many computer games where killing or attacking other players or in-game agents is often central to the game’s purpose. The paper begins with a psychology focused review of anger and its related emotions, before presenting techniques to encode some of these elements using Finite State Machines and Fuzzy Logic.
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Mood, self-set goals and examination performance: the moderating effect of depressed moodThe purpose of the present study was to investigate relationships between mood, performance goals, and examination performance. We tested the notion that feelings of depressed mood are central to the overall mood response and influence the functional impact of anger and tension on performance (see Lane & Terry, 2000). Fifty undergraduate students completed a measure of anger, confusion, depression, fatigue, tension and vigour approximately 10 min before a practical physiology examination. Participants also indicated the grade set as a goal for the examination, and rated their confidence to achieve this goal. Depressed mood data were analysed by dichotomising scores into depressed mood group (n = 23) or no-depressive symptoms group (n = 27). Analysis of covariance (ANCOVA) was adopted to explore the association between mood and performance and whether any differences exist between the depression and no-depression groups. Results indicated that only the anger-performance relationship differed between the depression and no-depression groups, whereby anger was associated with improved performance in the no-depression group. MANOVA results indicated that depressed mood was associated with a negative mood profile and low goal-confidence scores. Future research should investigate relationships between mood states using an ideographic design and explore links between variations in mood with more stable psychological factors such as emotional intelligence.