Conclusions
In this work, we have presented a methodology for generating large and diverse agent populations for the purposes of social simulations. This methodology is using genetic operations to produce individuals with unique appearance and behaviour.
We have separated the methodology into six steps. First step is the definition of the base population, which specifies the visual traits of the whole population, although using mutation we may achieve novelty during generation. Second step is the definition of motivational modifiers, where motivation serves as the goal selection mechanism. In our case, we used physiological needs as the main motivation. Third step is the definition of personality traits, where personality affects agents decisions during planning and agents select actions that best match their profile. In the context of social simulations, agents belong to specific social, ethnic or cultural groups and have to obey specific social norms. Therefore, fourth step is the definition of the social system and norms, in our case using Electronic Institutions. The fifth step is the adaptation and annotation of the environment that reflects all actions specified in the electronic institution. Agents are using these annotation to automatically plan their actions and interact with the environment. Following these steps results generating a diverse agent population having a high degree of variety in their appearance and behaviour, while also demonstrating substantially high degree of complexity of actions being performed by the agents.We have illustrated the application of the methodology proposed in this paper to the development of two case studies. In the first case study virtual agents were used to enrich a historical reconstruction with simulation of everyday life of ancient Sumerians in the city of Uruk, 3000 B.C. In the second case study we have applied our methodology to building a cultural simulation of the Darug tribe in Australia around 1700 A.D. Due to the high degree of automation in the creation of large virtual agent groups that our methodology offers, in both case studies we were able to achieve significant time savings while maintaining a high degree of complexity of the resulting virtual agent behaviour.
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