Agents for Games and Simulations: Trends in Techniques, by Jakub Gemrot, Rudolf Kadlec, Michal Bída (auth.), Frank

By Jakub Gemrot, Rudolf Kadlec, Michal Bída (auth.), Frank Dignum, Jeff Bradshaw, Barry Silverman, Willem van Doesburg (eds.)

Research on multi-agent structures has supplied a promising expertise for imposing cognitive clever non-playing characters. despite the fact that, the applied sciences utilized in video game engines and multi-agent systems will not be quite simply suitable because of a few inherent modifications in matters. the place online game engines specialize in real-time elements and hence propagate potency and primary keep watch over, multi-agent structures think autonomy of the brokers. elevated autonomy and intelligence may possibly provide merits for a extra compelling gameplay and should also be valuable for severe video games. even though, difficulties ensue while present online game layout suggestions are used to include state of the art multi-agent approach know-how. a truly comparable argument might be given for agent-based (social) simulation.

This quantity comprises the papers provided at AGS 2009, the 1st foreign Workshop on brokers for video games and Simulations, held in Budapest on may well eleven, 2009. the point of interest of the workshop was once at the specific demanding situations dealing with these utilizing agent know-how for video games and simulations, with issues overlaying the technical, conceptual and layout features of the field.

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The bot can move forward, backward, it can turn, or shoot. Additionally, the Jazzbot is equiped with several sensors: GPS, sonar, 3D compass and an object recognition sensor. e. the bot can access only a subset of the perceptual information a human player would have available. The Jazzbot ’s belief base is composed of two modules: JzASP (cf. 1) and JzRuby (cf. 2). While the first one integrates an Answer Set Programming [2] (ASP) solver Smodels [18] and contains a logic program reflecting agent’s beliefs about itself, the environment, objects in it and other players, the second, based on an interpreted object oriented programming language Ruby, stores the map of the agent’s environment.

24 5 ˇ sl´ D. Siˇ ak et al. Implementation The distributed simulation architecture described in the previous section has been implemented on top of the AGlobe multi-agent platform [21] and is part of the the AgentFly [18] multi-agent system for air-traffic control and planning. AGlobe is a multi-agent middleware supporting agent encapsulation, effective agent-to-agent communication, high-throughput message passing, message topicking, yellow pages services, agent full migration, but also agent life-cycle management.

The effect of changing the hardware resources allocated to deliberative control parts while keeping the resources allocated to the rest of the system constant; the effect measured in terms of the total simulation run-time (left) and simulation run-time per normalized airplane (right) significant speed-up for the same reasons as in the previous case. The different property is for more hardware resources (four, five and six cores). In such a case, the simulation stays optimally loaded, the simulation run-time decreases and the normalized run-time remains constant.

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