By Surangika Ranathunga, Stephen Cranefield (auth.), Martin Beer, Cyril Brom, Frank Dignum, Von-Wun Soo (eds.)

This e-book is composed usually of revised papers that have been offered on the brokers for tutorial video games and Simulation (AEGS) workshop hung on may possibly 2, 2011, as a part of the self reliant brokers and MultiAgent platforms (AAMAS) convention in Taipei, Taiwan. The 12 complete papers offered have been conscientiously reviewed and chosen from numerous submissions. The papers are equipped topical sections on middleware functions, dialogues and studying, adaption and convergence, and agent applications.

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Extra resources for Agents for Educational Games and Simulations: International Workshop, AEGS 2011, Taipei, Taiwan, May 2, 2011. Revised Papers

Sample text

For the middleware as a whole to be able to interface with both technologies while matching the language of that technology results in an easy integration process and an efficient, tight connection. Last, the design introduces connection transparency since the game engine and MAS can run in different processes or distributed over different computers or platforms, depending on the used internal connection mechanism. Next we’ll first describe the role of ontologies within the middleware after which we’ll look at the individual components connecting to game engine and MAS respectively.

For both tasks, syntax highlighting was available for Java and a graphical editor for POSH plans (Fig. S1). Fig. 1. The course of the experiment Subjects were given 4 questionnaires in total during the exam (15 minutes each). There was a 30 minutes long break for a light lunch between the tasks. The course of the experiment is summarized in Fig. 1. Subjects were informed that the study will take about 8 hours in total in advance, but the structure and the exact content were revealed only during the study.

Filtering sensory information should not be performed in the game engine as this process is dependent on the agent’s mind in a MAS. On the other hand, delegating this process to the MAS is not ideal as the cost of communicating the unfiltered information can have a negative performance impact on the system as a whole. Additionally, MASs generally don’t provide standard facilities implementing perception filtering. CIGA tackles this problem by introducing a filtering mechanism located closely to an agent’s sensors in the game engine.

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