Download e-book for iPad: Agents for Educational Games and Simulations: International by Surangika Ranathunga, Stephen Cranefield (auth.), Martin

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

ISBN-10: 3642323251

ISBN-13: 9783642323256

This e-book is composed ordinarily of revised papers that have been awarded on the brokers for tutorial video games and Simulation (AEGS) workshop hung on may perhaps 2, 2011, as a part of the self sustaining brokers and MultiAgent structures (AAMAS) convention in Taipei, Taiwan. The 12 complete papers provided have been rigorously reviewed and chosen from numerous submissions. The papers are geared up topical sections on middleware purposes, dialogues and studying, adaption and convergence, and agent applications.

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

Sample text

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.

Similarities between CIGA and HLA are the use of ontologies as a design contract and the use of a subscription mechanism to control the flow of information sent between components. For CIGA, this is described in [20]. Next we’ll compare the system of Pogamut which has a goal similar to CIGA. Pogamut is designed as a mediation-layer between a game engine (GE) and a decision-making system (DMS) to bridge the ”representational gap” [10]. It is based on a general abstract framework for connecting a DMS to a GE [9].

The use of Affordance Theory has been previously explored in [6,4]. Related to affordances, information associated with smart objects can be stored [11,15]. A small example showing the possibilities of a simple domain ontology is illustrated in figure 2. Fig. 2. Domain Ontology Example The left side of the illustration shows a domain ontology consisting of several object classes and actions for human actors. The right side of the example shows a simple scene with two human agents (A and B), a fire and two objects which can be used to attack a fire.

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Agents for Educational Games and Simulations: International Workshop, AEGS 2011, Taipei, Taiwan, May 2, 2011. Revised Papers by Surangika Ranathunga, Stephen Cranefield (auth.), Martin Beer, Cyril Brom, Frank Dignum, Von-Wun Soo (eds.)


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