By Surangika Ranathunga, Stephen Cranefield (auth.), Martin Beer, Cyril Brom, Frank Dignum, Von-Wun Soo (eds.)
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.
Read or Download Agents for Educational Games and Simulations: International Workshop, AEGS 2011, Taipei, Taiwan, May 2, 2011. Revised Papers PDF
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Additional resources for Agents for Educational Games and Simulations: International Workshop, AEGS 2011, Taipei, Taiwan, May 2, 2011. Revised Papers
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 . 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” . It is based on a general abstract framework for connecting a DMS to a GE .
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.
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.)