By Surangika Ranathunga, Stephen Cranefield (auth.), Martin Beer, Cyril Brom, Frank Dignum, Von-Wun Soo (eds.)
This e-book is composed regularly of revised papers that have been awarded on the brokers for academic video games and Simulation (AEGS) workshop hung on may possibly 2, 2011, as a part of the self sufficient brokers and MultiAgent platforms (AAMAS) convention in Taipei, Taiwan. The 12 complete papers offered have been rigorously reviewed and chosen from a number of submissions. The papers are geared up topical sections on middleware functions, 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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Extra resources for Agents for Educational Games and Simulations: International Workshop, AEGS 2011, Taipei, Taiwan, May 2, 2011. Revised Papers
Abstraction from the game engine is achieved using an intermediate layer, hereafter called the GE Interface layer, connecting a speciﬁc game engine. Figure 3 illustrates the design focusing on the functional interfaces and data ﬂows. Fig. 3. Integration Middleware in Game Engine A prerequisite for using CIGA is the ability to modify the game engine allowing implementation of the GE Interface layer. This layer is responsible for integrating CIGA’s Physical Interface component as an external game engine component to be included in the engine’s update loop which allows it to run processes on its own.
LNCS, vol. 5773, pp. 418–424. Springer, Heidelberg (2009) 20. : Scalable Perception for BDI-Agents Embodied in Virtual Environments. In: Proceedings of the 2011 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (2011) 21. : Goal-Based Communication Using BDI Agents as Virtual Humans in Training: An Ontology Driven Dialogue System. In: Dignum, F. ) Agents for Games and Simulations II. LNCS, vol. 6525, pp. 38–52. Springer, Heidelberg (2011) 22. : An architecture for integrating plan-based behavior generation with interactive game environments.
Semantic data is used as sensory information which allows an agent to build a social model of the environment based on meaningful concepts. The Ontology Model can be accessed to retrieve the formal representation of those concepts. Creating semantic data from raw data at runtime is a process performed in the GE Interface layer. g. associating the ﬁre concept to a ﬁre class in the game engine). g. an object’s size property is calculated). This translation process is performed when agents sense environment entities.