Download Artificial Intelligence and Simulation: 13th International by Bernard P. Zeigler, Rajanikanth Jammalamadaka, Salil R. PDF

By Bernard P. Zeigler, Rajanikanth Jammalamadaka, Salil R. Akerkar (auth.), Tag Gon Kim (eds.)

This booklet constitutes the refereed post-proceedings of the 13th overseas convention on AI, Simulation, and making plans in excessive Autonomy platforms, AIS 2004, held in Jeju Island, Korea in October 2004.

The seventy four revised complete papers offered including 2 invited keynote papers have been conscientiously reviewed and chosen from a hundred and seventy submissions; after the convention, the papers went via one other around of revision. The papers are prepared in topical sections on modeling and simulation methodologies, clever regulate, desktop and community safety, HLA and simulator interoperation, production, agent-based modeling, DEVS modeling and simulation, parallel and disbursed modeling and simulation, cellular machine networks, Web-based simulation and common platforms, modeling and simulation environments, AI and simulation, component-based modeling, watermarking and semantics, photographs, visualization and animation, and enterprise modeling.

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Extra resources for Artificial Intelligence and Simulation: 13th International Conference on AI, Simulation, Planning in High Autonomy Systems, AIS 2004, Jeju Island, Korea, October 4-6, 2004, Revised Selected Papers

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G. Kim, Theory of Modeling and Simulation :integrating discrete event and continuous complex dynamic systems, 2nd ed, Academic Press, San Diego, 2000. Unified Modeling for Singularly Perturbed Systems by Delta Operators: Pole Assignment Case Kyungtae Lee1, Kyu-Hong Shim2, and M. edu Abstract. A unified modeling method by using the δ-operators for the singularly perturbed systems is introduced. The unified model unifies the continuous and the discrete models. When compared with the discrete model, the unified model has an improved finite word-length characteristics and its δ-operator is handled conveniently like that of the continuous system.

If an agent is on the first floor, the keyword “go” is set to “up” and if on the second floor, the keyword “go” is set to “down”. 4 The Normal Movement Rule of the Agent In this subsection, we explain about the movement rule of an agent under the situation of no fire. The rule is decided according to the spot where an agent is. If an agent is in the Station spot, the agent moves to one Main Entrance spot of four at a probability of 25%. When the agent is in one Main Entrance spot, then he moves to the space spot which is in neighborhood.

In this paper however, we are interested in dynamic domains. There is a corresponding logic for such domains, and it is there that the interesting parallels with MaS formalisms occur. The AI literature for dynamic systems, like that for static systems, uses the vocabulary of classical logic with inferential rules enhanced with meta-rules that make the logic non-monotonic. A prototypical example is a series battery-driven circuit with two switches that can be either open or closed, and they power a light.

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