Download e-book for iPad: Artificial Intelligence and Simulation: 13th International by Bernard P. Zeigler, Rajanikanth Jammalamadaka, Salil R.

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

ISBN-10: 354024476X

ISBN-13: 9783540244769

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

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

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Additional info 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

Example text

To paraphrase the latter, it says that if some base system BS is not in BS, then T will make some wrong prediction, inference, etc. for it. , [Cohn 81]). It ensures the tightest possible fit between a theory and a class of base systems for which it is designed. Are there any known Galois correspondences about systems? The answer is yes. It requires familiarity with a kind of NML called circumscription to appreciate its significance. However we can give a paraphrase. The result is about a class of systems that have situations as described in section 4, and the kind of theory that is the tightest possible for them.

These agents are in Station spot at first and move into Main Entrance spot, then move some spot in the shopping mall. Each agent has four properties, implemented as keywords such as “condition”, “escape”, “shopping”, and “go”. The details of these keywords are shown as follows. The keyword “condition” means the condition of the agent. The keyword “condition” takes “alive” or “dead” as value. An initial value is “alive”. When an agent is in the spot whose keyword “fire” is “on+” or “on-“, agent’s keyword “condition” will change from “alive” to “dead” at a certain probability.

M od(T ∪ {α}) ⊆ M od(T ). , M1 is just a set of input-output trajectories like BS whereas M2 has internal state structure. As a justifying condition α that formalizes the existence of states is added, M1 is eliminated from consideration for validation for the trivial reason that it is not a model of T ∪ {α} because it cannot interpret α. This is a useful way to think about what is formally entailed by moving up hierarchies, that is really about successive tightening of the criteria to remain a satisfying model.

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


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