Artificial Intelligence and Simulation by Tag G. Kim

By Tag G. Kim

This booklet constitutes the refereed post-proceedings of the thirteenth 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 awarded including 2 invited keynote papers have been rigorously reviewed and chosen from one hundred 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 keep watch over, desktop and community defense, HLA and simulator interoperation, production, agent-based modeling, DEVS modeling and simulation, parallel and allotted modeling and simulation, cellular computing device networks, Web-based simulation and usual structures, modeling and simulation environments, AI and simulation, component-based modeling, watermarking and semantics, photos, visualization and animation, and enterprise modeling.

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A prototypical example is a series battery-driven circuit with two switches that can be either open or closed, and they power a light. The formalization of this simple setting can be done in language of propositional logic, with sw1 and sw2 to denote the switches and light to denote the light. Although they look like propositions and in the technical treatment in one level they behave like them, it is customary in NML to call them fluents. , the sequential actions of closing switches 1 and 2, then opening siwtch 1 is represented as where is an initial situation and the names of the actions are as expected.

0103j. 1423 that is smaller than That is, holds. 01 by the ZOH method. 0049]. 0070j. 9946±0100j. 0096j. 1431 that is smaller than That is, holds. 01. 4938]. 0042j. 1401 that is smaller than That is, holds. 01. 5]. 7020j. 0040j. 1344 that is smaller than That is, holds. 4 shows that the error bound between the exact and approximate solutions is satisfied for all the four kinds of models, and that the unified solutions using the have less error than the discrete solution. 4, the solution of the discrete-like unified model becomes the same as that of the continuous model as sampling interval approaches zero.

IFAC Symposium on Large Scale Systems, (1976) 321-326 11. : A Riccati equation for block diagonalization of ill-conditioned systems. IEEE Trans. on Automatic Control, 20 (1975) 812-814 12. : Singular perturbation methods in control analysis and design. Academic Press, Orlando, FL, (1986) 13. : Singular Perturbation Methodology in Control Systems. Peter Peregrinus, London, United Kingdom, (1988) 14. : Singular perturbation analysis of the closed-loop discrete optimal control system. Optimal Control Application & Methods, 5 (1984) 19-37 15.

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