Artificial intelligence modelling of control systems

The field of control systems simulation needs artificial intelligence technology as much as expert systems need systems simulation tools. A functional approach for the design of Expert Systems that perform model generations and simulations is proposed. A differential games simulator design is chosen to exemplify the above ideas. The discrete-event approach, based on the geometry of the game is proposed. Results are comparable with the simu lation results obtained using the imperative approach, but the interrogative approach offers faster execution and clearer sim ula tor definition. Knowledge representation of the differential games models is described using Semantic Networks. The model genera tion methodology is a blend of several problem-solving para digms, and the hierarchical dynamic goal system construction serves as the basis for model generation. Prolog-based imple mentation of the system is suggested.

[1]  G. Leitmann,et al.  Optimal strategies in the neighborhood of a collision course , 1976 .

[2]  George Leitmann.  A differential game of pursuit and evasion , 1969 .

[3]  G. W. Stewart,et al.  Incorporating origin shifts into the QR algorithm for symmetric tridiagonal matrices , 1970, CACM.

[4]  G. Leitmann,et al.  Qualitative differential games with two targets , 1979 .

[5]  Touvia Miloh.  A note on Three-dimensional pursuit-evasion game with bounded curvature , 1982 .

[6]  Kristen Nygaard,et al.  SIMULA: an ALGOL-based simulation language , 1966, CACM.

[7]  James D. Hollan,et al.  STEAMER: An Interactive Inspectable Simulation-Based Training System , 1984, AI Mag..

[8]  Heimo H. Adelsberger,et al.  Expert systems and simulation , 1985 .

[9]  G. M. Anderson.  A near-optimal closed-loop solution method for nonsingular zero-sum differential games , 1974 .

[10]  Robert A. Kowalski,et al.  Logic and semantic networks , 1979, CACM.

[11]  Feliks Kluźniak,et al.  Prolog for Programmers , 1985 .

[12]  Ivan Bratko,et al.  Prolog Programming for Artificial Intelligence , 1986 .

[13]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[14]  Automated learning by tactical decision systems in air combat , 1989 .

[15]  George Leitmann,et al.  A note on avoidance control , 1983 .

[16]  Yuval Lirov,et al.  Artificial intelligence in air combat games , 1987 .

[17]  Josef Shinar,et al.  Pursit-evasion game analysis in a line of sight coordinate system , 1985 .

[18]  A. Merz.  The game of two identical cars , 1972 .

[19]  Bernard P. Zeigler.  Multifaceted modeling methodology: Grappling with the irreducible complexity of systems , 1984 .

[20]  Robert D. Brennan,et al.  A survey of Digital Simulation: Digital Analog Simulator Programs , 1964 .

[21]  Urban H. Lynch.  Differential Game Barriers and Their Application in Air-to-Air Combat , 1973 .

[22]  G. Leitmann,et al.  Avoidance control , 1977 .

[23]  Bernard P. Zeigler,et al.  Multifacetted Modelling and Discrete Event Simulation , 1984 .

[24]  Alfred V. Aho,et al.  The Design and Analysis of Computer Algorithms , 1974 .

[25]  John N. Buxton,et al.  Control and Simulation Language , 1962, Comput. J..

[26]  Geoffrey Gordon,et al.  A general purpose systems simulation program , 1899, AFIPS '61 (Eastern).