A parallel algorithm for real-time decision making: A rough set approach

We consider decision tables with the values of conditional attributes (conditions) measured by sensors. These sensors produce outputs after an unknown but finite number of time units. We construct an algorithm for computing a highly parallel program represented by a Petri net from a given decision table. The constructed net allows to identify objects in decision tables to an extent which makes appropriate decisions possible. The outputs from sensors are propagated through the net with maximal speed. This is done by an appropriate implementation of all rules true in a given decision table. Our approach is based on rough set theory (Pawlak, 1991). It also seems to have some significance for theoretical foundations of real-time systems.

[1]  Thomas G. Dietterich,et al.  Readings in Machine Learning , 1991 .

[2]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[3]  Ryszard S. Michalski,et al.  Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning , 1993 .

[4]  Marcel Schoppers.  Real-time knowledge-based control systems , 1991, CACM.

[5]  Morton Nadler,et al.  Pattern recognition engineering , 1993 .

[6]  Keith Edwards.  Real-time structured methods , 1993 .

[7]  P. Langley,et al.  Computational Models of Scientific Discovery and Theory Formation , 1990 .

[8]  Nancy Martin,et al.  Programming Expert Systems in OPS5 - An Introduction to Rule-Based Programming(1) , 1985, Int. CMG Conference.

[9]  Edmund H. Durfee,et al.  Approximate Processing in Real-Time Problem Solving , 1988, AI Mag..

[10]  B. Chandrasekaran,et al.  Real-time disturbance control , 1991, CACM.

[11]  David W. Payton,et al.  Intelligent real-time control of robotic vehicles , 1991, CACM.

[12]  Roman Słowiński,et al.  Intelligent Decision Support , 1992, Theory and Decision Library.

[13]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[14]  Yair Wand,et al.  An Automated Approach to Information Systems Decomposition , 1992, IEEE Trans. Software Eng..

[15]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[16]  Ingo Wegener,et al.  The complexity of Boolean functions , 1987 .

[17]  Keith Edwards,et al.  Real-Time Structured Methods: Systems Analysis , 1993 .

[18]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[19]  James L. Crowley,et al.  Navigation for an intelligent mobile robot , 1985, IEEE J. Robotics Autom..

[20]  Elaine Kant,et al.  Programming expert systems in OPS5 , 1985 .

[21]  Brahim Chaib-draa,et al.  Trends in distributed artificial intelligence , 1992, Artificial Intelligence Review.

[22]  Carl G. Looney,et al.  Fuzzy Petri nets for rule-based decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[23]  Michel Hack,et al.  Decidability Questions for Petri Nets , 1975, Outstanding Dissertations in the Computer Sciences.

[24]  John R. Anderson,et al.  Machine learning - an artificial intelligence approach , 1982, Symbolic computation.

[25]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[26]  Andrzej Skowron,et al.  A rough set approach to real-time state identification , 1993, Bull. EATCS.

[27]  Andrzej Skowron,et al.  Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables , 1994, ISMIS.

[28]  James Lyle Peterson,et al.  Petri net theory and the modeling of systems , 1981 .

[29]  Barbara Hayes Roth.  Architectural foundations for real-time performance in intelligent agents , 1990 .

[30]  Andrzej Skowron,et al.  A rough set approach to decision rules generation , 1993 .

[31]  Chris Richardson,et al.  Lisp systems in the 1990s , 1991, CACM.

[32]  Zdzislaw Pawlak.  Concurrent versus sequential - the rough sets perspective , 1992, Bull. EATCS.

[33]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .