Charles L. Forgy

Abstract: The Rete Match Algorithm is an efficient method for comparing a large collection of patterns to a large collection of objects. It finds all the objects that match each pattern. The algorithm was developed for use in production system interpreters, and it has been used for systems containing from a few hundred to more than a thousand patterns and objects. This article presents the algorithm in detail. It explains the basic concepts of the algorithm, it describes pattern and object representations that are appropriate for the algorithm, and it describes the operations performed by the pattern matcher.

Abstract: The Rete Match Algorithm is an efficient method for companng a large collection of patterns to a large collection of objects. It finds all the objects that match each pattern The algorithm was developed for use in production system interpreters, and it has been used for systems containing from a few hundred to more than a thousand patterns and objects. This article presents the algorithm in detail It explains the basic concepts of the algorithm, it describes pattern and object representations that are appropriate for the algorithm, and it describes the operations performed by the pattern matcher.

Abstract: : This is a combination introductory and reference manual for OPS5, a programming language for production systems. OPS5 is used primarily for applications in the areas of artificial intelligence, cognitive psychology, and expert systems. OPS5 interpreters have been implemented in LISP and BLISS.

Abstract: Rule-based systems, on the surface, appear to be capable of exploiting large amounts of parallelism—it is possible to match each rule to the data memory in parallel. In practice, however, we show that the speed-up from parallelism is quite limited, less than 10-fold. The reasons for the small speed-up are: (1) the small number of rules relevant to each change to data memory; (2) the large variation in the processing required by the relevant rules; and (3) the small number of changes made to data memory between synchronization steps. Furthermore, we observe that to obtain this limited factor of 10-fold speed-up, it is necessary to exploit parallelism at a very fine granularity. We propose that a suitable architecture to exploit such fine-grain parallelism is a bus-based shared-memory multiprocessor with 32-64 processors. Using such a multiprocessor (with individual processors working at 2 MIPS), it is possible to obtain execution speeds of about 3800 rule-firings/sec. This speed is significantly higher than that obtained by other proposed parallel implementations of rule-based systems.

Abstract: Rule-based systems are widely used in artificial intelligence for modeling intelligent behavior and building expert systems. Most rule-based programs, however, are extremely computation intensive and run quite slowly. The slow speed of execution has prohibited the use of rule-based systems in domains requiring high performance and real-time response. In this paper we explore various methods for speeding up the execution of rule-based systems. In particular, we examine the role of parallelism in the high-speed execution of rule-based systems and study the architectural issues in the design of computers for rule-based systems. Our results show that contrary to initial expectations, the speed-up that can be obtained from parallelism is quite limited, only about tenfold. The reasons for the small speed-up are: (1) the small number of rules relevant to each change to data memory; (2) the large variation in the processing requirements of relevant rules; and (3) the small number of changes made to data memory between synchronization steps. Furthermore, we observe that to obtain this limited factor of tenfold speed-up, it is necessary to exploit parallelism at a very fine granularity. We propose that a suitable architecture to exploit such fine-grain parallelism is a shared-memory multiprocessor with 32-64 processors. Using such a multiprocessor, it is possible to obtain execution speeds of about 3800 rule-firings/set. This speed is significantly higher than that obtained by other proposed parallel implementations of rule-based systems.

Abstract: A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, "Principles of Artificial Intelligence" describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. "Principles of Artificial Intelligence"evolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

Abstract: The Rete Match Algorithm is an efficient method for comparing a large collection of patterns to a large collection of objects. It finds all the objects that match each pattern. The algorithm was developed for use in production system interpreters, and it has been used for systems containing from a few hundred to more than a thousand patterns and objects. This article presents the algorithm in detail. It explains the basic concepts of the algorithm, it describes pattern and object representations that are appropriate for the algorithm, and it describes the operations performed by the pattern matcher.

Abstract: The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities.

Abstract: The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities.

Abstract: Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in a given partial order. We consider the problem of discovering frequently occurring episodes in a sequence. Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and present detailed experimental results. The methods are in use in telecommunication alarm management.