Table-driven Rules in Expert Systems

The structure and organization of expert systems can be usefully modeled after corresponding human experts. Often this modeling degrades because of insufficient expressive power in production system languages. Relational table techniques provide additional abstraction capabilities and are useful in extending the expressiveness of production system rules; the resulting systems can be easier to build, understand and debug because they can reflect more accurately human methods of reasoning. The number of superfluous rules is reduced by organizing much of the problem domain knowledge in relations in working memory. The relational table methods also provide a tool for the interfacing of knowledge bases and databases

Circuit Minimization Techniques Applied to Knowledge Engineering

Often knowledge engineers encounter situations during the interviewing process in which experts have difficulty expressing the knowledge to be captured. In these situations, the experts cannot readily present their knowledge so that the knowledge engineers can encode it in the chosen formalism (for example, in production rules). During the development of an expert system for underwriting homeowner insurance policies, this situation was occasionally encountered. When the experts could not express their knowledge in chunks suitable for encoding directly in production rules, circuit minimization techniques were used to construct the set of production rules from exhaustive tables of acquired knowledge. The techniques also served to find errors in the acquired knowledge. Circuit minimization techniques, therefore, have been found to provide valuable assistance in the knowledge engineering process, both in the acquisition and verification of knowledge

Improving Production System Performance on Parallel Architectures by Creating Constrained Copies of Rules

Production systems have pessimistically been hypothesized to contain only minimal amounts of parallelism [Gupta 1984]. However, techniques are being investigated to extract more parallelism from existing systems. Among these methods, it is desirable to find those which balance the work being performed in parallel evenly among the rules, while at the same time decrease the amount of work which must be performed sequentially in each cycle. The technique of creating constrained copies of culprit rules accomplishes both of the above goals. Production systems are plagued by occasional rules which slow down the entire execution. These rules require much more processing than others and thus cause other processors to idle while the culprit rules continue to match. By creating the constrained copies and distributing them to their own processors, each performs less work while others are busy, yielding increased parallelism, improved load balancing, and less work overall per cycle

A Methodology for Programming Production Systems and its Implications on Parallelism

Production systems have been studied as a language for artificial intelligence programming for over a decade. The flexibility of a programming paradigm which allows for loosely structured, independent rules to represent knowledge is attractive. Unfortunately, two seemingly independent phenomena have hindered the ability to take full advantage of production systems. First, the performance of large production systems suffers due to the large amounts of computation required to run them. Second, the programming styles of individuals primarily accustomed to conventional programming has adversely affected the maintainability and performance of the resulting systems. The parallel execution of production systems has been studied in order to address the performance issues. Preliminary results have been interpreted pessimistically; production systems have been observed to contain only moderate to low levels of parallelism. By investigating the issue of programming style, however, it will be shown that the apparent lack of large-scale or massive parallelism is an artifact of this problem. Indeed, a set of programming guidelines and tools will be presented which yield more maintainable, understandable, and parallelizable production systems. Is there a programming methodology or environment which will allow for the development of more maintainable and parallelizable production systems? This work will attempt to demonstrate that using a combination of several techniques, resulting production systems will more appropriately conform with the theory which supports their use. Production systems are not appropriate for encoding all problem solving tasks. They are appropriate when there is a clear separation of explicit control knowledge, tabular knowledge, and pattern-directed knowledge. This classification has been presented by many researchers in the field, often in order to advocate their separation. The issue has been addressed from a knowledge representation standpoint: here it will be one of several issues which, when addressed properly, will result in systems with improved performance in addition to their more adequate representation of the knowledge. Substantially more paral1elism can be extracted from these systems. In this regard, the techniques complement parallel match algorithms which provide the first step in the solution for mapping production systems onto parallel architectures. The techniques are table-driven rules, creating constrained copies of culprit rules, multiple rule firing, and combining rule chains. These methods are combined into a new way of viewing production system execution. Rather than assuming the sequentiality of production systems and trying to extract parallelism explicitly, the systems are assumed to be implicitly parallel and all necessarily sequential aspects are explicitly defined

An Expert Systems Approach to Realtime, Active Management of a Target Resource

The application of expert systems techniques to process control domains represents a potential approach to managing the increasing complexity and dynamics which characterizes many process control environments. This thesis reports on one such application in a complex, multi-agent environment, with an eye toward generalization to other process control domains. The application concerns the automation of large computing system operation. The requirement for high availability, high performance, computing systems has created a demand for fast, consistent, expert quality response to operational problems, and effective, flexible automation of computer operations would satisfy this demand while improving the productivity of operations. However, like many process control environments, the computer operations environment is characterized by high complexity and frequent change, rendering it difficult to automate operations in traditional procedural software. These are among the characteristics which motivate an expert systems approach to automation. JESQ, the focus of this thesis, is a realtime expert system which continuously monitors the level of operating system queue space in a large computing system and takes corrective action as queue space diminishes. JESQ is one of several expert systems which comprise a system called Yorktown Expert System/MVS Manager (YES/MVS). YES/MVS automates many tasks in the domain of computer operations, and is among the first expert systems designed for continuous execution in realtime. The expert system is currently running at the IBM Thomas J. Watson Research Center, and has received a favorable response from operations staff. The thesis concentrates on several related issues. The requirements which distinguish continuous realtime expert systems that exert active control over their environments from more conventional session-oriented expert systems are identified, and strategies for meeting these requirements are described. An alternative methodology for managing large computing installations is presented. The problems of developing and testing a realtime expert system in an industrial environment are described