Rule-Based Expert Systems

Fiabilitate şi Durabilitate, 2018

Expert systems, as a component of Artificial Intelligence. are used on an increasingly wider scale to solve certain problems in various fields. In this paper, we are describing the structure of an expert system used to design mechanisms.

Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics, 1989

This paper is concerned with the development o f an expert system which determines the best grasp configuration t o pick up a rectangular box. This two level system first chooses from a predetermined set of grasps, the ones best suited for the task using a set of rules. and then computes a quality index for each possible configuration. The program is written in LISP and was tested for many different objects and situations. The grasps found are not necessarily optimal but are efficient since they are similar t o the grasps a human would choose in the same situation. Because the program gives fast results and doesn't perform tedious computations, the best grip found can easily be used as a sub-optimal solution, or as a starting point for an optimizing program.

Applied Intelligence, 1992

In most expert systems for constructional tasks, the knowledge base consists of a set of facts or object definitions and a set of rules. These rules contain knowledge about correct or ideal solutions as well as knowledge on how to control the construction process. In this paper, we present an approach that avoids this type of rules and thus the disadvantages caused by them. We propose a static knowledge base consisting of a set of object definitions interconnected by is-a and part-of links. This conceptual hierarchy declaratively defines a taxonomy of domain objects and the aggregation of components to composite objects. Thus, the conceptual hierarchy describes the set of all admissible solutions to a constructional problem. Interdependencies between objects are represented by constraints. A solution is a syntactically complete and correct instantiation of the conceptual hierarchy. No control knowledge is included in the conceptual hierarchy. Instead, the control mechanism will use the conceptual hierarchy as a guideline. Thus it is possible to determine in which respects a current partial solution is incomplete simply by syntactical comparison with the conceptual hierarchy. The control architecture proposed here has the following characteristics: separation of control and object knowledge, declarative representation of control knowledge, and explicit control decisions in the problem solving process. Thus, a flexible control mechanism can be realized that supports interactive construction, integration of case-based approaches and simulation methods. This control method is part of an expert system kernel for planning and configuration tasks in technical domains. This kernel has been developed at the University of Hamburg and is currently applied to several domains.

Advances in Robot Manipulators, 2010

Computational Intelligence, 1987

We study the effect of adding a rule to a rule-based heuristic classification expert system, in particular, a mle that causes an unforeseen interaction with rules already in the rule set. We show that it is possible for such an interaction to occur between sets of rules, even when no interaction is present between any pair of rules contained in these sets. A method is presented that identifies interactions between sets of rules, and an analysis is given which relates these interactions to rule-based programming practices which help to maintain the integrity of the knowledge base. We argue that the methe is practical, given some reasonable assumptions on the knowledge base.

IEEE Expert / IEEE Intelligent Systems, 1986

1997

Engineering with computers, 1985

This paper provides an overview of the burgeoning new field of expert (knowledge-based) systems. This survey is tutorial in nature, intended to convey the gestalt of such systems to engineers who are newly exposed to the field. The discussion includes definitions, basic concepts, expert system architecture, descriptions of some of the programming tools and environments with which knowledge-based systems can be built, and approaches to knowledge acquisition. Some currently extant expert systems are described en passant, including a few developed for engineering purposes. Comments follow on the engineering of knowledge, as both cultural and social processes. The paper closes with an assessment of the roles that expert systems can play in engineering analysis, design, planning, and education.

2018

A rule-based kernel system to support reasoning within the blackboard problem architecture has been developed, with enough generality to support a number of expert system projects. The blackboard allows diverse expert-system modules to cooperate in solving complex design problems, making use of network communication to reduce difficulties with combining programs in various languages and operating systems. Features of the internal structure of the kernel allow it to be adapted readily to new problem areas and to new configurations of expert modules. From our experience we derive some design principles for enabling multi-expert cooperation, and for using expert knowledge effectively.