Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Implementation and results of a prototype expert system on strategic analysis

Expertise and experience are key factors for experts in strategic analysis in order to give advice on strategic matters such as the strength or the competitive position of an enterprise. They are able to reason with uncertain or incomplete knowledge. Expert systems may be able to do the same if this heuristic knowledge can be modeled and processed properly. This article discusses the modeling, implementation and the results of such an expert on strategic analysis. The results are compared to those generated by Business Insight and further improvements are discussed

PROLOG META-INTERPRETERS FOR RULE-BASED INFERENCE UNDER UNCERTAINTY

Uncertain facts and inexact rules can be represented and processed in standard Prolog through meta-interpretation. This requires the specification of appropriate parsers and belief calculi. We present a meta-interpreter that takes a rule-based belief calculus as an external variable. The certainty-factors calculus and a heuristic Bayesian belief-update model are then implemented as stand-alone Prolog predicates. These, in turn, are bound to the meta-interpreter environment through second-order programming. The resulting system is a powerful experimental tool which enables inquiry into the impact of various designs of belief calculi on the external validity of expert systems. The paper also demonstrates the (well-known) role of Prolog meta-interpreters in building expert system shells.Information Systems Working Papers Serie

Application of expert systems in project management decision aiding

The feasibility of developing an expert systems-based project management decision aid to enhance the performance of NASA project managers was assessed. The research effort included extensive literature reviews in the areas of project management, project management decision aiding, expert systems technology, and human-computer interface engineering. Literature reviews were augmented by focused interviews with NASA managers. Time estimation for project scheduling was identified as the target activity for decision augmentation, and a design was developed for an Integrated NASA System for Intelligent Time Estimation (INSITE). The proposed INSITE design was judged feasible with a low level of risk. A partial proof-of-concept experiment was performed and was successful. Specific conclusions drawn from the research and analyses are included. The INSITE concept is potentially applicable in any management sphere, commercial or government, where time estimation is required for project scheduling. As project scheduling is a nearly universal management activity, the range of possibilities is considerable. The INSITE concept also holds potential for enhancing other management tasks, especially in areas such as cost estimation, where estimation-by-analogy is already a proven method

META-INTERPRETERS FOR RULE-BASED REASONING UNDER UNCERTAINTY

One of the key challenges in designing expert systems is a credible representation of uncertainty and partial belief. During the past decade, a number of rule-based belief languages were proposed and implemented in applied systems. Due to their quasi-probabilistic nature, the external validity of these languages is an open question. This paper discusses the theory of belief revision in expert systems through a canonical belief calculus model which is invariant across different languages. A meta-interpreter for non-categorical reasoning is then presented. The purposes of this logic model is twofold: first, it provides a clear and concise conceptualization of belief representation and propagation in rule-based systems. Second, it serves as a working shell which can be instantiated with different belief calculi. This enables experiments to investigate the net impact of alternative belief languages on the external validity of a fixed expert system.Information Systems Working Papers Serie

Modelling information usage and decision processes in new product introductions : an information-processing perspective.

The objective of this study is to understand the problem solving process used in new product introductions, and other unstructured business problems. I hope this understanding will contribute to improved decision support systems. Based on Cognitive psychology theories (in particular, Anderson, 1983, 1987), a set of propositions were outlined and investigated by using a computer model. One application of the expert system shell, used here, is to try to model the expert\u27s knowledge. The shell is used to develop a system that simulates the expert\u27s approach to problem solving. The differences between this application and expert system development, are: (i) the focus is on trying to understand the mind of the expert, instead of trying to replace him; and (ii) the problem area is ill-structured, instead of narrow and well-defined. The introduction of new products into markets is an example of an ill-structured problem, in a business setting. In particular, identifying opportunities is to create new products--their future growth and competitiveness often depends on this. The method adopted, computer simulation, has both advantages and limitations. The advantages include: (i) in-depth analysis of the problem-solving process; (ii) operationalizing the theory; and (iii) producing a program that can act as a research vehicle for future projects. The limitations are: (i) small sample size; (ii) lack of clear-cut validation procedures; and (iii) dependence on shell features. The findings, for the most part, supported the propositions (i) The expert model clearly had more procedural knowledge than the textbook model. This supports the proceduralization theory of skill acquisition. (ii) Reasoning by analogy was used by both expert and novices. The use of weak methods by the expert does not support the theory. (iii) The expert adopted a forward reasoning strategy within a task agenda. This supports the hierarchical goal structure theory of Anderson. (iv) The use of soft information was also observed

GenRule : Learning of Shortcut-Oriented Diagnostic Problem Solving in the MOLTKE 3 Workbench

GenRule is the offline processing component of the MOLTKE3 workbench’s learning mechanism. It learns from diagnostic cases, i.e. protocols of the diagnostic behavior of an experienced service technician. The result of a learning step are so called shortcut rules, which allow the derivation of symptom values from other already known values. Furthermore, these rules are used to direct the diagnostic strategy applied by the MOLTKE3 shell. The presented mechanism appears to be well suited for modeling the typical diagnostic behavior of a service technician