PLANET: An Intelligent Decision Support System for Resource Planning in Manufacturing Organizations

This paper describes a problem solver called PLANET that has been developed in collaboration with a large computer manufacturing company to assist planning managers with the formulation and maintenance of planning models for resource allocation. PLANET is equipped with the primitives that enable it to preserve much of the richness of the process of the planning activity, namely, the generation of symbolic alternatives, and for the expression of domain specific knowledge which enables it to synthesize these alternatives into an overall planning model. This knowledge is maintained in a âmeta-model.â In contrast to modeling systems which allow for parametric perturbations of an algebraic model, PLANET's meta-model provides it with the capability for systematic variations in the symbolic model assumptions, with concomitant structural variations induced in the algebraic model that reflect the interdependencies of those assumptions. Whenever previously held assumptions change, PLANET uses the existing model as a point of departure in formulating the revised plan. In this way, the program is able to take cognizance of the ongoing nature of organizational problem solving, and can serve an important decision support function in maintaining and reasoning about evolving plans.Information Systems Working Papers Serie

RULE-BASED VERSUS STRUCTURE-BASED MODELS FOR EXPLAINING AND GENERATING EXPERT BEHAVIOR

Flexible representations are required in order to understand and generate expert behavior. While production rules with quantifiers can encode experiential knowledge, they often have assumptions implicit in them, making them brittle in problem scenarios where these assumptions do not hold. Qualitative models achieve flexibility by representing the domain entities and their interrelationships explicitly. However, in problem domains where assumptions underlying such models change periodically, it is necessary to be able to synthesize and maintain qualitative models in response to the changing assumptions. In this paper, we argue for a representation that contains partial model components that are synthesized into qualitative models containing entities and relationships relevant to the domain. The model components can be replaced and rearranged in response to changes in the task environment. We have found this "model constructor" to be useful in synthesizing models that explain and generate expert behavior, and have explored its ability to support decision-making in the problem domain of business resource planning, where reasoning is based on models that evolve in response to changing external conditions or internal policies.Information Systems Working Papers Serie

Rule-based versus structurebased models for explaining and generating expert behavior

This paper appears in the Com~~zzlnications of the ACM, September 1987. We would like to acknowledge htfarilyn Stelzner and anonymous referees for their comments and suggestions which have contributed significantly in sharpening the presentation of this paper