Acquisition, representation and rule generation for procedural knowledge

Current research into the design and continuing development of a system for the acquisition of procedural knowledge, its representation in useful forms, and proposed methods for automated C Language Integrated Production System (CLIPS) rule generation is discussed. The Task Analysis and Rule Generation Tool (TARGET) is intended to permit experts, individually or collectively, to visually describe and refine procedural tasks. The system is designed to represent the acquired knowledge in the form of graphical objects with the capacity for generating production rules in CLIPS. The generated rules can then be integrated into applications such as NASA's Intelligent Computer Aided Training (ICAT) architecture. Also described are proposed methods for use in translating the graphical and intermediate knowledge representations into CLIPS rules

On the acquisition and representation of procedural knowledge

Historically knowledge acquisition has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some of some types of knowledge, little attention has been devoted to procedural knowledge. NASA personnel frequently perform tasks that are primarily procedural in nature. Previous work is reviewed in the field of knowledge acquisition and then focus on knowledge acquisition for procedural tasks with special attention devoted to the Navy's VISTA tool. The design and development is described of a system for the acquisition and representation of procedural knowledge-TARGET (Task Analysis and Rule Generation Tool). TARGET is intended as a tool that permits experts to visually describe procedural tasks and as a common medium for knowledge refinement by the expert and knowledge engineer. The system is designed to represent the acquired knowledge in the form of production rules. Systems such as TARGET have the potential to profoundly reduce the time, difficulties, and costs of developing knowledge-based systems for the performance of procedural tasks

AN APPROACH TO DEPENDENCY DIRECTED BACKTRACKING USING DOMAIN SPECIFIC KNOWLEDGE

The idea of dependency directed backtracking proposed by Stallman and Sussman (1977) offers significant advantages over heuristic starch schemes with chronological backtracking which waste much effort by discarding many "good" choices when backtracking situations arise. However, we have found that existing non-chronological backtracking machinery is not suitable for certain types of problems, namely, those where choices do not follow logically from previous choices, but are based on a heuristic evaluation of a constrained set of alternatives. This is because a choice is not justified by a âset of supportâ (of previous choices), but because its advantages outweigh its drawbacks in comparison to its competitors. What is needed for these types of problems is a scheme where the advantages and disadvantages of choices are explicitly recorded during problem solving. Then, if an unacceptable situation arises, information about the nature of the unacceptability and the tradeoffs can be used to determine the most appropriate backtracking point. Further, this requires the problem solver to use its hindsight to preserve those "good" intervening choices that were made chronologically after the "bad" choice, and to resume its subsequent reasoning in fight of the modified set of constraints. In this paper, we describe a problem solver for non-chronological backtracking in situations involving tradeoffs. By endowing the backtracker with access to domain-specific knowledge, a highly contextual approach to reasoning in dependency directed backtracking situations can be achieved.Information Systems Working Papers Serie

DEPENDENCY DIRECTED BACKTRACKING IN GENERALIZED SATISFICING ASSIGNMENT PROBLEMS

Many authors have described search techniques for the satisficing assignment problem: the problem of finding an interpretation for a set of discrete variables that satisfies a given set of constraints. In this paper we present a formal specification of dependency directed backtracking as applied to this problem. We also generalize the satisficing assignment problem to include limited resource constraints that arise in operations research and industrial engineering. We discuss several new search heuristics that can be applied to this generalized problem, and give some empirical results on the performance of these heuristics.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

Plan Verification in a Programmer's Apprentice

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under the Office of Naval Research contract N00014-75-C-0643.Brief Statement of the Problem: An interactive programming environment called the Programmer's Apprentice is described. Intended for use by the expert programmer in the process of program design and maintenance, the apprentice will be capable of understanding, explaining and reasoning about the behavior of real-world LISP programs with side effects on complex data-structures. We view programs as engineered devices whose analysis must be carried out at many level of abstraction. This leads to a set of logical dependencies between modules which explains how and why modules interact to achieve an overall intention. Such a network of dependencies is a teleological structure which we call a plan; the process of elucidating such a plan stucture and showing that it is coherent and that it achieves its overall intended behavior we call plan verification. This approach to program verification is sharply contrasted with the traditional Floyd-Hoare systems which overly restrict themselves to surface features of the programming language. More similar in philosophy is the evolving methodology of languages like CLU or ALPHARD which stress conceptual layering.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

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

DEPENDENCY DIRECTED BACKTRACKING IN GENERALIZED SATISFICING ASSIGNMENT PROBLEMS

Many authors have described search techniques for the satisficing assignment problem: the problem of finding an interpretation for a set of discrete variables that satisfies a given set of constraints. In this paper we present a formal specification of dependency directed backtracking as applied to this problem. We also generalize the satisficing assignment problem to include limited resource constraints that arise in operations research and industrial engineering. We discuss several new search heuristics that can be applied to this generalized problem, and give some empirical results on the performance of these heuristics.Information Systems Working Papers Serie