| The problem addressed in this research is that of developing a method which integrates problem solving with learning from experience within an extensive model of different knowledge types. A unified framework is developed through an analysis of various types, aspects and roles of knowledge relevant for the kind of systems described above. The framework contains a knowledge representation platform and a generic model of problem solving. It further specifies a general reasoning approach that combines reasoning within a deep model with reasoning from heuristic rules and past cases. Finally, it provides a model of learning methods that retain concrete problem solving cases in a way that makes them useful for solving similar problems later. The framework emphasizes knowledge-intensive case-based reasoning and learning as the major paradigm. A comprehensive and thorough knowledge model is the basis for generation of goal related explanations that support the reasoning and learning processes. Reasoning from heuristic rules or from 'scratch' within the deeper model is regarded partly as supporting methods to the case-based reasoning, partly as methods to 'fall back on' if the case-based method fails. The purpose of the framework is to provide an environment for discussion of different approaches to knowledge intensive problem solving and learning.;CREEK specifies a structural and functional architecture based on an expressive, frame-based knowledge representation language, and an explicit model of control knowledge. It has a reasoning strategy which first attempts case-based reasoning, then rule-based reasoning, and, finally, model-based reasoning. The system interacts with the user during both problem solving and learning, e.g. by asking for confirmation or rejection of unexplained facts. The knowledge representation system, including an explicit model of basic representational constructs and basic inference methods, has been implemented. Otherwise, CREEK is an architectural specification--a system design. Its main characteristics are demonstrated by an example from the domain of diagnosis and treatment of oil well drilling fluid (mud). (Abstract shortened with permission of author.);Four systems focus on different methodological issues of knowledge intensive problem solving and learning. Each system represents interesting solutions to subproblems, but none of them provide a scope that is broad enough to represent the type of method requested for developing and maintaining complex applications in a practical, real world setting. |
