twelve years after

Abstract not found

Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, Forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. Forte uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including propositional theory refinement, first-order induction, and inverse resolution. Forte is demonstrated in several domains, including logic programming and qualitative modelling.

A central goal of qualitative physics is to provide a framework for organizing and using quantitative knowledge. One important use of quantitative knowledge is numerical simulation. While current numerical simulators are powerful, they are often hard to con struct, do not reveal the assumptions underlying their construction, and do not produce explanations of the behaviors they predict. This paper shows how to combine qualitative and quantitative models to produce a new class of self-explanatory simulations which combine the advantages of both kinds of reasoning. Self-explanatory simulations provide the accuracy of numerical models and the interpretive power of qualitative reasoning. We define what self-explanatory simulations are and show how to construct them automatically. We illustrate their power with some examples generated with an implemented system, SIMGEN. We analyze the limitations of our techniques, and discuss plans for future work.

: Design rationale is a record of design activity: of alternatives available, choices made, the reasons for them, and explanations of how a proposed design is intended to work. We describe a representation called the Functional Representation (FR) that has been used to represent how a device's functions arise causally from the functions of its components and their interconnections. We propose that FR can provide the basis for capturing the causal aspects of the design rationale. We briefly discuss the use of FR for a number of tasks in which we would expect the design rationale to be useful: generation of diagnostic knowledge, design verification and redesign. Keywords: Design rationale, functional representation, causal models, redesign, design problem solving, concurrent engineering. Functional Representation as Design Rationale 01/28/93 Design Rationale and Its Uses The design process involves exploration of design spaces, simulation and verification of candidate designs, possibl...

One of the original motivations for research in qualitative physics was the development of intelligent tutoring systems and learning environments for physical domains and complex systems. This article demonstrates how a synergistic combination of qualitative reasoning and other AI techniques can be used to create an intelligent learning environment for students learning to analyze and design thermodynamic cycles. Pedagogically this problem is important because thermodynamic cycles express the key properties of systems which interconvert work and heat, such as power plants, propulsion systems, refrigerators, and heat pumps, and the study of thermodynamic cycles occupies a major portion of an engineering student's training in thermodynamics. This article describes CyclePad, a fully implemented articulate virtual laboratory that captures a substantial fraction of the knowledge in an introductory thermodynamics textbook and provides explanations of calculations and coaching support for students who are learning the principles of such cycles. CyclePad employs a distributed coaching model, where a combination of on-board facilities and a server-based coach accessed via email provide help for students, using a combination of teleological and case-based reasoning. CyclePad is a fielded system, in routine use in classrooms scattered all over the world. We analyze the combination of ideas that made CyclePad possible and comment on some lessons learned about the utility of various AI techniques based on our experience in fielding CyclePad. 1999 Elsevier Science B.V. All rights reserved.

Abstract not found

The ability to answer prediction questions is crucial to reasoning about physical systems. A prediction question poses a hypothetical scenario and asks for the resulting behavior of variables of interest. Prediction questions can be answered by simulating a model of the scenario. An appropriate system boundary, which separates aspects of the scenario that must be modeled from those that can be ignored, is critical to achieving a simple yet adequate model. This paper presents an efficient algorithm for system boundary selection, it shows the important role played by the model's time scale, and it provides a separate algorithm for selecting this time scale. Both algorithms have been implemented in a compositional modeling program called tripel and evaluated in the plant physiology domain.

Abstract not found

Adequate problem representations require the identification of abstractions and approximations that are well suited to the task at hand. In this paper we introduce a new class of approximations, called causal approximations, that are commonly found in modeling the physical world. Causal approximations support the efficient generation of parsimonious causal explanations, which play an important role in reasoning about engineered devices. The central problem to be solved in generating parsimonious causal explanations is the identification of a simplest model that explains the phenomenon of interest. We formalize this problem and show that it is, in general, intractable. In this formalization, simplicity of models is based on the intuition that using more approximate models of fewer phenomena leads to simpler models. We then show that when all the approximations are causal approximations, the above problem can be solved in polynomial time.