Faikenhainer, B. and K.D. Forbus, Compositional modeling: finding the right model for the job, Artificial Intelligence 51 ( 1991 ) 95-143.

Abstract not found

Abstract not found

Abstract: One goal of qualitative physics is to capture the mental models of engineers and scientists. This paper shows how Qualitative Process theory can be used to express concepts of engineering thermodynamics. This encoding provides the means to integrate qualitative and quantitative knowledge for solving textbook thermodynamics problems. These ideas have been implemented in a program called SCHISM, which analyzes thermodynamic cycles, such as gas turbine plants and steam power plants. We describe its analysis of a sample textbook problem and discuss our plans for future work. 1

This paper describes a qualitative domain theory for core phenomena in engineering thermodynamics, expressed in Qualitative Process theory. It represents many of the best features of domain models developed by our group over the past five years. It focuses on supporting system-level qualitative analyses of typical fluid and thermal systems, such as refrigerators and power plants. We use explicit modeling assumptions [3] to control the level of detail used in building models of specific scenarios. We begin by outlining the primitives of the specific QP modeling language. The bulk of the paper describes the domain model itself, highlighting our design choices, simplifications, and use of modeling assumptions. Next we demonstrate how this domain model can be used to build models of a variety of specific scenarios, including simplified versions of a refrigerator, a steam plant, and a thermal control system. Finally, we describe some planned extensions to the model. Contents 1 Introducti...

Understanding people's commonsense knowledge about physical world is a fundamental problem in building intelligent systems. If this knowledge can be represented and used by computers, they can duplicate people's ability to understand and interact with the world. Qualitative physics is the attempt to capture and formalize this knowledge. An important aspect of qualitative reasoning is the ability to derive the possible behaviors of a given physical system from the structure of the system, using minimal initial information. This thesis investigates qualitative domain theories and reasoning techniques which will en-able computers to analyze the qualitative behaviors of physical systems which include both me-chanical mechanisms and fluids, such as internal combustion engines and hydraulic lift pumps. We have developed a domain theory which integrates richer models of mechanics, fluids, and geometry than previous research in qualitative physics. These theories and inference techniques are embodied in QSA, a program that produces possible behaviors of physical systems. iii To My Parents iv I would like to thank: ACKNOWLEDGMENTS • Professor Kenneth Forbus, my advisor, for initiating and directing this research, for stim-ulating me to think creatively and to stand on my own legs, and for giving me a great example of researcher.

The representation of physical systems using qualitative formalisms are examined in this review, with an emphasis on recent developments in the area. The push to develop reasoning systems incorporating deep knowledge originally focused on naive physical representations, but have now shifted to more formal ones based on qualitative mathematics. The qualitative differential constraint formalism used in systems like QSIM is examined, and current efforts to link this to competing representations like Qualitative Process Theory are noted. Inference and representation are intertwined, and the decision to represent notions like causality explicitly, or infer it from other properties has shifted as the field has developed. The evolution of causal and functional representations are thus examined. Finally, a growing body of work that allows reasoning systems to utilise multiple representations of a system is identified. Dimensions along which multiple model hierarchies could be constructed are e...

Abstract not found

Representing Physical and Design Knowledge in Innovative Design Nikitas Marinos Sgouros The ability to design is one of the hallmarks of intelligent behavior, since it allows an agent to shape its environment according to its needs. Therefore developing computational models of design should be one of the primary goals for a discipline such as Artificial Intelligence that aims to create intelligent artifacts. This thesis describes DIAS, a computational framework for innovative engineering design. This framework provides ways for representing the physical and design knowledge in a domain, along with methods for allowing these different types of knowledge to interact during the design process. This model has been instantiated in OUZO, a program that designs separation systems in chemical engineering. iii To my family iv ACKNOWLEDGEMENTS I would like to thank: ffl My advisor, Kenneth Forbus, for all the support and direction during this research. ffl The members of my committee, Lar...

This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplifie d problem, and generalization of the results obtained. Our emphasis in this work is to demonstrate th e effectiveness of this approach in addressing complexity and grain size issues that affect qualitative simulation. The thought experiment methodology is presented formally, the implementation of a problem solve r called TEPS is briefly discussed, and the methodology is compared with related techniques such as approximation, aggregation, and exaggeration. 1