Model Aggregation on Model-Based Approach using Prime Models - A Preliminary Report

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Citation: Rattikorn Hewett. (1991) Model Aggregation on Model-Based Approach using Prime Models - A Preliminary Report. In KSL-91-12, February,1991.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Rattikorn Hewett
title Model Aggregation on Model-Based Approach using Prime Models - A Preliminary Report
number KSL-91-12
institution Knowledge Systems, AI Laboratory
year 1991
month February
Bibtex more
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abstract Models are central in reasoning about physical systems. While many studies of model based reasoning in Artificial Intelligence have focused on fundamental reasoning techniques, only recently have attempts been made to study the principles of model construction. The tradeoff between quality and efficiency in reasoning produces one important issue in model development - how to build models that contain enough information (good approximation) for the reasoning and yet do not contain so much detail (good abstraction) that searching would become intractable. The problem becomes even more pronounced when dealing with large and complex systems. One common practice is to decompose systems into subdivisions where problem-solving can be broken down into smaller problems. However, decomposability alone is not enough. The organization of models must provide integration of various reasoning results to allow coherent reasoning about the overall system. Another approach to help us manage complex systems with a high computational cost is by reasoning with multiple appropriate models. This allows us to reason at multiple levels of abstraction. For example, different models of a device correspond to different abstractions of it. Each model is well suited to a particular class of uses - both in terms of the answers it can provide and in terms of efficiency of the inference. However, as the systems get larger and more complicated, complete instantiation becomes both undesirable and impossible.To handle this problem one needs to know what is relevant and what models to select. All of these topics are currently under research in model-based reasoning about large and complex systems.

KSL Technical Report ID: KSL-91-12
Facts about Model Aggregation on Model-Based Approach using Prime Models - A Preliminary ReportRDF feed
Abstract Models are central in reasoning about phys … Models are central in reasoning about physical systems. While many studies of model based reasoning in Artificial Intelligence have focused on fundamental reasoning techniques, only recently have attempts been made to study the principles of model construction. The tradeoff between quality and efficiency in reasoning produces one important issue in model development - how to build models that contain enough information (good approximation) for the reasoning and yet do not contain so much detail (good abstraction) that searching would become intractable. The problem becomes even more pronounced when dealing with large and complex systems. One common practice is to decompose systems into subdivisions where problem-solving can be broken down into smaller problems. However, decomposability alone is not enough. The organization of models must provide integration of various reasoning results to allow coherent reasoning about the overall system. Another approach to help us manage complex systems with a high computational cost is by reasoning with multiple appropriate models. This allows us to reason at multiple levels of abstraction. For example, different models of a device correspond to different abstractions of it. Each model is well suited to a particular class of uses - both in terms of the answers it can provide and in terms of efficiency of the inference. However, as the systems get larger and more complicated, complete instantiation becomes both undesirable and impossible.To handle this problem one needs to know what is relevant and what models to select. All of these topics are currently under research in model-based reasoning about large and complex systems. reasoning about large and complex systems.
Author Rattikorn Hewett  +
Bibtype techreport  +
Has author Rattikorn Hewett  +
Has identifier KSL-91-12  +
Has publishing details February,1991  +
Has title Model Aggregation on Model-Based Approach using Prime Models - A Preliminary Report  +
Has where published KSL-91-12  +
Has year 1991  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-91-12  +
Month February  +
Number KSL-91-12  +
Process note NO  +
Title Model Aggregation on Model-Based Approach using Prime Models - A Preliminary Report  +
Year 1991  +
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