Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering

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Citation: Patrice O. Gautier and Thomas R. Gruber. (1993) Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering. In KSL-93-06, 1993.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Patrice O. Gautier and Thomas R. Gruber
title Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering
number KSL-93-06
institution Knowledge Systems, AI Laboratory
address Washington D.C.
year 1993
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abstract Generating explanations of device behavior is a long-standing goal of AI research in reasoning about physical systems. Much of the relevant work has concentrated on new methods for modeling and simulation, such as qualitative physics, or on sophisticated natural language generation, in which the device models are specially crafted for explanatory purposes. We show how two techniques from the modeling research—compositional modeling and causal ordering—can be effectively combined to generate natural language explanations of device behavior from engineering models. The explanations offer three advances over the data displays produced by conventional simulation software: (1) causal interpretations of the data, (2) summaries at appropriate levels of abstraction (physical mechanisms and component operating modes), and (3) query-driven, natural language summaries. Furthermore, combining the compositional modeling and causal ordering techniques allows models that are more scalable and less brittle than models designed solely for explanation. However, these techniques produce models with detail that can be distracting in explanations and would be removed in hand-crafted models (e.g., intermediate variables). We present domain-independent filtering and aggregation techniques that overcome these problems.

KSL Technical Report ID: KSL-93-06
Facts about Generating Explanations of Device Behavior Using Compositional Modeling and Causal OrderingRDF feed
Abstract Generating explanations of device behavior … Generating explanations of device behavior is a long-standing goal of AI research in reasoning about physical systems. Much of the relevant work has concentrated on new methods for modeling and simulation, such as qualitative physics, or on sophisticated natural language generation, in which the device models are specially crafted for explanatory purposes. We show how two techniques from the modeling research—compositional modeling and causal ordering—can be effectively combined to generate natural language explanations of device behavior from engineering models. The explanations offer three advances over the data displays produced by conventional simulation software: (1) causal interpretations of the data, (2) summaries at appropriate levels of abstraction (physical mechanisms and component operating modes), and (3) query-driven, natural language summaries. Furthermore, combining the compositional modeling and causal ordering techniques allows models that are more scalable and less brittle than models designed solely for explanation. However, these techniques produce models with detail that can be distracting in explanations and would be removed in hand-crafted models (e.g., intermediate variables). We present domain-independent filtering and aggregation techniques that overcome these problems. n techniques that overcome these problems.
Address Washington D.C.  +
Author Patrice O. Gautier and Thomas R. Gruber  +
Bibtype techreport  +
Has author Patrice O. Gautier and Thomas R. Gruber  +
Has identifier KSL-93-06  +
Has publishing details 1993  +
Has title Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering  +
Has where published KSL-93-06  +
Has year 1993  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-93-06  +
Number KSL-93-06  +
Process note NO  +
Title Generating Explanations of Device Behavior Using Compositional Modeling and Causal Ordering  +
Year 1993  +
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