Explanatory Diagnosis: Conjecturing actions to explain observations

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Citation: Sheila A. McIlraith. (1998) Explanatory Diagnosis: Conjecturing actions to explain observations. In KSL-98-21, July,1998.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Sheila A. McIlraith
title Explanatory Diagnosis: Conjecturing actions to explain observations
number KSL-98-21
institution Knowledge Systems, AI Laboratory
year 1998
month July
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abstract In this paper we present contributions towards a logical theory of diagnosis for systems that can be affected by the actions of agents. Specifically, we examine the task of conjecturing diagnoses to explain {\it what happened} to a system, given a theory of system behaviour and some observed (aberrant) behaviour. We characterize what happened by introducing the notion of explanatory diagnosis in the language of the situation calculus. Explanatory diagnoses conjecture sequences of actions to account for a change in system behaviour. As such, we show that determining an explanatory diagnosis is analogous to classical AI planning with state constraints and incomplete knowledge. The representation scheme we employ provides an axiomatic solution to the frame, ramification and qualification problems. Exploiting this representation, we show that determining an explanatory diagnosis can be achieved by regression followed by theorem proving in the database describing what is known of the initial state of our system. Further, we show that by exploiting features inherent to diagnosis problems, we can simplify the diagnosis task.

KSL Technical Report ID: KSL-98-21
Facts about Explanatory Diagnosis: Conjecturing actions to explain observationsRDF feed
Abstract In this paper we present contributions tow In this paper we present contributions towards a logical theory of diagnosis for systems that can be affected by the actions of agents. Specifically, we examine the task of conjecturing diagnoses to explain {\it what happened} to a system, given a theory of system behaviour and some observed (aberrant) behaviour. We characterize what happened by introducing the notion of explanatory diagnosis in the language of the situation calculus. Explanatory diagnoses conjecture sequences of actions to account for a change in system behaviour. As such, we show that determining an explanatory diagnosis is analogous to classical AI planning with state constraints and incomplete knowledge. The representation scheme we employ provides an axiomatic solution to the frame, ramification and qualification problems. Exploiting this representation, we show that determining an explanatory diagnosis can be achieved by regression followed by theorem proving in the database describing what is known of the initial state of our system. Further, we show that by exploiting features inherent to diagnosis problems, we can simplify the diagnosis task. blems, we can simplify the diagnosis task.
Author Sheila A. McIlraith  +
Bibtype techreport  +
Has author Sheila A. McIlraith  +
Has identifier KSL-98-21  +
Has publishing details July,1998  +
Has title Explanatory Diagnosis: Conjecturing actions to explain observations  +
Has where published KSL-98-21  +
Has year 1998  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-98-21  +
Month July  +
Number KSL-98-21  +
Process note NO  +
Title Explanatory Diagnosis: Conjecturing actions to explain observations  +
Year 1998  +
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