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Using Compiled Knowledge to Guide and Focus Abductive Diagnosis
 IEEE Transactions on Knowledge and Data Engineering
, 1996
"... Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational com ..."
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Cited by 25 (6 self)
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Several artificial intelligence architectures and systems based on "deep" models of a domain have been proposed, in particular for the diagnostic task. These systems have several advantages over traditional knowledge based systems, but they have a main limitation in their computational complexity. One of the ways to face this problem is to rely on a knowledge compilation phase, which produces knowledge that can be used more effectively with respect to the original one. In this paper we show how a specific knowledge compilation approach can focus reasoning in abductive diagnosis, and, in particular, can improve the performances of AID, an abductive diagnosis system. The approach aims at focusing the overall diagnostic cycle in two interdependent ways: avoiding the generation of candidate solutions to be discarded aposteriori and integrating the generation of candidate solutions with discrimination among different candidates. Knowledge compilation is used offline to produce operational...
Representing diagnostic knowledge for probabilistic horn abduction
 Readings in modelbased diagnosis
, 1992
"... This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straightforward implementations using branch and bound search with either logicprogramming technology or ATMS technology. The main fo ..."
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Cited by 23 (6 self)
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This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straightforward implementations using branch and bound search with either logicprogramming technology or ATMS technology. The main focus of this paper is arguing for a form of representational adequacy of this very simple system for diagnostic reasoning. It is shown how it can represent modelbased knowledge, with and without faults, and with and without nonintermittency assumptions. It is also shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. 1
Representing Bayesian networks within probabilistic Horn abduction
 In Proc. Seventh Conf. on Uncertainty in Artificial Intelligence
, 1991
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logic ..."
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Cited by 15 (4 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. It is shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. The main contributions are in finding a relationship between logical and probabilistic notions of evidential reasoning. This can be used as a basis for a new way to implement Bayesian Networks that allows for approximations to the value of the posterior probabilities, and also points to a way that Bayesian networks can be extended beyond a propositional language. 1
More on GoalDirected Diagnosis
 In Working Notes of the Second International Workshop on Principles of Diagnosis
, 1991
"... In many diagnosisandrepair domains, diagnostic reasoning cannot be abstracted from repair actions, nor from actions necessary to obtain diagnostic information. We call these exploratorycorrective domains. In TraumAID 2.0, a consultation system for multiple trauma management, we have developed an ..."
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Cited by 2 (0 self)
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In many diagnosisandrepair domains, diagnostic reasoning cannot be abstracted from repair actions, nor from actions necessary to obtain diagnostic information. We call these exploratorycorrective domains. In TraumAID 2.0, a consultation system for multiple trauma management, we have developed and implemented a framework for reasoning in such domains which integrates diagnostic reasoning with planning and action. In this paper, we present GoalDirected Diagnosis (GDD), the diagnostic reasoning component of this framework. Taking the view that a diagnosis is only worthwhile to the extent that it can affect subsequent decisions, GDD focuses on the formation of appropriate goals for its complementary planner. 1 Prologue In many domains, it is common to distinguish reasoning and activity concerned with what problems need be addressed from that reasoning concerned with how to address those problems. As such, Artificial Intelligence (AI) subsumes as separate subdisciplines diagnosis ...
Of Current Formulations Of ModelBased Diagnosis
, 1991
"... There are three parts to this paper. First, I present what I hope is a conclusive, worstcase, complexity analysis of two wellknown formulations of the Minimal Diagnosis problem — those of [Reiter 87] and [Reggia et al., 85]. I then show that Reiter's conflictsets solution to the problem deco ..."
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There are three parts to this paper. First, I present what I hope is a conclusive, worstcase, complexity analysis of two wellknown formulations of the Minimal Diagnosis problem — those of [Reiter 87] and [Reggia et al., 85]. I then show that Reiter's conflictsets solution to the problem decomposes the single exponential problem into two problems, each exponential, that need be solved sequentially. From a worst case perspective, this only amounts to a factor of two, in which case I see no reason to prefer it over a simple generateandtest approach. This is only emphasized with the results of the third part of the paper. Here I argue for a different perspective on algorithms, that of expected, rather than worstcase performance. From that point of view, a sequence of two exponential algorithms has lesser probability to finish early than a single such algorithm. I show that the straightforward generateandtest approach may in fact be somewhat
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"... Representing diagnostic knowledge for probabilistic Horn abduction This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straightforward implementations using branch and bound search with ..."
Abstract
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Representing diagnostic knowledge for probabilistic Horn abduction This paper presents a simple logical framework for abduction, with probabilities associated with hypotheses. The language is an extension to pure Prolog, and it has straightforward implementations using branch and bound search with either logicprogramming technology or ATMS technology. The main focus of this paper is arguing for a form of representational adequacy of this very simple system for diagnostic reasoning. It is shown how it can represent modelbased knowledge, with and without faults, and with and without nonintermittency assumptions. It is also shown how this representation can represent any probabilistic knowledge representable in a Bayesian belief network. 1