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Probabilistic Horn abduction and Bayesian networks
 Artificial Intelligence
, 1993
"... This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesia ..."
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Cited by 298 (37 self)
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This paper presents a simple framework for Hornclause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy. It also shows how Bayesian networks can be extended beyond a propositional language. This paper also shows how a language with only (unconditionally) independent hypotheses can represent any probabilistic knowledge, and argues that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language. Scholar, Canadian Institute for Advanced...
Exploiting structure in policy construction
 IJCAI95, pp.1104–1111
, 1995
"... Markov decision processes (MDPs) have recently been applied to the problem of modeling decisiontheoretic planning. While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable. We present an algorithm, call ..."
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Cited by 226 (22 self)
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Markov decision processes (MDPs) have recently been applied to the problem of modeling decisiontheoretic planning. While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable. We present an algorithm, called structured policy iteration (SPI), that constructs optimal policies without explicit enumeration of the state space. The algorithm retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but exploitsthe variable and propositionalindependencies reflected in a temporal Bayesian network representation of MDPs. The principles behind SPI can be applied to any structured representation of stochastic actions, policies and value functions, and the algorithm itself can be used in conjunction with recent approximation methods. 1
Logic Programming, Abduction and Probability: a topdown anytime algorithm for estimating prior and posterior probabilities
 New Generation Computing
, 1993
"... Probabilistic Horn abduction is a simple framework to combine probabilistic and logical reasoning into a coherent practical framework. The numbers can be consistently interpreted probabilistically, and all of the rules can be interpreted logically. The relationship between probabilistic Horn abducti ..."
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Cited by 39 (8 self)
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Probabilistic Horn abduction is a simple framework to combine probabilistic and logical reasoning into a coherent practical framework. The numbers can be consistently interpreted probabilistically, and all of the rules can be interpreted logically. The relationship between probabilistic Horn abduction and logic programming is at two levels. At the first level probabilistic Horn abduction is an extension of pure Prolog, that is useful for diagnosis and other evidential reasoning tasks. At another level, current logic programming implementation techniques can be used to efficiently implement probabilistic Horn abduction. This forms the basis of an "anytime" algorithm for estimating arbitrary conditional probabilities. The focus of this paper is on the implementation. Scholar, Canadian Institute for Advanced Research Logic Programming, Abduction and Probability 2 1 Introduction Probabilistic Horn Abduction [22, 21, 23] is a framework for logicbased abduction that incorporates proba...
The Independent Choice Logic and Beyond
"... Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a l ..."
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Cited by 18 (5 self)
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Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity. 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 13 (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
Abduction and ExplanationBased Learning: Case Studies in Diverse Domains
, 1993
"... This paper presents a knowledgebased learning method and reports on case studies in di#erent domains. The method integrates abduction and explanationbased learning. Abduction provides an improved method for constructing explanations. The improvement enlarges the set of examples that can be expl ..."
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Cited by 9 (2 self)
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This paper presents a knowledgebased learning method and reports on case studies in di#erent domains. The method integrates abduction and explanationbased learning. Abduction provides an improved method for constructing explanations. The improvement enlarges the set of examples that can be explained so that one can learn from additional examples using traditional explanationbased macro learning. Abduction also provides a form of knowledge level learning. Descriptions of case studies show how to set up abduction engines for tasks in particular domains. The case studies involve over a hundred examples taken from diverse domains requiring logical, physical, and psychological knowledge and reasoning. The case studies are relevant to a wide range of practical tasks including: natural language understanding and plan recognition; qualitative physical reasoning and postdiction; diagnosis and signal interpretation; and decisionmaking under uncertainty. The descriptions of the ca...
ILP turns 20  Biography and future challenges
 MACH LEARN
, 2011
"... Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characteri ..."
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Cited by 7 (6 self)
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Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging realworld applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.
Logic, Knowledge Representation and Bayesian Decision Theory
 IN PROCEEDINGS CL2000, VOL. 1861 OF LNCS
, 2000
"... In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are re ..."
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Cited by 4 (0 self)
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In this paper I give a brief overview of recent work on uncertainty in AI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reasoning under uncertainty, evidential and causal reasoning, decision analysis, dynamical systems, optimal control, reinforcement learning and Bayesian learning. The independent choice logic provides a bridge between logical representations and belief networks that lets us understand these other representations and their relationship to logic and shows how they can extended to firstorder rulebased representations.
Using Domain Knowledge to Select Solutions in Abductive Diagnosis
, 1994
"... . This paper presents a novel extension to abductive reasoning in causal nets, namely the use of domain knowledge to select among alternative diagnoses. We describe how preferences among multiple causes of a given state can be expressed in terms of causal nets, and how these preferences can be used ..."
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Cited by 3 (2 self)
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. This paper presents a novel extension to abductive reasoning in causal nets, namely the use of domain knowledge to select among alternative diagnoses. We describe how preferences among multiple causes of a given state can be expressed in terms of causal nets, and how these preferences can be used to select among alternative diagnoses. We investigate this new extension by proving a number of properties, and show how our preference scheme interacts with conventional ways of choosing among competing diagnoses. Our extension increases the expressive power of causal nets, enjoys a number of desirable properties, and compares favourably with existing proposals for expressing preferential knowledge in causal nets. 1 INTRODUCTION The standard definition of abduction in causal nets (e.g. [2]) often yields multiple possible explanations of a given set of observations, without any further means of distinguishing between these explanations. In this paper, we show how knowledge required for maki...