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Adaptive Probabilistic Networks with Hidden Variables
 Machine Learning
, 1997
"... . Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem ..."
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Cited by 158 (10 self)
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. Probabilistic networks (also known as Bayesian belief networks) allow a compact description of complex stochastic relationships among several random variables. They are rapidly becoming the tool of choice for uncertain reasoning in artificial intelligence. In this paper, we investigate the problem of learning probabilistic networks with known structure and hidden variables. This is an important problem, because structure is much easier to elicit from experts than numbers, and the world is rarely fully observable. We present a gradientbased algorithmand show that the gradient can be computed locally, using information that is available as a byproduct of standard probabilistic network inference algorithms. Our experimental results demonstrate that using prior knowledge about the structure, even with hidden variables, can significantly improve the learning rate of probabilistic networks. We extend the method to networks in which the conditional probability tables are described using a ...
Relational bayesian networks
 Proceedings of the 13th Conference of Uncertainty in Artificial Intelligence (UAI13
, 1997
"... A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probabilitydistributionover the relations is directly represented by a Bayesian network. By using a powerful wa ..."
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Cited by 104 (10 self)
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A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose, here a probabilitydistributionover the relations is directly represented by a Bayesian network. By using a powerful way of specifying conditional probability distributions in these networks, the resulting formalism is more expressive than the previous ones. Particularly, it provides for constraints on equalities of events, and it allows to define complex, nested combination functions. 1
Answering Queries from ContextSensitive Probabilistic Knowledge Bases
 Theoretical Computer Science
, 1996
"... We define a language for representing contextsensitive probabilistic knowledge. A knowledge base consists of a set of universally quantified probability sentences that include context constraints, which allow inference to be focused on only the relevant portions of the probabilistic knowledge. We p ..."
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Cited by 93 (0 self)
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We define a language for representing contextsensitive probabilistic knowledge. A knowledge base consists of a set of universally quantified probability sentences that include context constraints, which allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a query answering procedure which takes a query Q and a set of evidence E and constructs a Bayesian network to compute P (QjE). The posterior probability is then computed using any of a number of Bayesian network inference algorithms. We use the declarative semantics to prove the query procedure sound and complete. We use concepts from logic programming to justify our approach. Keywords: reasoning under uncertainty, Bayesian networks, Probability model construction, logic programming Submitted to Theoretical Computer Science special issue on Uncertainty in Databases and Deductive Systems. This work was partially supported by NSF g...
Lifted firstorder probabilistic inference
 In Proceedings of IJCAI05, 19th International Joint Conference on Artificial Intelligence
, 2005
"... Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poo ..."
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Cited by 88 (7 self)
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Most probabilistic inference algorithms are specified and processed on a propositional level. In the last decade, many proposals for algorithms accepting firstorder specifications have been presented, but in the inference stage they still operate on a mostly propositional representation level. [Poole, 2003] presented a method to perform inference directly on the firstorder level, but this method is limited to special cases. In this paper we present the first exact inference algorithm that operates directly on a firstorder level, and that can be applied to any firstorder model (specified in a language that generalizes undirected graphical models). Our experiments show superior performance in comparison with propositional exact inference. 1
A Perspective on Inductive Logic Programming
"... . The stateoftheart in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, of 2) theory, techniques and applications, of 3) various technical problems address ..."
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Cited by 55 (8 self)
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. The stateoftheart in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, of 2) theory, techniques and applications, of 3) various technical problems addressed within inductive logic programming. 1 Introduction The term inductive logic programming was first coined by Stephen Muggleton in 1990 [1]. Inductive logic programming is concerned with the study of inductive machine learning within the representations offered by computational logic. Since 1991, annual international workshops have been organized [28]. This paper is an attempt to analyze the developments within this field. Particular attention is devoted to the relation between inductive logic programming and its neighboring fields such as machine learning, computational logic and data mining, and to the role that theory, techniques and implementations, and applications play. The analysis...
LargeScale Repositories of Highly Expressive Reusable Knowledge
, 1999
"... We describe an ongoing project to develop technology that will support collaborative construction and effective use of distributed largescale repositories of highly expressive reusable ontologies. We are focusing on developing a distributed server architecture for ontology construction and use, re ..."
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Cited by 49 (1 self)
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We describe an ongoing project to develop technology that will support collaborative construction and effective use of distributed largescale repositories of highly expressive reusable ontologies. We are focusing on developing a distributed server architecture for ontology construction and use, representation formalisms that remove key barriers to expressing essential knowledge in and about ontologies, ontology construction tools, and tools for obtaining domain models for use in applications from largescale ontology repositories. We are building on the results of the DARPA Knowledge Sharing Effort, specifically by using the Knowledge Interchange Format (KIF) as a core representation language and the Ontolingua system as a core ontology development environment. In order to enable distributed ontology repositories and services, we are developing a distributed server architecture for ontology construction and use based on ontology servers which provide access via a network API to the ...
Probabilistic Logic Learning
 ACMSIGKDD Explorations: Special issue on MultiRelational Data Mining
, 2004
"... The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This pap ..."
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Cited by 34 (8 self)
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The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of di#erent formalisms and learning techniques have been developed. This paper provides an introductory survey and overview of the stateof theart in probabilistic logic learning through the identification of a number of important probabilistic, logical and learning concepts.
Probabilistic Logic Programming and Bayesian Networks
 In Asian Computing Science Conference
, 1995
"... We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative ..."
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Cited by 30 (0 self)
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We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning. To appear in Proceedings of the 1995 Asian Computing Science Conference, Pathumthani, Thailand, December 1995. This work was partially supported by NSF grant IRI9509165. 1 Introduction Reasoning u...
Defining Explanation in Probabilistic Systems
 In Proc. UAI97
, 1997
"... As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to expla ..."
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Cited by 23 (3 self)
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As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature one due to G ardenfors and one due to Pearland show that both suffer from significant problems. We propose an approach to defining a notion of "better explanation" that combines some of the features of both together with more recent work by Pearl and others on causality. 1 INTRODUCTION Probabilistic inference is often hard for humans to understand. Even a simple inference in a small domain may seem counterintuitive and surprising; the situation only gets worse for large and complex domains. Thus, a system doing probabilistic inference must be able to explain its findings and recommendations to evoke confidence on the part of the user. Indeed, in experiments wi...
A Theoretical Framework for ContextSensitive Temporal Probability Model Construction with Application to Plan Projection
 In Proc. UAI95
, 1995
"... We define a contextsensitive temporal probability logic for representing classes of discretetime temporal Bayesian networks. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We prese ..."
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Cited by 21 (3 self)
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We define a contextsensitive temporal probability logic for representing classes of discretetime temporal Bayesian networks. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a Bayesian network construction algorithm whose generated networks give sound and complete answers to queries. We use related concepts in logic programming to justify our approach. We have implemented a Bayesian network construction algorithm for a subset of the theory and demonstrate it's application to the problem of evaluating the effectiveness of treatments for acute cardiac conditions. 1 Introduction Most of our knowledge about the world is highly contextual. The use of context facilitates reasoning by allowing an agent to focus its attention on that portion of its knowledge relevant to a given problem. In automated reasoning the need to use context becomes particularly acute when d...