Results 1  10
of
26
Bayesian Networks Without Tears
 AI MAGAZINE
, 1991
"... I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesia ..."
Abstract

Cited by 295 (2 self)
 Add to MetaCart
(Show Context)
I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AIuncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
Dynamic construction of belief networks
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1990
"... AbstractWe describe a method for incrementally constructing belief networks, which are directed acyclic graph representations for probability distributions. We have developed a networkconstruction language (FRAIW), which is similar to a fonvardchaining language using data dependencies but has add ..."
Abstract

Cited by 48 (2 self)
 Add to MetaCart
AbstractWe describe a method for incrementally constructing belief networks, which are directed acyclic graph representations for probability distributions. We have developed a networkconstruction language (FRAIW), which is similar to a fonvardchaining language using data dependencies but has additional features for specifying distributions. A particularly important feature of this language is that it allows the user to conveniently specify conditional probability matrices using stereotyped models of intercausal interaction. Using FRAIW, one can define parameterized classes of probabilistic models. These parameterized models make it possible to apply probabilistic reasoning to problems for which it is impractical to have a single large, static model.
On the Generation of Alternative Explanations with Implications for Belief Revision
 In Uncertainty in Artificial Intelligence (UAI91
, 1991
"... In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of messagepassing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In t ..."
Abstract

Cited by 45 (5 self)
 Add to MetaCart
In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of messagepassing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In this paper, we present a general approach based on linear constraint systems that naturally generates alternative explanations in an orderly and highly efficient manner. This approach is then applied to costbased abduction problems as well as belief revision in Bayesian networks.
Network Engineering for Complex Belief Networks
 In Proc. UAI
, 1996
"... Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a rapid prototyping approach to network construction. We describe criteria for identifying network modules and the use of ` ..."
Abstract

Cited by 39 (4 self)
 Add to MetaCart
(Show Context)
Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a rapid prototyping approach to network construction. We describe criteria for identifying network modules and the use of `stubs' within a belief network. We propose an object oriented representation for belief networks which captures the semantic as well as representational knowledge embedded in the variables, their values and their parameters. Methods for evaluating complex networks are described. Throughout the discussion, tools which support the engineering of large belief networks are identified. 1. Introduction As belief networks become more popular and well understood as a tool for modeling uncertainty and as the computational power of belief network inference engines increases, belief networks are being applied to problems of increasing size and complexity. In the early 1990's, Pathfinder, at 109 nodes...
OnLine New Event Detection, Clustering, And Tracking
, 1999
"... In this work, we discuss and evaluate solutions to text classification problems associated with the events that are reported in online sources of news. We present solutions to three related classification problems: new event detection, event clustering, and event tracking. The primary focus of this ..."
Abstract

Cited by 27 (0 self)
 Add to MetaCart
In this work, we discuss and evaluate solutions to text classification problems associated with the events that are reported in online sources of news. We present solutions to three related classification problems: new event detection, event clustering, and event tracking. The primary focus of this thesis is new event detection, where the goal is to identify news stories that have not previously been reported, in a stream of broadcast news comprising radio, television, and newswire. We present an algorithm for new event detection, and analyze the effects of incorporating domain properties into the classification algorithm. We explore a solution that models the temporal relationship between news stories, and investigate the use of proper noun phrase
Abductive plan recognition and diagnosis: A comprehensive empirical evaluation
 In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning
, 1992
"... While it has been realized for quite some time within AI that abduction is a general model of explanation for a variety of tasks, there have been no empirical investigations into the practical feasibility of a general, logicbased abductive approach to explanation. In this paper we present extensive ..."
Abstract

Cited by 23 (4 self)
 Add to MetaCart
While it has been realized for quite some time within AI that abduction is a general model of explanation for a variety of tasks, there have been no empirical investigations into the practical feasibility of a general, logicbased abductive approach to explanation. In this paper we present extensive empirical results on applying a general abductive system, Accel, to moderately complex problems in plan recognition and diagnosis. In plan recognition, Accel has been tested on 50 short narrative texts, inferring characters ' plans from actions described in a text. In medical diagnosis, Accel has diagnosed 50 realworld patient cases involving brain damage due to stroke (previously addressed by setcovering methods). Accel also uses abduction to accomplish modelbased diagnosis of logic circuits (a full adder) and continuous dynamic systems (a temperature controller and the water balance system of the human kidney). The results indicate that general purpose abduction is an e ective and e cient mechanism for solving problems in plan recognition and diagnosis. 1
Probabilistic Abduction using Markov Logic Networks
"... Abduction is inference to the best explanation of a given set of evidence. It is important for plan or intent recognition systems. Traditional approaches to abductive reasoning have either used firstorder logic, which is unable to reason under uncertainty, or Bayesian networks, which can handle unc ..."
Abstract

Cited by 17 (4 self)
 Add to MetaCart
Abduction is inference to the best explanation of a given set of evidence. It is important for plan or intent recognition systems. Traditional approaches to abductive reasoning have either used firstorder logic, which is unable to reason under uncertainty, or Bayesian networks, which can handle uncertainty using probabilities but cannot directly handle an unbounded number of related entities. This paper proposes a new method for probabilistic abductive reasoning that combines the capabilities of firstorder logic and graphical models by using Markov logic networks. Experimental results on a plan recognition task demonstrate the effectiveness of this method. 1
A Fast HillClimbing Approach Without an Energy Function for Probabilistic Reasoning
 In Proceedings of the 5th IEEE International Conference on Tools with Artificial Intelligence
, 1993
"... Integer linear programming (ILP) has long been an important tool for Operations Research akin to our AI search heuristics for NPhard problems. However, there has been relatively little incentive to use it in AI even though it also deals with optimization. The problem stems from the misperception th ..."
Abstract

Cited by 16 (10 self)
 Add to MetaCart
Integer linear programming (ILP) has long been an important tool for Operations Research akin to our AI search heuristics for NPhard problems. However, there has been relatively little incentive to use it in AI even though it also deals with optimization. The problem stems from the misperception that because the general ILP problem is difficult to solve, then it will be difficult for all cases. As we all know, AI search at first glance also seems this way until we begin to apply it to a specific domain. Clearly, there are many gains to be had from studying the problem with a different perspective like ILP. In this paper, we look at probabilistic reasoning with Bayesian networks. For some time now, we have been stalled by its computational complexities. Algorithms have been designed for small classes of networks, but have been mainly inextensible to the general case. In particular, we consider belief revision in Bayesian networks which is the search for the mostprobable explanation for...
Hypothesis Management in SituationSpecific Network Construction
, 2001
"... This paper considers the problem of knowledgebased model construction in the presence of uncertainty about the association of domain entities to random variables. Multientity Bayesian networks (MEBNs) are defined as a representation for knowledge in domains characterized by uncertainty in the ..."
Abstract

Cited by 13 (9 self)
 Add to MetaCart
This paper considers the problem of knowledgebased model construction in the presence of uncertainty about the association of domain entities to random variables. Multientity Bayesian networks (MEBNs) are defined as a representation for knowledge in domains characterized by uncertainty in the number of relevant entities, their interrelationships, and their association with observables. An MEBN implicitly specifies a probability distribution in terms of a hierarchically structured collection of Bayesian network fragments that together encode a joint probability distribution over arbitrarily many interrelated hypotheses. Although a finite querycomplete model can always be constructed, association uncertainty typically makes exact model construction and evaluation intractable. The objective of hypothesis management is to balance tractability against accuracy. We describe an approach to hypothesis management, present an application to the problem of military situation awareness, and compare our approach to related work in the tracking and fusion literature. 1
Bayesian Abductive Logic Programs
"... In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine firstorder logic and Bayesian networks. However, unlike BLPs that use lo ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine firstorder logic and Bayesian networks. However, unlike BLPs that use logical deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for solving problems like plan/activity recognition and diagnosis that require abductive reasoning. First, we present the necessary enhancements to BLPs in order to support logical abduction. Next, we apply BALPs to the task of plan recognition and demonstrate its efficacy on two data sets. We also compare the performance of BALPs with several existing approaches for abduction. 1