Results 1  10
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39
Objectoriented Bayesian networks.
 In Proc. UAI97,
, 1997
"... Abstract Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of mediumscale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to re ..."
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Cited by 218 (9 self)
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Abstract Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of mediumscale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of pro gramming using logical circuits. In this paper, we de scribe an objectoriented Bayesian network (OOBN) lan guage, which allows complex domains to be described in terms of interrelated objects. We use a Bayesian net work fragment to describe the probabilistic relations be tween the attributes of an object. These attributes can themsel ves be objects, providing a natural framework for encoding partof hierarchies. Classes are used to pro vide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inher itance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochas tic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural infor mation encoded by an OOBNparticularly the encap sulation of variables within an object and the reuse of model fragments in different contextscan also be used to speed up the inference process.
Building LargeScale Bayesian Networks
, 1999
"... Bayesian Networks (BNs) model problems that involve uncertainty. A BN is a directed graph, whose nodes are the uncertain variables and whose edges are the causal or influential links between the variables. Associated with each node is a set of conditional probability functions that model the unce ..."
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Cited by 54 (16 self)
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Bayesian Networks (BNs) model problems that involve uncertainty. A BN is a directed graph, whose nodes are the uncertain variables and whose edges are the causal or influential links between the variables. Associated with each node is a set of conditional probability functions that model the uncertain relationship between the node and its parents. The benefits of using BNs to model uncertain domains are well known, especially since the recent breakthroughs in algorithms and tools to implement them. However, there have been serious problems for practitioners trying to use BNs to solve realistic problems. This is because, although the tools make it possible to execute largescale BNs efficiently, there have been no guidelines on building BNs. Specifically, practitioners face two significant barriers. The first barrier is that of specifying the graph structure such that it is a sensible model of the types of reasoning being applied. The second barrier is that of eliciting the conditional probability values, from a domain expert, for a graph containing many combinations of nodes, where each may have a large number of discrete or continuous values. We have tackled both of these practical problems in recent research projects and have produced partial solutions for both that have been applied extensively on a number of reallife applications. In this paper we shall concentrate on this first problem, that of specifying a sensible BN graph structure. Our solution is based on the notion of generally applicable `building blocks', called idioms, which can be combined together into modular subnets. These can then in turn be combined into larger BNs, using simple combination rules and by exploiting recent ideas on modular and Object Oriented BNs (OOBNs). This appr...
Using ranked nodes to model qualitative judgements in Bayesian Networks
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2007
"... Although Bayesian Nets (BNs) are increasingly being used to solve real world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising t ..."
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Cited by 12 (8 self)
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Although Bayesian Nets (BNs) are increasingly being used to solve real world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely costeffective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive realworld case studies we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe this work represents a useful contribution to BN research and technology since its application makes the difference between being able to build realistic BN models and not.
Practical Issues in Modeling Large Diagnostic Systems with Multiply Sectioned Bayesian Networks
 INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
, 2000
"... As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include mo ..."
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Cited by 12 (2 self)
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As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These large projects demand a systematic approach to handle the complexity in knowledge engineering. The needs include modularity in representation, distribution in computation, as well as coherence in inference. Multiply Sectioned Bayesian Networks (MSBNs) provide a distributed multiagent framework to address these needs. According to the framework, a large system is partitioned into subsystems and represented as a set of related Bayesian subnets. To ensure exact inference, the partition of a large system into subsystems and the representation of subsystems must follow a set of technical constraints. How to satisfy these goals for a given system may not be obvious to a practitioner. In this paper, we address three practical modeling issues.
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
, 2003
"... When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this inf ..."
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Cited by 9 (0 self)
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When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this information is too vague to be encoded as properties that are local to families of variables. For instance, conventional algorithms do not exploit prior information about repetitive structures, which are often found in object oriented domains such as computer networks, large pedigrees and genetic analysis.
StateoftheArt of Intention Recognition and its use in Decision Making  A research summary
"... Intention recognition is the process of becoming aware of the intentions of other agents, inferring them through observed actions or effects on the environment. Intention recognition enables proactiveness, in cooperating or promoting cooperation, and in preempting danger. Technically, intention rec ..."
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Cited by 7 (6 self)
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Intention recognition is the process of becoming aware of the intentions of other agents, inferring them through observed actions or effects on the environment. Intention recognition enables proactiveness, in cooperating or promoting cooperation, and in preempting danger. Technically, intention recognition can be performed incrementally as you go along, which amounts to learning. Intention recognition can also use past experience from a database of past interactions, not necessarily with the same agent. Bayesian Networks (BN) can be employed to dynamically summarize general statistical evidence, furnishing heuristic information to link with the situation specific information, about which logical reasoning can take place, and decisions made on actions to be performed, possibly involving actions to obtain new observations. This situated reasoning feeds into the BN to tune it, and back again into the logic component. In this article, we provide a review bearing on the stateoftheart work on intention and plan recognition, which includes a comparison with our recent research, where we address a number of important issues of intention recognition. We also argue for an integrative approach to
The Use of Bayesian Network for Web Effort Estimation
 Proceedings of ICWE'07
, 2007
"... The objective of this paper is to further investigate the use of Bayesian Networks (BN) for Web effort estimation when using a crosscompany dataset. Four BNs were built; two automatically using the Hugin tool with two training sets; two using a structure elicited by a domain expert, with parameters ..."
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Cited by 6 (2 self)
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The objective of this paper is to further investigate the use of Bayesian Networks (BN) for Web effort estimation when using a crosscompany dataset. Four BNs were built; two automatically using the Hugin tool with two training sets; two using a structure elicited by a domain expert, with parameters obtained from automatically fitting the network to the same training sets used in the automated elicitation (hybrid models). The accuracy of all four models was measured using two validation sets, and point estimates. As a benchmark, the BNbased predictions were also compared to predictions obtained using Manual StepWise Regression (MSWR), and CaseBased Reasoning (CBR). The BN model generated using Hugin presented similar accuracy to CBR and Mean effortbased predictions. Our results suggest that Hybrid BN models can provide significantly superior prediction accuracy. However, good results also seem to depend on characteristics of the training and validation sets used. 1.
Representing and combining partially specified cpts
 In Proc., 1999 Conf. on Uncertainty in AI
, 1999
"... This paper extends previous work with network fragments and situationspecific network construction. We formally define the asymmetry network, an alternative representation for a conditional probability table. We also present an objectoriented representation for partially specified asymmetry networ ..."
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Cited by 6 (0 self)
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This paper extends previous work with network fragments and situationspecific network construction. We formally define the asymmetry network, an alternative representation for a conditional probability table. We also present an objectoriented representation for partially specified asymmetry networks. We show that the representation is parsimonious. We define an algebra for the elements of the representation that allows us to ‘factor ’ any CPT and to soundly combine the partially specified asymmetry networks. 1.