153,051 research outputs found
Constructing Situation Specific Belief Networks
This paper describes a process for constructing situation-specific belief
networks from a knowledge base of network fragments. A situation-specific
network is a minimal query complete network constructed from a knowledge base
in response to a query for the probability distribution on a set of target
variables given evidence and context variables. We present definitions of query
completeness and situation-specific networks. We describe conditions on the
knowledge base that guarantee query completeness. The relationship of our work
to earlier work on KBMC is also discussed.Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998
Constructing Belief Networks to Evaluate Plans
This paper examines the problem of constructing belief networks to evaluate
plans produced by an knowledge-based planner. Techniques are presented for
handling various types of complicating plan features. These include plans with
context-dependent consequences, indirect consequences, actions with
preconditions that must be true during the execution of an action,
contingencies, multiple levels of abstraction multiple execution agents with
partially-ordered and temporally overlapping actions, and plans which reference
specific times and time durations.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Network Fragments: Representing Knowledge for Constructing Probabilistic Models
In most current applications of belief networks, domain knowledge is
represented by a single belief network that applies to all problem instances in
the domain. In more complex domains, problem-specific models must be
constructed from a knowledge base encoding probabilistic relationships in the
domain. Most work in knowledge-based model construction takes the rule as the
basic unit of knowledge. We present a knowledge representation framework that
permits the knowledge base designer to specify knowledge in larger semantically
meaningful units which we call network fragments. Our framework provides for
representation of asymmetric independence and canonical intercausal
interaction. We discuss the combination of network fragments to form
problem-specific models to reason about particular problem instances. The
framework is illustrated using examples from the domain of military situation
awareness.Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997
Representing and Combining Partially Specified CPTs
This paper extends previous work with network fragments and
situation-specific network construction. We formally define the asymmetry
network, an alternative representation for a conditional probability table. We
also present an object-oriented 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.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
The Automated Mapping of Plans for Plan Recognition
To coordinate with other agents in its environment, an agent needs models of
what the other agents are trying to do. When communication is impossible or
expensive, this information must be acquired indirectly via plan recognition.
Typical approaches to plan recognition start with a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Time-Dependent Utility and Action Under Uncertainty
We discuss representing and reasoning with knowledge about the time-dependent
utility of an agent's actions. Time-dependent utility plays a crucial role in
the interaction between computation and action under bounded resources. We
present a semantics for time-dependent utility and describe the use of
time-dependent information in decision contexts. We illustrate our discussion
with examples of time-pressured reasoning in Protos, a system constructed to
explore the ideal control of inference by reasoners with limit abilities.Comment: Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991
Dynamic Construction of Belief Networks
We describe a method for incrementally constructing belief networks. We have
developed a network-construction language similar to a forward-chaining
language using data dependencies, but with additional features for specifying
distributions. Using this language, we 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.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
Using First-Order Probability Logic for the Construction of Bayesian Networks
We present a mechanism for constructing graphical models, specifically
Bayesian networks, from a knowledge base of general probabilistic information.
The unique feature of our approach is that it uses a powerful first-order
probabilistic logic for expressing the general knowledge base. This logic
allows for the representation of a wide range of logical and probabilistic
information. The model construction procedure we propose uses notions from
direct inference to identify pieces of local statistical information from the
knowledge base that are most appropriate to the particular event we want to
reason about. These pieces are composed to generate a joint probability
distribution specified as a Bayesian network. Although there are fundamental
difficulties in dealing with fully general knowledge, our procedure is
practical for quite rich knowledge bases and it supports the construction of a
far wider range of networks than allowed for by current template technology.Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993
An Application of Uncertain Reasoning to Requirements Engineering
This paper examines the use of Bayesian Networks to tackle one of the tougher
problems in requirements engineering, translating user requirements into system
requirements. The approach taken is to model domain knowledge as Bayesian
Network fragments that are glued together to form a complete view of the domain
specific system requirements. User requirements are introduced as evidence and
the propagation of belief is used to determine what are the appropriate system
requirements as indicated by user requirements. This concept has been
demonstrated in the development of a system specification and the results are
presented here.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Dynamic Jointrees
It is well known that one can ignore parts of a belief network when computing
answers to certain probabilistic queries. It is also well known that the
ignorable parts (if any) depend on the specific query of interest and,
therefore, may change as the query changes. Algorithms based on jointrees,
however, do not seem to take computational advantage of these facts given that
they typically construct jointrees for worst-case queries; that is, queries for
which every part of the belief network is considered relevant. To address this
limitation, we propose in this paper a method for reconfiguring jointrees
dynamically as the query changes. The reconfiguration process aims at
maintaining a jointree which corresponds to the underlying belief network after
it has been pruned given the current query. Our reconfiguration method is
marked by three characteristics: (a) it is based on a non-classical definition
of jointrees; (b) it is relatively efficient; and (c) it can reuse some of the
computations performed before a jointree is reconfigured. We present
preliminary experimental results which demonstrate significant savings over
using static jointrees when query changes are considerable.Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998
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