Control of Problem Solving: Principles and Architecture

Breese, John S.; Fehling, Michael

doi:10.1016/b978-0-444-88650-7.50010-x

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…A variety of solutions have been proposed, ranging from compilation techniques (to shift the burden from run-time to compile-time), to the determination of bounds of the posterior probabilities. Following the work of Horvitz (1988) and Breese and Fehling (1988), establishing the applicability of decision theoretic principles in defining bounded rationality for reasoning with limited resources, Heckerman et al (1989) have provided a decision-theoretic-based analysis of computation versus compilation. Given a description of the nature of evidential relationships in the domain, of the utilities attached to alternative actions, of the cost of run-time delay, and of the cost of memory, their analysis determines the subset of evidence that is more cost-effective to compile.…”

Section: Previous Work: Real-time Approximate Reasoning Systemsmentioning

confidence: 99%

Time-constrained reasoning under uncertainty

Bonissone

Halverson

1990

Real-Time Syst

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Abstract. Dynamic classification problems present unique challenges beyond those of more traditional static knowledge-based systems. Uncertain and incomplete input data, unpredictable event sequences, and critical time and resource constraints require new approaches and techniques for automated reasoning. Our work toward addressing these complex requirements has concentrated on developing an integrated software architecture which supports the knowledge engineering process from development to deployment. The approach we are using to deal with real-time issues in the deployment environment involves the use of a fast knowledge representation scheme, efficient forward and backward chaining mechanisms, and a meta-controller which handles asynchronous inputs, prioritized task requests, and hard performance deadlines.

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Section: Previous Work: Real-time Approximate Reasoning Systemsmentioning

confidence: 99%

Time-constrained reasoning under uncertainty

Bonissone

Halverson

1990

Real-Time Syst

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“…The problems of reasoning and search with limited time have received substantial attention from A1 researchers. One version of this problem is that of real-time reasoning and search, in which a fixed amount of time is available for each episode of reasoning or search (Korf 1988;Breese and Fehling 1990). Other work has explored obtaining predictable response time, rather than fixed response times (Levesque and Brachman 1987).…”

Section: Rational Search and Inferencementioning

confidence: 99%

“…They suggest improving the quality of utility estimates by using the values revealed by the position evaluation function as evidence for the true values, and by treating the search graph as a belief network so as to combine this evidence via Bayesian conditionalization. In addition, one may seek to control reasoning on a larger scale than individual search steps using similar decision-theoretic judgments (see Horvitz 1988; Fehling et al 1989;Horvitz et al 1989;Breese and Fehling 1990).…”

Section: Rational Search and Inferencementioning

confidence: 99%

Rationality and Its Roles in Reasoning

Doyle

1992

Computational Intelligence

104

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“…In other work, researchers are examining the use of decision theory to provide a richer framework for making this kind of modeling trade-off at the metalevel (Breese and Fehling 1988;Horvitz et al 1989;Wellman et al 1992). …”

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confidence: 97%

Construction of Belief and Decision Networks

Breese

1992

Computational Intelligence

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We describe a representation and set of inference techniques for the dynamic construction of probabilistic and decision-theoretic models expressed as networks. In contrast to probabilistic reasoning schemes that rely on fixed models, we develop a representation that implicitly encodes a large number of possible model structures. Based on a particular query and state of information, the system constructs a customized belief net for that particular situation. We develop an interpretation of the network construction process in terms of the implicit networks encoded in the database. A companion method for constructing belief networks with decisions and values (decision networks) is also developed that uses sensitivity analysis to focus the model building process. Finally, we discuss some issues of control of model construction and describe examples of constructing networks.

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Control of Problem Solving: Principles and Architecture

Cited by 12 publications

References 13 publications

Time-constrained reasoning under uncertainty

Time-constrained reasoning under uncertainty

Rationality and Its Roles in Reasoning

Construction of Belief and Decision Networks

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