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Reasoning about Beliefs and Actions under Computational Resource Constraints
 In Proceedings of the 1987 Workshop on Uncertainty in Artificial Intelligence
, 1987
"... ion Modulation In many cases, it may be more useful to do normative inference on a model that is deemed to be complete at a particular level of abstraction than it is to do an approximate or heuristic analysis of a model that is too large to be analyzed under specific resource constraints. It may pr ..."
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Cited by 179 (18 self)
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ion Modulation In many cases, it may be more useful to do normative inference on a model that is deemed to be complete at a particular level of abstraction than it is to do an approximate or heuristic analysis of a model that is too large to be analyzed under specific resource constraints. It may prove useful in many cases to store several beliefnetwork representations, each containing propositions at different levels of abstraction. In many domains, models at higher levels of abstraction are more tractable. As the time available for computation decreases, network modules of increasing abstraction can be employed. ffl Local Reformulation Local reformulation is the modification of specific troublesome topologies in a belief network. Approximation methods and heuristics designed to modify the microstructure of belief networks will undoubtedly be useful in the tractable solution of large uncertainreasoning problems. Such strategies might be best applied at knowledgeencoding time. An...
Decision Theory in Expert Systems and Artificial Intelligence
 International Journal of Approximate Reasoning
, 1988
"... Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision ..."
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Cited by 89 (18 self)
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Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decisiontheoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expertsystem paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expertsystems sett...
Toward normative expert systems: Part I. The pathfinder project
 Methods Inf. Med
, 1992
"... Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymphnode diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this ..."
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Cited by 83 (15 self)
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Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymphnode diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertainreasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decisiontheoretic methods for reasoning under uncertainty and our initial attempts to use nondecisiontheoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.
A hierarchical dirichlet language model
 Natural Language Engineering
, 1994
"... We discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as 'smoothing'. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new directions ..."
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Cited by 79 (3 self)
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We discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as 'smoothing'. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new directions for language modelling. The ideas of this paper are also applicable to other problems such as the modelling of triphomes in speech, and DNA and protein sequences in molecular biology. The new algorithm is compared with smoothing on a two million word corpus. The methods prove to be about equally accurate, with the hierarchical model using fewer computational resources. 1
A principled approach to detecting surprising events in video
 in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2005
"... Primates demonstrate unparalleled ability at rapidly orienting towards important events in complex dynamic environments. During rapid guidance of attention and gaze towards potential objects of interest or threats, often there is no time for detailed visual analysis. Thus, heuristic computations are ..."
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Cited by 76 (6 self)
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Primates demonstrate unparalleled ability at rapidly orienting towards important events in complex dynamic environments. During rapid guidance of attention and gaze towards potential objects of interest or threats, often there is no time for detailed visual analysis. Thus, heuristic computations are necessary to locate the most interesting events in quasi realtime. We present a new theory of sensory surprise, which provides a principled and computable shortcut to important information. We develop a model that computes instantaneous lowlevel surprise at every location in video streams. The algorithm significantly correlates with eye movements of two humans watching complex video clips, including television programs (17,936 frames, 2,152 saccadic gaze shifts). The system allows more sophisticated and timeconsuming image analysis to be efficiently focused onto the most surprising subsets of the incoming data. 1.
Two views of belief: Belief as generalized probability and belief as evidence
, 1992
"... : Belief functions are mathematical objects defined to satisfy three axioms that look somewhat similar to the Kolmogorov axioms defining probability functions. We argue that there are (at least) two useful and quite different ways of understanding belief functions. The first is as a generalized prob ..."
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Cited by 72 (12 self)
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: Belief functions are mathematical objects defined to satisfy three axioms that look somewhat similar to the Kolmogorov axioms defining probability functions. We argue that there are (at least) two useful and quite different ways of understanding belief functions. The first is as a generalized probability function (which technically corresponds to the inner measure induced by a probability function). The second is as a way of representing evidence. Evidence, in turn, can be understood as a mapping from probability functions to probability functions. It makes sense to think of updating a belief if we think of it as a generalized probability. On the other hand, it makes sense to combine two beliefs (using, say, Dempster's rule of combination) only if we think of the belief functions as representing evidence. Many previous papers have pointed out problems with the belief function approach; the claim of this paper is that these problems can be explained as a consequence of confounding the...
Online Bayes Point Machines
"... We present a new and simple algorithm for learning large margin classi ers that works in a truly online manner. The algorithm generates a linear classi er by averaging the weights associated with several perceptronlike algorithms run in parallel in order to approximate the Bayes point. A rand ..."
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Cited by 69 (3 self)
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We present a new and simple algorithm for learning large margin classi ers that works in a truly online manner. The algorithm generates a linear classi er by averaging the weights associated with several perceptronlike algorithms run in parallel in order to approximate the Bayes point. A random subsample of the incoming data stream is used to ensure diversity in the perceptron solutions. We experimentally study the algorithm's performance on online and batch learning settings.