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12
Principles of Metareasoning
 Artificial Intelligence
, 1991
"... In this paper we outline a general approach to the study of metareasoning, not in the sense of explicating the semantics of explicitly specified metalevel control policies, but in the sense of providing a basis for selecting and justifying computational actions. This research contributes to a devel ..."
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Cited by 162 (10 self)
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In this paper we outline a general approach to the study of metareasoning, not in the sense of explicating the semantics of explicitly specified metalevel control policies, but in the sense of providing a basis for selecting and justifying computational actions. This research contributes to a developing attack on the problem of resourcebounded rationality, by providing a means for analysing and generating optimal computational strategies. Because reasoning about a computation without doing it necessarily involves uncertainty as to its outcome, probability and decision theory will be our main tools. We develop a general formula for the utility of computations, this utility being derived directly from the ability of computations to affect an agent's external actions. We address some philosophical difficulties that arise in specifying this formula, given our assumption of limited rationality. We also describe a methodology for applying the theory to particular problemsolving systems, a...
Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study
 In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence
, 1989
"... We define and exercise the expected value of computation as a fundamental component of reflection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reflection and action under scarce resources, and discuss how the techniques have been appli ..."
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Cited by 80 (8 self)
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We define and exercise the expected value of computation as a fundamental component of reflection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reflection and action under scarce resources, and discuss how the techniques have been applied in a highstakes medical domain. The work centers on endowing a computational agent with the ability to harness incomplete characterizations of problemsolving performance to control the amount of effort applied to a problem or subproblem, before taking action in the world or turning to another problem. We explore the use of the techniques in controlling decisiontheoretic inference itself, and pose the approach as a model of rationality under scarce resources. 1 Reflection and Flexibility Reflection about the course of problem solving and about the interleaving of problem solving and physical activity is a hallmark of intelligent behavior. Applying a portion of available reasoning resour...
Anytime heuristic search
 Journal of Artificial Intelligence Research (JAIR
, 2007
"... We describe how to convert the heuristic search algorithm A * into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the we ..."
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Cited by 37 (2 self)
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We describe how to convert the heuristic search algorithm A * into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A * algorithm, and consider its performance in three domains; slidingtile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memoryefficient search algorithm Recursive BestFirst Search (RBFS) into an anytime algorithm. 1.
Anytime Heuristic Search: First Results
, 1997
"... We describe a simple technique for converting heuristic search algorithms into anytime algorithms that offer a tradeoff between search time and solution quality. The technique is related to work on use of nonadmissible evaluation functions that make it possible to find good, but possibly subop ..."
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Cited by 23 (3 self)
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We describe a simple technique for converting heuristic search algorithms into anytime algorithms that offer a tradeoff between search time and solution quality. The technique is related to work on use of nonadmissible evaluation functions that make it possible to find good, but possibly suboptimal, solutions more quickly than it takes to find an optimal solution. Instead of
Evolving Neural Networks to Focus Minimax Search
 In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI94
, 1994
"... Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are promising enough to be explored further. The networks effectively hide problem states from minimax based on the knowledg ..."
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Cited by 21 (7 self)
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Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are promising enough to be explored further. The networks effectively hide problem states from minimax based on the knowledge they have evolved about the limitations of minimax and the evaluation function. Focus networks were encoded in markerbased chromosomes and were evolved against a fullwidth minimax opponent that used the same evaluation function. The networks were able to guide the search away from poor information, resulting in stronger play while examining fewer states. When evolved with a highly sophisticated evaluation function of the Bill program, the system was able to match Bill's performance while only searching a subset of the moves. 1 Introduction Almost all current game programs rely on the minimax search algorithm (Shannon 1950) to return the best move. Because of time and space constraints...
Decision Analytic Networks in Artificial Intelligence
, 1995
"... Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a fa ..."
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Cited by 7 (0 self)
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Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a family of graphical models of decision theory known as influence diagrams or as belief networks. These models are equally attractive to theoreticians, decision modelers, and designers of knowledgebased systems. From a theoretical perspective, they combine graph theory, probability theory and decision theory. From an implementation perspective, they lead to powerful automated systems. Although many practicing decision analysts have already adopted influence diagrams as modeling and structuring tools, they may remain unaware of the theoretical work that has emerged from the artificial intelligence community. This paper surveys the first decade or so of this work. Investment Technology Group, ...
Guided model checking with a bayesian metaheuristic
 Fundam. Inform
, 2006
"... This paper presents a formal verification algorithm for finding errors in models of concurrent systems. The algorithm improves explicit guided model checking by applying the empirical Bayes method to revise heuristic estimates of the distance from a given state to an error state. Guided search using ..."
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Cited by 4 (0 self)
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This paper presents a formal verification algorithm for finding errors in models of concurrent systems. The algorithm improves explicit guided model checking by applying the empirical Bayes method to revise heuristic estimates of the distance from a given state to an error state. Guided search using the revised estimates finds errors with less search effort than the original estimates. 1.
UtilityBased Categorization
, 1993
"... The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utilitybased approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choi ..."
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Cited by 3 (1 self)
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The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utilitybased approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choice of appropriate actions. This is in contrast to classical and similaritybased approaches which seek logical completeness in concept description with respect to sensory data rather than actionoriented e#ectiveness. Utilitybased categorization is normative and not descriptive. It prescribes howanintelligent agent ought to conceptualize to act e#ectively. It provides ideals for categorization, speci#es criteria for the design of e#ective computational agents, and provides a model of ideal competence. A decisiontheoretic framework for utilitybased categorization whichinvolves reasoning about alternative categorization models of varying levels of abstraction is proposed. Categorization mode...
Model Construction in Planning
 In Notes from the Ninth National Conference on Artificial Intelligence (AAAI91) Workshop on KnowledgeBased Construction of Probabilistic and Decision Models
, 1991
"... We view planning as a search through a space of plan models. A plan model consists of a partial description of a course of action (a plan) and a set of decision models that support analysis of the plan. In this framework, model building is focussed on the development of techniques to support the inc ..."
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Cited by 2 (0 self)
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We view planning as a search through a space of plan models. A plan model consists of a partial description of a course of action (a plan) and a set of decision models that support analysis of the plan. In this framework, model building is focussed on the development of techniques to support the incremental evaluation and construction of plans. There are special problems associated with planning that make model construnction in this context much more difficult than it might be for other applications. Since many alternative structures must be generated and evaluated while planning, exhaustive search techniques for model construction are inappropriate. We seek to develop techniques for building sparse decision structures (models) that contain enough information to allow us to make search choices without swamping the evaluator with a lot of inessential data.