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22
Bayesian Networks Without Tears
 AI MAGAZINE
, 1991
"... I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesia ..."
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Cited by 235 (2 self)
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I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AIuncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
Planning Under Time Constraints in Stochastic Domains
 ARTIFICIAL INTELLIGENCE
, 1993
"... We provide a method, based on the theory of Markov decision processes, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future reward ..."
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Cited by 163 (19 self)
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We provide a method, based on the theory of Markov decision processes, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards. Standard goals of achievement, as well as goals of maintenance and prioritized combinations of goals, can be specified in this way. An optimal policy can be found using existing methods, but these methods require time at best polynomial in the number of states in the domain, where the number of states is exponential in the number of propositions (or state variables). By using information about the starting state, the reward function, and the transition probabilities of the domain, we restrict the planner's attention to a set of world states that are likely to be encountered in satisfying the goal. Using this restricted set of states, the planner can generate more or less complete ...
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 160 (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...
Planning With Deadlines in Stochastic Domains
 In Proceedings of the Eleventh National Conference on Artificial Intelligence
, 1993
"... We provide a method, based on the theory of Markov decision problems, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards. S ..."
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Cited by 138 (10 self)
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We provide a method, based on the theory of Markov decision problems, for efficient planning in stochastic domains. Goals are encoded as reward functions, expressing the desirability of each world state; the planner must find a policy (mapping from states to actions) that maximizes future rewards. Standard goals of achievement, as well as goals of maintenance and prioritized combinations of goals, can be specified in this way. An optimal policy can be found using existing methods, but these methods are at best polynomial in the number of states in the domain, where the number of states is exponential in the number of propositions (or state variables) . By using information about the starting state, the reward function, and the transition probabilities of the domain, we can restrict the planner's attention to a set of world states that are likely to be encountered in satisfying the goal. Furthermore, the planner can generate more or less complete plans depending on the time it has avail...
Rationality and its Roles in Reasoning
 Computational Intelligence
, 1994
"... The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, in ..."
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Cited by 109 (4 self)
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The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. 1 Introduction People make judgments of rationality all the time, usually in criticizing someone else's thoughts or deeds as irrational, or in defending their own as rational. Artificial intelligence researchers construct systems and theories to perform or describe rational thought and action, criticizing and defending these systems and theories in terms similar to but more formal than those of the man or woman on the street. Judgments of human rationality commonly involve several different conceptions of rationality, including a logical conception used to judge thoughts, and an economic one used to judge actions or...
Provably BoundedOptimal Agents
 Journal of Artificial Intelligence Research
, 1995
"... Since its inception, artificial intelligence has relied upon a theoretical foundation centred around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a ..."
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Cited by 79 (1 self)
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Since its inception, artificial intelligence has relied upon a theoretical foundation centred around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the field. We propose instead a property called bounded optimality. Roughly speaking, an agent is boundedoptimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of realtime environments. We illustrate these results using a simple model of an automated mail sorting facility. We also define a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity th...
HeuristicBiased Stochastic Sampling
 In Proceedings of the Thirteenth National Conference on Artificial Intelligence
, 1996
"... This paper presents a search technique for scheduling problems, called HeuristicBiased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic ..."
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Cited by 77 (0 self)
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This paper presents a search technique for scheduling problems, called HeuristicBiased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the realworld problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search. Introducing HBSS This paper presents a search technique, called HeuristicBiased Stochastic Sampling (HBSS), that was design...
A Bayesian Approach to Relevance in Game Playing
 ARTIFICIAL INTELLIGENCE
, 1997
"... The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational e ..."
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Cited by 35 (0 self)
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The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational efficiency of the alphabeta algorithm. Our approach is to form a Bayesian model of our uncertainty. We adopt an evaluation function that returns a probability distribution estimating the probability of various errors in valuing each position. These estimates are obtained by training from data. We thus use additional information at each leaf not available to the standard approach. We utilize this information in three ways: to evaluate which move is best after we are done expanding, to allocate additional thinking time to moves where additional time is most relevant to game outcome, and, perhaps most importantly, to expand the tree along the most relevant lines. Our measure of the relevan...
How to Solve It Automatically: Selection Among ProblemSolving Methods
 Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems
, 1998
"... The choice of an appropriate problemsolving method, from available methods, is a crucial skill for experts in many areas. We describe a technique for the automatic selection among methods, which is based on a statistical analysis of their past performances. We formalize the statistical problem ..."
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Cited by 23 (0 self)
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The choice of an appropriate problemsolving method, from available methods, is a crucial skill for experts in many areas. We describe a technique for the automatic selection among methods, which is based on a statistical analysis of their past performances. We formalize the statistical problem involved in selecting an efficient problemsolving method, derive a solution to this problem, and describe a methodselection algorithm. The algorithm not only chooses among available methods, but also decides when to abandon the chosen method, if it proves to take too much time. We give empirical results on the use of this technique in selecting among search engines in the PRODIGY planning system. 1 Introduction The choice of an appropriate problemsolving method is one of the main themes of Polya's famous book How to Solve It (Polya 1957). Polya showed that the selection of an effective approach to a problem is a crucial skill for a mathematician. Psychologists have accumulated m...