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The Witness Algorithm: Solving Partially Observable Markov Decision Processes
, 1994
"... This paper describes the POMDP framework and presents some wellknown results from the field. It then presents a novel method called the witness algorithm for solving POMDP problems and analyzes its computational complexity. We argue that the witness algorithm is superior to existing algorithms for s ..."
Abstract

Cited by 53 (3 self)
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This paper describes the POMDP framework and presents some wellknown results from the field. It then presents a novel method called the witness algorithm for solving POMDP problems and analyzes its computational complexity. We argue that the witness algorithm is superior to existing algorithms for solving POMDP's in an important complexitytheoretic sense.
Multicriteria Reinforcement Learning
, 1998
"... We consider multicriteria sequential decision making problems where the vectorvalued evaluations are compared by a given, fixed total ordering. Conditions for the optimality of stationary policies and the Bellman optimality equation are given. The analysis requires special care as the topology int ..."
Abstract

Cited by 34 (0 self)
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We consider multicriteria sequential decision making problems where the vectorvalued evaluations are compared by a given, fixed total ordering. Conditions for the optimality of stationary policies and the Bellman optimality equation are given. The analysis requires special care as the topology introduced by pointwise convergence and the ordertopology introduced by the preference order are in general incompatible. Reinforcement learning algorithms are proposed and analyzed. Preliminary computer experiments confirm the validity of the derived algorithms. It is observed that in the mediumterm multicriteria RL often converges to better solutions (measured by the first criterion) than their singlecriterion counterparts. These type of multicriteria problems are most useful when there are several optimal solutions to a problem and one wants to choose the one among these which is optimal according to another fixed criterion. Example applications include alternating games, when in addition...
Balking and reneging in M/G/s systems exact analysis and approximations
 Probability in the Engineering and Informational Sciences
, 2008
"... We consider the virtual queueing time (vqt, also known as workinsystem, or virtualdelay) process in an M/G/s queue with impatient customers. We focus on the vqtbased balking model and relate it to reneging behavior of impatient customers in terms of the steadystate distribution of the vqt proc ..."
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Cited by 1 (0 self)
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We consider the virtual queueing time (vqt, also known as workinsystem, or virtualdelay) process in an M/G/s queue with impatient customers. We focus on the vqtbased balking model and relate it to reneging behavior of impatient customers in terms of the steadystate distribution of the vqt process. We construct a singleserver system, analyze its operating characteristics, and use them to approximate the multiserver system. We give both analytical results and numerical examples. We conduct simulation to assess the accuracy of the approximation. 1
Optimal Management of Groundwater under Uncertainty: A
, 2014
"... The Centre for Environmental and Resource Economics (CERE) is an inter‐disciplinary and inter‐university research centre at the Umeå Campus: Umeå University and the Swedish University of Agricultural Sciences. The main objectives with the Centre are to tie together research groups at the different d ..."
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The Centre for Environmental and Resource Economics (CERE) is an inter‐disciplinary and inter‐university research centre at the Umeå Campus: Umeå University and the Swedish University of Agricultural Sciences. The main objectives with the Centre are to tie together research groups at the different departments and universities; provide seminars and workshops within the field of environmental & resource economics and management; and constitute a platform for a creative and strong research environment within the field.
Multicriteria Reinforcement Learning
, 1998
"... We consider multicriteria sequential decision making problems where the vectorvalued evaluations are compared by a given, fixed total ordering. Conditions for the optimality of stationary policies and the Bellman optimality equation are given. The analysis requires special care as the topology int ..."
Abstract
 Add to MetaCart
We consider multicriteria sequential decision making problems where the vectorvalued evaluations are compared by a given, fixed total ordering. Conditions for the optimality of stationary policies and the Bellman optimality equation are given. The analysis requires special care as the topology introduced by pointwise convergence and the ordertopology introduced by the preference order are in general incompatible. Reinforcement learning algorithms are proposed and analyzed. Preliminary computer experiments confirm the validity of the derived algorithms. It is observed that in the mediumterm multicriteria RL often converges to better solutions (measured by the first criterion) than their singlecriterion counterparts. These type of multicriteria problems are most useful when there are several optimal solutions to a problem and one wants to choose the one among these which is optimal according to another fixed criterion. Example applications include alternating games, when in addition...