Many decision problems contain, in some form, a NP-hard combinatorial problem. Therefore decision support systems have to solve such combinatorial problems in a reasonable time. Many combinatorial problems can be solved by a search method. The search methods used in decision support systems have to be robust in the sense that they can handle a large variety of (user defined) constraints and that they allow user interaction, i.e. they allow a decision maker to control the search process manually. In this paper we show how Markov decision processes can be used to guide a random search process. We first formulate search problems as a special class of Markov decision processes such that the search space of a search problem is the state space of...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
Randomized Search heuristics are frequently applied to NP-hard combinatorial optimization problems. ...
Many decision problems contain, in some form, a NP-hard combinatorial problem. Therefore decision su...
We propose a heuristic search algorithm for finding optimal policies in a new class of sequential de...
The quality of solution provided by a search heuristic on a particular problem is by no means an abs...
AbstractThis paper analyzes the performance of local search algorithms (guided by the best-to-date s...
International audienceWe investigate the classical active pure exploration problem in Markov Decisio...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
AbstractThe performance, on a given problem, of search heuristics such as simulated annealing and de...
We describe a heuristic search algorithm for Markov decision problems, called LAO*, that is derived ...
The search for finite-state controllers for partially observable Markov decision processes (POMDPs) ...
AbstractClassic heuristic search algorithms can find solutions that take the form of a simple path (...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
Randomized Search heuristics are frequently applied to NP-hard combinatorial optimization problems. ...
Many decision problems contain, in some form, a NP-hard combinatorial problem. Therefore decision su...
We propose a heuristic search algorithm for finding optimal policies in a new class of sequential de...
The quality of solution provided by a search heuristic on a particular problem is by no means an abs...
AbstractThis paper analyzes the performance of local search algorithms (guided by the best-to-date s...
International audienceWe investigate the classical active pure exploration problem in Markov Decisio...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
Markov Decision Problems (MDPs) are the foundation for many problems that are of interest to researc...
AbstractThe performance, on a given problem, of search heuristics such as simulated annealing and de...
We describe a heuristic search algorithm for Markov decision problems, called LAO*, that is derived ...
The search for finite-state controllers for partially observable Markov decision processes (POMDPs) ...
AbstractClassic heuristic search algorithms can find solutions that take the form of a simple path (...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
Randomized Search heuristics are frequently applied to NP-hard combinatorial optimization problems. ...