We consider the problem of determining a most preferred path from a start node to a goal node set in an acyclic OR-graph, given a multiattribute preference function, a multiobjective reward structure, and heuristic information about this reward structure. We present an algorithm which is shown to terminate with a most preferred path, given an admissible heuristic set. The algorithm illustrates how Artificial Intelligence techniques can be productively employed to solve multiobjective problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30207/1/0000597.pd
Graphical models are widely used to model complex interactions between variables. A graphical model ...
International audienceComparison of solutions in combinatorial problems is often based on an additiv...
ABSTRACT: In this thesis we investigate path finding problems, that is, plan-ning routes from a star...
Abstract: We consider the problem of determining a most preferred path from a start node to a goal n...
The multiobjective search model is a framework for solving multi-criteria optimization problems usin...
In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. Thes...
In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. Thes...
The aim of this paper is to propose a new approach interweaving preference elicitation and search to...
In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. Thes...
Multiobjective heuristic search in state space graphs generally aims at determining the exact set of...
International audienceWe propose an introduction to the use of incremental preference elicita-tion m...
This paper extends the multicriteria decision paradigm to the heuristic search domain in a systemati...
Preference-based Evolutionary Multiobjective Optimization (EMO) algorithms approximate the region of...
Building on previous work of the authors, this paper formally defines and reviews the first approach...
Heuristic search is used to efficiently solve the single-node shortest path problem in weighted gra...
Graphical models are widely used to model complex interactions between variables. A graphical model ...
International audienceComparison of solutions in combinatorial problems is often based on an additiv...
ABSTRACT: In this thesis we investigate path finding problems, that is, plan-ning routes from a star...
Abstract: We consider the problem of determining a most preferred path from a start node to a goal n...
The multiobjective search model is a framework for solving multi-criteria optimization problems usin...
In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. Thes...
In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. Thes...
The aim of this paper is to propose a new approach interweaving preference elicitation and search to...
In multiobjective state space graph problems, each solution-path is evaluated by a cost vector. Thes...
Multiobjective heuristic search in state space graphs generally aims at determining the exact set of...
International audienceWe propose an introduction to the use of incremental preference elicita-tion m...
This paper extends the multicriteria decision paradigm to the heuristic search domain in a systemati...
Preference-based Evolutionary Multiobjective Optimization (EMO) algorithms approximate the region of...
Building on previous work of the authors, this paper formally defines and reviews the first approach...
Heuristic search is used to efficiently solve the single-node shortest path problem in weighted gra...
Graphical models are widely used to model complex interactions between variables. A graphical model ...
International audienceComparison of solutions in combinatorial problems is often based on an additiv...
ABSTRACT: In this thesis we investigate path finding problems, that is, plan-ning routes from a star...