255 research outputs found
Abstracting soft constraints: framework, properties, examples
Soft constraints are very and expressive. However, they also are very complex to handle. For this reason, it may be reasonable in several cases to pass to an abstract version of a given soft constraint problem, and then to bring some useful information from the abstract problem to the concrete one. This will hopefully make the search for a solution, or for an optimal solution, of the concrete problem, faster. In this paper we propose an abstraction scheme for soft constraint problems and we study its main properties. We show that processing the abstracted version of a soft constraint problem can help us in finding good approximations of the optimal solutions, or also in obtaining information that can make the subsequent search for the best solution easier. We also show how the abstraction scheme can be used to devise new hybrid algorithms for solving soft constraint problems, and also to import constraint propagation algorithms from the abstract scenario to the concrete one. This may be useful when we don\u27t have any (or any efficient) propagation algorithm in the concrete setting
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Extensible graphical game generator
Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2000.Vita.Includes bibliographical references (leaves 162-167).An ontology of games was developed, and the similarities between games were analyzed and codified into reusable software components in a system called EGGG, the Extensible Graphical Game Generator. By exploiting the similarities between games, EGGG makes it possible for someone to create a fully functional computer game with a minimum of programming effort. The thesis behind the dissertation is that there exist sufficient commonalities between games that such a software system can be constructed. In plain English, the thesis is that games are really a lot more alike than most people imagine, and that these similarities can be used to create a generic game engine: you tell it the rules of your game, and the engine renders it into an actual computer game that everyone can play.by Jon Orwant.Ph.D
Towards 40 years of constraint reasoning
Research on constraints started in the early 1970s. We are approaching 40 years since the beginning of this successful field, and it is an opportunity to revise what has been reached. This paper is a personal view of the accomplishments in this field. We summarize the main achievements along three dimensions: constraint solving, modelling and programming. We devote special attention to constraint solving, covering popular topics such as search, inference (especially arc consistency), combination of search and inference, symmetry exploitation, global constraints and extensions to the classical model. For space reasons, several topics have been deliberately omitted.Partially supported by the Spanish project TIN2009-13591-C02-02 and Generalitat de Catalunya grant 2009-SGR-1434.Peer Reviewe
Comparing Optimization Methods for Radiation Therapy Patient Scheduling using Different Objectives
Radiation therapy (RT) is one of the most common technologies used to treat
cancer. To better use resources in RT, optimization models can be used to
automatically create patient schedules, a task that today is done manually in
almost all clinics. This paper presents a comprehensive study of different
optimization methods for modeling and solving the RT patient scheduling
problem. The results can be used as decision support when implementing an
automatic scheduling algorithm in practice. We introduce an Integer Linear
Programming (IP) model, a column generation IP model (CG-IP), and a Constraint
Programming model. Patients are scheduled on multiple machine types considering
their priority for treatment, session duration and allowed machines, while
taking expected future patient arrivals into account. Different cancer centers
may have different scheduling objectives, and therefore each model is solved
using multiple different objective functions, including minimizing waiting
times, and maximizing the fulfillment of patients' preferences for treatment
times. The test data is generated from historical data from Iridium Netwerk, a
large cancer center in Belgium with 10 linear accelerators. The results
demonstrate that the CG-IP model can solve all the different problem instances
to a mean optimality gap of less than 1% within one hour. The proposed
methodology provides a tool for automated scheduling of RT treatments and can
be generally applied to RT centers.Comment: 20 pages, 4 figures, Submitted to Operations Research Foru
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