Principles of Constraint Processing (original) (raw)
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Algorithms for Constraint Satisfaction Problems: A Survey, Appeared In
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A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This article overviews many of these approaches in a tutorial fashion. Articles
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The areas of planning and scheduling (from the Artificial Intelligence point of view) have seen important advances thanks to application of constraint satisfaction techniques. Currently, many important real-world problems require efficient constraint handling for planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Solutions to these problems require integration of resource
A bit of history ... Constraints have recently emerged as a research area that combines researchers from a number of fields, including Artificial Intelligence, Programming Languages, Symbolic Computing and Computational Logic. Constraint networks and constraint satisfaction problems have been studied in Artificial Intelligence starting from the seventies (Montanary, 1974), (Waltz, 1975). Systematic use of constraints in programming has started in the eighties (Gallaire, 1985), (Jaffar, Lassez, 1987). The constraint satisfaction origins from Artificial Intelligence where the problems like scene labelling was studied (Waltz, 1975). The scene labelling problem is probably the first constraint satisfaction problem that was formalised. The goal is to recognise the objects in the scene by interpreting lines in the drawings. First, the lines or edges are labelled, i.e., they are categorised into few types, namely convex (+), concave (-) and occluding edges (<). In some advanced systems, the shadow border is recognised as well. There are a lot ways how to label the scene (exactly 3 n , where n is a number of edges) but only few of them has any 3D meaning. The idea how to solve this combinatorial problem is to find legal labels for junctions satisfying the constraint that the edge has the same label at both ends. This reduces the problem a lot because there are only a very limited number of legal labels for junctions. ... and some applications. Constraint programming has been successfully applied in numerous domains. Recent applications include computer graphics (to express geometric coherence in the case of scene analysis), natural language processing (construction of efficient parsers), database systems (to ensure and/or restore consistency of the data), operations research problems (like optimisation problems), molecular biology (DNA sequencing), business applications (option trading), electrical engineering (to locate faults), circuit design (to compute layouts), etc. Current research in this area deals with various foundational issues, with implementation aspects, and with new applications of constraint programming. What does the constraint programming deal with? Constraint programming is the study of computational systems based on constraints. The idea of constraint programming is to solve problems by stating constraints (conditions, properties, requirements) which must be satisfied by the solution.
Backtracking Algorithms for Constraint Satisfaction Problems
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Over the past twenty five years many backtracking algorithms havebeen developed for constraint satisfaction problems. This survey describesthe basic backtrack search within the search space framework and thenpresents a number of improvements developed in the past two decades,including look-back methods such as backjumping, constraint recording,backmarking, and look-ahead methods such as forward checking and dynamicvariable ordering.1 IntroductionConstraint networks have proven...
2010
The area of AI planning and scheduling has seen important advances thanks to the application of constraint satisfaction and optimization techniques. Efficient constraint handling is important for real-world problems in planning, scheduling, and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Approaches to these problems must integrate resource allocation and plan synthesis capabilities. We need to manage complex problems where planning, scheduling, and constraint satisfaction must be interrelated, which entail a great potential of application. The workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems, or COPLAS, aims at providing a forum for meeting and exchanging ideas and novel works in the field of AI planning, scheduling, and constraint satisfaction techniques, and the many relationships that exist among them. In fact, most of the accepted papers are based on combined approaches of constraint satisfaction for planning, scheduling, and mixing planning and scheduling. This makes the COPLAS workshop an attractive place for both researchers and practitioners (COPLAS is ranked as CORE B in ERA Conference Ranking). The sixth edition of the workshop, COPLAS 2011, was held in June 2011 in Freiburg, Germany during the International Conference on Automated Planning and Scheduling (ICAPS'11). All the submissions were reviewed by at least three anonymous referees from the program committee. The nine papers accepted for oral presentation in the workshop, provide a mix of constraint satisfaction and optimization techniques for planning, scheduling, and related topics, as well as their applications to real-world problems. We hope that the ideas and approaches presented in the papers and presentations will lead to a valuable discussion and will inspire future research and developments for all the readers. The Organizing Committee.
Engineering Applications of Artificial Intelligence, 2008
The area of AI planning and scheduling has seen important advances thanks to the application of constraint satisfaction and optimization techniques. Efficient constraint handling is important for real-world problems in planning, scheduling, and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Approaches to these problems must integrate resource allocation and plan synthesis capabilities. We need to manage complex problems where planning, scheduling, and constraint satisfaction must be interrelated, which entail a great potential of application. The workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems, or COPLAS, aims at providing a forum for meeting and exchanging ideas and novel works in the field of AI planning, scheduling, and constraint satisfaction techniques, and the many relationships that exist among them. In fact, most of the accepted papers are based on combined approaches of constraint satisfaction for planning, scheduling, and mixing planning and scheduling. This makes the COPLAS workshop an attractive place for both researchers and practitioners (COPLAS is ranked as CORE B in ERA Conference Ranking). The sixth edition of the workshop, COPLAS 2011, was held in June 2011 in Freiburg, Germany during the International Conference on Automated Planning and Scheduling (ICAPS'11). All the submissions were reviewed by at least three anonymous referees from the program committee. The nine papers accepted for oral presentation in the workshop, provide a mix of constraint satisfaction and optimization techniques for planning, scheduling, and related topics, as well as their applications to real-world problems. We hope that the ideas and approaches presented in the papers and presentations will lead to a valuable discussion and will inspire future research and developments for all the readers. The Organizing Committee.