Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Backtrack programming techniques
Communications of the ACM
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Generalized disjunctive constraint propagation for solving the job shop problem with time lags
Engineering Applications of Artificial Intelligence
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The areas of Artificial Intelligence (AI) planning and scheduling have seen important advances thanks to the application of constraint satisfaction models and techniques. Most real-world problems are typically known as highly coupled planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. Therefore, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints; i.e., solutions to these problems must integrate resource allocation and plan synthesis capabilities, which can be efficiently managed by using constraint techniques. This special issue of Engineering Applications of Artificial Intelligence on ''Constraint Satisfaction Techniques for Planning and Scheduling Problems'' compiles a selection of papers of COPLAS'07: the joint CP/ICAPS-2007 workshop of the same name and presents novel issues on planning, scheduling, constraint programming/constraint satisfaction problems (CSPs) and many other common areas that exist among them. On the whole, this issue mainly focus on managing complex problems where planning, scheduling and constraint satisfaction must be combined and/or interrelated, which entails an enormous potential for practical applications and future research.