Distributed Constraint Handling and Optimization

Constraints pervade our everyday lives and are usually perceived as elements that limit solutions to the problems that we face (e.g., the choices we make everyday are typically constrained by limited money or time). However, from a computational point of view, constraints are key components for efficiently solving hard problems. In fact, constraints encode knowledge about the problem at hand, and so restrict the space of possible solutions that must be considered. By doing so, they greatly reduce the computational effort required to solve a problem. Against this background, constraint processing can be viewed as a wide and diverse research area unifying techniques and algorithms that span across many different disciplines including Planning and Scheduling, Operation Research, Computer Vision, Automated Reasoning and Decision Theory. All these areas deal with hard computational problems that can be made more tractable by carefully considering the constraints that define the structure of the problem. Here we will focus on how constraint processing can be used to address optimization problems in Multi-Agent Systems. Specifically, we will consider Distributed Constraint Optimization Problems (DCOPs) where a set of agents must