The Constraint Problems usually addressed fall into one of two models: the Constraint Satisfaction Problem (CSP) and the Constraint Optimization Problem (COP). However, in many real-life applications, more functions should be optimized at the same time, and solutions are ranked by means of a Partial Order. Also, when dealing with Null values, the solution set is ranked through a Partial Order, ...

Branch and bound is an effective technique for solving constraint optimization problems (COP’s). However, its search space expands very rapidly as the domain sizes of the problem variables grow. In this paper, we present an algorithm that clusters the values of a variable’s domain into sets. Branch and bound can then branch on these sets of values rather than on individual values, thereby reduc...

Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the stand...

Current approaches that model dynamism in DCOPs solve a sequence of static problems, reacting to changes in the environment as the agents observe them. Such approaches thus ignore possible predictions on future changes. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to r...

This work considers implementation of requirements expressed as High-level Message Sequence Charts (HMSCs). All HMSCs are not implementable, but a particular subclass called local HMSCs can be implemented using a simple projection operation. This paper proposes a new technique to transform an arbitrary HMSC specification into a local HMSC, hence allowing implementation. We show that this transf...

In these lectures I will present an introduction to the results that have been recently obtained in constraint optimization of random problems using statistical mechanics techniques. After presenting the general results, in order to simplify the presentation I will describe in details only the problems related to the coloring of a random graph.

We present in this paper an incentive-compatible distributed optimization method applied to social choice problems. The method works by computing and collecting VCG taxes in a distributed fashion. This introduces a certain resilience to manipulation from the problem solving agents. An extension of this method sacrifices Pareto-optimality in favor of budget-balance: the solutions chosen are not ...

My thesis proposes novel schemes for achieving privacy in constraint optimization and new ways of analyzing the privacy properties of constraint optimization algorithms. Thus, it aims to contribute to the fields of constraint processing and multiagent systems. Motivation: Many problem domains within constraint processing require algorithms that better accomodate the need for privacy. Two salien...

Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction and optimization problems (DisCSPs and DCOPs). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the relation of global cons...

The problem of finding the set of pareto optimals for constraints and qualitative preferences together is of great interest to many application areas. It can be viewed as a preference constrained optimization problem where the goal is to find one or more feasible solutions that are not dominated by other feasible outcomes. Our work aims to enhance the current literature of the problem by provid...