Stochastic Constraint Programming

To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.Comment: Proceedings of the 15th Eureopean Conference on Artificial Intelligenc

Soft Concurrent Constraint Programming

Soft constraints extend classical constraints to represent multiple consistency levels, and thus provide a way to express preferences, fuzziness, and uncertainty. While there are many soft constraint solving formalisms, even distributed ones, by now there seems to be no concurrent programming framework where soft constraints can be handled. In this paper we show how the classical concurrent constraint (cc) programming framework can work with soft constraints, and we also propose an extension of cc languages which can use soft constraints to prune and direct the search for a solution. We believe that this new programming paradigm, called soft cc (scc), can be also very useful in many web-related scenarios. In fact, the language level allows web agents to express their interaction and negotiation protocols, and also to post their requests in terms of preferences, and the underlying soft constraint solver can find an agreement among the agents even if their requests are incompatible.Comment: 25 pages, 4 figures, submitted to the ACM Transactions on Computational Logic (TOCL), zipped file

Finite domain constraint programming systems

Tutorial at CP'2002, Principles and Practice of Constraint Programming. Powerpoint slides.</p

Concurrent constraint programming with process mobility

We propose an extension of concurrent constraint programming with primitives for process migration within a hierarchical network, and we study its semantics. To this purpose, we first investigate a &quot;pure &quot; paradigm for process migration, namely a paradigm where the only actions are those dealing with transmissions of processes. Our goal is to give a structural definition of the semantics of migration; namely, we want to describe the behaviour of the system, during the transmission of a process, in terms of the behaviour of the components. We achieve this goal by using a labeled transition system where the effects of sending a process, and requesting a process, are modeled by symmetric rules (similar to handshaking-rules for synchronous communication) between the two partner nodes in the network. Next, we extend our paradigm with the primitives of concurrent constraint programming, and we show how to enrich the semantics to cope with the notions of environment and constraint store. Finally, we show how the operational semantics can be used to define an interpreter for the basic calculus.