Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Graph coloring is also used to model more traditional CSPs relevant to AI, such as planning, time-tabling and scheduling. Provably optimal solutions may be desirable for commercial and defense applications. Additionally, for applications such as register allocation and code optimization, naturally-occurring instances of graph coloring are often small and can be solved optimally. A recent wave of improvements in algorithms for Boolean satisfiability (SAT) and 0-1 Integer Linear Programming (ILP) suggests generic problem-reduction methods, rather than problem-specific heuristics, because (1) heuristics may be upset by new constraints, (2) heuristics tend to ignore structure, and (3) many relevant problems are provably inapproximable. Problem reductions often lead to highly symmetric SAT instances, and symmetries are known to slow down SAT solvers. In this work, we compare several avenues for symmetry breaking, in particular when certain kinds of symmetry are present in all generated instances. Our focus on reducing CSPs to SAT allows us to leverage recent dramatic improvement in SAT solvers and automatically benefit from future progress. We can use a variety of black-box SAT solvers without modifying their source code because our symmetry-breaking techniques are static, i.e., we detect symmetries and add symmetry breaking predicates (SBPs) during pre-processing. An important result of our work is that among the types of instance-independent SBPs we studied and their combinations, the simplest and least complete constructions are the most effective. Our experiments also clearly indicate that instance-independent symmetries should mostly be processed together with instance-specific symmetries rather than at the specification level, contrary to what has been suggested in the literature.

This work focuses on improving state-of-the-art in stochastic local search (SLS) for solving Boolean satisfiability (SAT) instances arising from real-world industrial SAT application domains. The recently introduced SLS method CRSat has been shown to noticeably improve on previously suggested SLS techniques in solving such real-world instances by combining justification-based local search with limited Boolean constraint propagation on the non-clausal formula representation form of Boolean circuits. In this work, we study possibilities of further improving the performance of CRSat by exploiting circuit-level structural knowledge for developing new search heuristics for CRSat. To this end, we introduce and experimentally evaluate a variety of search heuristics, many of which are motivated by circuit-level heuristics originally developed in completely different contexts, e.g., for electronic design automation applications. To the best of our knowledge, most of the heuristics are novel in the context of SLS for SAT and, more generally, SLS for constraint satisfaction problems.

In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems.

Variable elimination is a general technique for constraint processing. It is often discarded because of its high space complexity. However, it can be extremely useful when combined with other techniques. In this paper we study the applicability of variable elimination to the challenging problem of finding still-lifes. We illustrate several alternatives: variable elimination as a stand-alone algorithm, interleaved with search, and as a source of good quality lower bounds. We show that these techniques are the best known option both theoretically and empirically. In our experiments we have been able to solve the n=20 instance, which is far beyond reach with alternative approaches.

In this paper we present a proposal for a knowledge-based programming environment. In such an environment, declarative background knowledge, procedures, and concrete data are represented in suitable languages and combined in a flexible manner. This leads to a highly declarative programming style. We illustrate our approach on an example and report about our prototype implementation.

In order to give appropriate semantics to qualitative conditionals of the form "if A then normally B", ordinal conditional functions (OCFs) ranking the possible worlds according to their degree of plausibility can be used. An OCF accepting all conditionals of a knowledge base R can be characterized as the solution of a constraint satisfaction problem. We present a high-level, declarative approach using constraint logic programming techniques for solving this constraint satisfaction problem. In particular, the approach developed here supports the generation of all minimal solutions; these minimal solutions are of special interest as they provide a basis for model-based inference from R.

Many important computational problems that arise in various areas of AI are intractable. Nevertheless, AI research was very successful in developing and implementing heuristic solvers that work well on realworld instances. An important component of virtually every solver is a powerful polynomial-time preprocessing procedure that reduces the problem input. For instance, preprocessing techniques for the propositional satisfiability problem are based on Boolean Constraint Propagation (see, e.g., Eén and Biere, 2005), CSP solvers make use of various local consistency algorithms that filter the domains of variables (see, e.g., Bessière, 2006); similar preprocessing methods are used by solvers for Nonmonotonic and Bayesian reasoning problems (see, e.g., Gebser et al., 2008, Bolt and van der Gaag, 2006, respectively). Until recently, no provable performance guarantees for polynomial-time preprocessing methods have been obtained, and so preprocessing was only subject of empirical studies. A possible reason for the lack of theoretical results is a certain inadequacy of the P vs NP framework for such an analysis: if we could reduce in polynomial time an instance of an NPhard problem just by one bit, then we can solve the entire problem in polynomial time by repeating the reduction step a polynomial number of times, and P NP follows. With the advent of parameterized complexity (Downey, Fellows, and Stege, 1999), a new theoretical framework became available that provides suitable tools to analyze the power of preprocessing. Parameterized complexity considers a problem in a two-dimensional setting, where in addition to the input size n, a problem parameter k is taken into consideration.

In this paper we present a full architecture to deploy efficiently a grid in a private cloud approach. We first give details about the resources constraints acquisition. We use Rich Internet Application (RIA) to access and/or modify the resources in a very user-friendly interface. Then, using the previous information, we explain how we can compute a dynamic deployment plan, that can be used either to build an optimal grid of computers or to give information to its scheduler. This plan is computed using pddl solver with various logical constraints obtained from the IT users through the RIA.

We present an original integration of high level planning and execution with incoming perceptual information from vision, SLAM, topological map segmentation and dialogue. The task of the robot system, implementing the integrated model, is to explore unknown areas and report detected objects to an operator, by speaking loudly. The knowledge base of the planner maintains a graph-based representation of the metric map that is dynamically constructed via an unsupervised topological segmentation method, and augmented with information about the type and position of detected objects, within the map, such as cars or containers. According to this knowledge the cognitive robot can infer strategies in so generating parametric plans that are instantiated from the perceptual processes. Finally, a model-based approach for the execution and control of the robot system is proposed to monitor, concurrently, the low level status of the system and the execution of the activities, in order to achieve the goal, instructed by the operator.

Mainly, we need kinds of virtualization technologies to offer on-demand to identify what parts of the model are really important and computing resources. There is widespread consensus that what other parts are secondary. Then, we would like to study the Future Internet will be heavily based on some kind of the scalability of the current models and identify the current successful Cloud technology. However, to master the deployment limits. Therefore, we propose to consider all existing of Cloud-based infrastructures, some hard scientific CP models in order to answer to these questions.