This problem involves imprecise probability judgements, widening the scope of application areas. In fact, there is a In this paper, we study algorithms for probabilistic large number of potential application areas for PSAT, from satisfiability (PSAT), an NPcomplete problem, machine learning to the modelling of biological processes, and their empiric complexity distribution. We define from hardware and software verification to economics and a PSAT normal form, based on which we propose econometrics. However, there are very few, if any, practical two logic-based algorithms: a reduction of algorithms available, used in limited applications.

We examine the complexity of constraint satisfaction problems that consist of a set of AllDiff constraints. Such CSPs naturally model a wide range of real-world and combinatorial problems, like scheduling, frequency allocations and graph coloring problems. As this problem is known to be NP-complete, we investigate under which further assumptions it becomes tractable. We observe that a crucial property seems to be the convexity of the variable domains and constraints. Our main contribution is an extensive study of the complexity of Multiple AllDiff CSPs for a set of natural parameters, like maximum domain size and maximum size of the constraint scopes. We show that, depending on the parameter, convexity can make the problem tractable while it is provably intractable in general.

Lazy clause generation is a powerful approach to reducing search in constraint programming. This is achieved by recording sets of domain restrictions that previously led to failure as new clausal propagators. Symmetry breaking approaches are also powerful methods for reducing search by recog- nizing that parts of the search tree are symmetric and do not need to be explored. In this paper we show how we can successfully combine symmetry breaking methods with lazy clause generation. Further, we show that the more precise nogoods generated by a lazy clause solver allow our combined approach to exploit redundancies that cannot be exploited via any previous symmetry breaking method, be it static or dynamic.

Such problems are that each relation R can be defined by a Boolean combination frequently intractable, but there are several important of equations over the signature,, andc, which are set CSPs that are known to be polynomial-time function symbols for intersection, union, and complementation, tractable. We introduce a large class of set CSPs respectively. Details of the formal definition and many that can be solved in quadratic time. Our class, examples of set constraint languages can be found in Section which we call EI, contains all previously known 3. The choice of N is just for notational convenience; tractable set CSPs, but also some new ones that as we will see, we could have selected any infinite set for are of crucial importance for example in description our purposes. In the following, a set constraint satisfaction logics. The class of EI set constraints has an problem (set CSP) is a problem of the form CSP(Γ) for a elegant universal-algebraic characterization, which set constraint language Γ. It has been shown by Marriott and we use to show that every set constraint language Odersky [Marriott and Odersky, 1996] that all set CSPs are that properly contains all EI set constraints already contained in NP; they also showed that the largest set constraint has a finite sublanguage with an NPhard constraint language, which consists of all relations that can be satisfaction problem.

We develop a novel circuit-level stochastic local search (SLS) method D-CRSat for Boolean satisfiability by integrating a structure-based heuristic into the recent CRSat algorithm. D-CRSat significantly improves on CRSat on real-world application benchmarks on which other current CNF and circuit-level SLS methods tend to perform weakly. We also give an intricate proof of probabilistically approximate completeness for D-CRSat, highlighting key features of the method.

The Partner Units Problem is a specific type of configuration problem with important applications in the area of surveillance and security. In this work we show that a special case of the problem, that is of great interest to our partners in industry, can directly be tackled via a structural problem decompostion method. Combining these theoretical insights with general purpose AI techniques such as constraint satisfaction and SAT solving proves to be particularly effective in practice.

Multi-Agent Plan Recognition (MAPR) seeks to proposed to formalize MAPR with a new model, revealing identify the dynamic team structures and team behaviors the distinction between the hardness of single and multi-agent from the observed activity sequences (team plan recognition, and solve MAPR problems in the model using traces) of a set of intelligent agents, based on a a first-cut approach, provided that a fully observed team library of known team activity sequences (team trace and a library of full team plans were given as input plans). Previous MAPR systems require that team [Banerjee et al., 2010]; etc. traces and team plans are fully observed. In this Despite the success of previous approaches, they either assume paper we relax this constraint, i.e., team traces and that agents in the same team can only execute a common team plans are allowed to be partial. This is an important activity, i.e., coordinated activities of agents are not allowed task in applying MAPR to real-world domains, in a team, or require that the team trace and team plans are since in many applications it is often difficult complete, i.e., missing values (activities that are missing) are to collect full team traces or team plans due not allowed. In many real-world applications, however, it is to environment limitations, e.g., military operation.

We study distributed task-allocation problems wherecooperative agents need to perform some tasks simultaneously. Examples are multi-agent routing problems where several agents need to visit some targets simultaneously, for example, to move obstacles out of the way cooperatively. In this paper, we first generalize the concept of reaction functions proposed in the literature to characterize the agent costs of performing multiple complex tasks. Second, we show how agents can construct and approximate reaction functions in a distributed way. Third, we show how reaction functions can be used by an auction-like algorithm to allocate tasks to agents. Finally, we show empirically that the team costs of our algorithms are substantially smaller than those of an existing state-of-the-art allocation algorithm for complex tasks.

This paper examines an extended double auction model where market clearing is restricted by temporal constraints. It is found that the allocation problem in this model can be effectively transformed into a weighted bipartite matching in graph theory. By using the augmentation technique, we propose a Vickrey-Clarke-Groves (VCG) mechanism in this model and demonstrate the advantages of the payment compared with the classical VCG payment (the Clarke pivot payment). We also show that the algorithms for both allocation and payment calculation run in polynomial time. It is expected that the method and results provided in this paper can be applied to the design and analysis of dynamic double auctions and futures markets.

Continuous state DEC-MDPs are critical for agent teams in domains involving resources such as time, but scaling them up is a significant challenge. To meet this challenge, we first introduce a novel continuous-time DEC-MDP model that exploits transition independence in domains with temporal constraints. Moreimportantly, we present a new locally optimal algorithm called SPAC. Compared to the best previous algorithm, SPAC finds solutions of comparable quality substantially faster; SPAC also scales to larger teams of agents.