For these algorithms, use of a suboptimal elimination order leads to inefficiency, and improving Breadth-first and depth-first search are basic search an elimination order by even small amount can result in strategies upon which many other search algorithms large computational savings. Solving the treewidth problem are built. In this paper, we describe an approach exactly, and finding an optimal elimination order, allows these to integrating these two strategies in a single algorithms to run as efficiently as possible.

Kidneys are the most prevalent organ transplants, but demand dwarfs supply. Kidney exchanges enable willing but incompatible donor-patient pairs to swap donors. These swaps can include cycles longer than two pairs as well, and chains triggered by altruistic donors. Current kidney exchanges address clearing(deciding who gets kidneys from whom) as an offline problem: they optimize the current batch. In reality, clearing is an online problem where patient-donor pairs and altruistic donors appear and expire over time. In this paper, we study trajectory-based online stochastic optimization algorithms (which use a recent scalable optimal offline solver as a subroutine) for this. We identify tradeoffs in these algorithms between different parameters. We also uncover the need to set the batch size that the algorithms consider an atomic unit. We develop an experimental methodology for setting these parameters, and conduct experiments on real and generated data. We adapt the REGRETS algorithm of Bent and van Hentenryck for the setting. We then develop a better algorithm. We also show that the AMSAA algorithm of Mercier and van Hentenryck does not scale to the nationwide level. Our best online algorithm saves significantly more lives than the current practice of solving each batch separately.

The MAP (maximum a posteriori assignment) problem in Bayesian networks is the problem of finding the most probable instantiation of a set of variables given partial evidence for the remaining variables. The state-of-the-art exact solution method is depth-first branch-and-bound search using dynamic variable ordering and a jointree upper bound proposed by Park and Darwiche [2003]. Since almost all search time is spent computing the jointree bounds, we introduce an efficient method for computing these bounds incrementally. We point out that, using a static variable ordering, it is only necessary to compute relevant upper bounds at each search step, and it is also possible to cache potentials of the jointree for efficient backtracking. Since the jointree computation typically produces bounds for joint configurations of groups of variables, our method also instantiates multiple variables at each search step, instead of a single variable, in order to reduce the number of times that upper bounds need to be computed. Experiments show that this approach leads to orders of magnitude reduction in search time.

Systematic search and local search paradigms for combinatorial problems are generally believed to have complementary strengths. Nevertheless, attempts to combine the power of the two paradigms have had limited success, due in part to the expensive information communication overhead involved. We propose a hybrid strategy based on shared memory, ideally suited for multi-core processor architectures. This method enables continuous information exchange between two solvers without slowing down either of the two. Such a hybrid search strategy is surprisingly effective, leading to substantially better quality solutions to many challenging Maximum Satisfiability (MaxSAT) instances than what the current best exact or heuristic methods yield, and it often achieves this within seconds. This hybrid approach is naturally best suited to MaxSAT instances for which proving unsatisfiability is already hard; otherwise the method falls back to pure local search.

Recently, Richter et al. [2008] proposed a novel some point in every solution plan. Previous work way of using a set of landmarks as a pseudo-heuristic within has very successfully exploited planning landmarks a satisficing heuristic search. This technique allowed both in satisficing (non-optimal) planning. We propose a reducing the length of the generated plans, as well as improving methodology for deriving admissible heuristic estimates success rate both with respect to the iterative approach for cost-optimal planning from a set of planning of Hoffmann et al., and with respect to other stateof-the-art landmarks. The resulting heuristics fall into a satisficing planners. In particular, the LAMA planner novel class of multi-path dependent heuristics, and by Richter and Westphal utilizing such a landmarks-based we present a simple best-first search procedure exploiting heuristic search was the clear winner of the Sequential Satisficing such heuristics.

Optimization in graphical models is an important problem which has been studied in many AI frameworks such as weighted CSP, maximum satisfiability or probabilistic networks. By identifying conditionally independent subproblems, which are solved independently and whose optimum is cached, recent Branch and Bound algorithms offer better asymptotic time complexity. But the locality of bounds induced by decomposition often hampers the practical effects of this result because subproblems are often uselessly solved to optimality. Following the Russian Doll Search (RDS) algorithm, a possible approach to overcome this weakness is to (inductively) solve a relaxation of each subproblem to strengthen bounds. The algorithm obtained generalizes both RDS and tree-decomposition based algorithms such as BTD or AND-OR Branch and Bound. We study its efficiency on different problems, closing a very hard frequency assignment instance which has been open for more than 10 years.

In many scenarios, quickly solving a relatively small search problem with an arbitrary start and arbitrary goal state is important (e.g., GPS navigation). In order to speed this process, we introduce a new class of memory-based heuristics, called true distance heuristics, that store true distances between some pairs of states in the original state space can be used for a heuristic between any pair of states. We provide a number of techniques for using and improving true distance heuristics such that most of the benefits of the all-pairs shortest-path computation can be gained with less than 1% of the memory. Experimental results on a number of domains show a 6-14 fold improvement in search speed compared to traditional heuristics.

P. This compilation, however, is linear in the number of possible initial states that is exponential in the number of fluents. The problem of planning in the presence of sensing We show nonetheless that even in such cases, a sound, has been addressed in recent years as a nondeterministic complete, and polynomial translation X(P) is possible, provided search problem in belief space. In this that the problem P has bounded contingent width, and work, we use ideas advanced recently for compiling show that the contingent width of almost all existing benchmarks conformant problems into classical ones for introducing is 1; a result that parallels the one reported by Palacios a different approach where contingent problems and Geffner for conformant planning. We then show how the P are mapped into non-deterministic problems non-deterministic but fully observable problem X(P) can be X(P) in state space.

Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have  deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we first present an algorithm for finding optimal procurement solutions, as well as a heuristic algorithm that achieves over 99% of the optimal and is capable of handling thousands of providers. Using experiments, we show that these algorithms achieve an improvement of up to 130% over current strategies that procure only single services. Finally, we consider settings where service costs are not known to the consumer, and introduce several mechanisms that incentivise providers to reveal their costs truthfully and that still achieve up to 95% efficiency.

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 reducing the branching factor of its search space. The aim of our clustering algorithm is to construct a collection of sets such that branching on these sets will still allow effective bounding. In conjunction with the reduced branching factor, the size of the explored search space is thus significantly reduced. We test our method and show empirically that it can yield significant performance gains over existing stateof- the-art techniques.