The stable marriage problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools, or more generally to any two-sided market. We consider a useful variation of the stable marriage problem, where the men and women express their preferences using a preference list with ties over a subset of the members of the other sex. Matchings are permitted only with people who appear in these preference lists. In this setting, we study the problem of finding a stable matching that marries as many people as possible. Stability is an envy-free notion: no man and woman who are not married to each other would both prefer each other to their partners or to being single. This problem is NP-hard. We tackle this problem using local search, exploiting properties of the problem to reduce the size of the neighborhood and to make local moves efficiently. Experimental results show that this approach is able to solve large problems, quickly returning stable matchings of large and often optimal size.

We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly each relational sequence is mapped into a feature vector using the result of a feature construction method. Since, the efficacy of sequence learning algorithms strongly depends on the features used to represent the sequences, the second step is to find an optimal subset of the constructed features leading to high classification accuracy. This feature selection task has been solved adopting a wrapper approach that uses a stochastic local search algorithm embedding a naive Bayes classifier. The performance of the proposed method applied to a real-world dataset shows an improvement when compared to other established methods, such as hidden Markov models, Fisher kernels and conditional random fields for relational sequences.

Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one problem is to find an optimal relaxation, which is a generalisation of the feedback vertex set problem on directed graphs, and thus it is an NP-hard task. We present several constraint programming formulations of the problem. We also present formulations using partial weighted maximum Boolean satisfiability and mixed integer linear programming. We study all these formulations by experimentally comparing them on a variety of randomly generated instances of the feature subscription problem.

The Zen Puzzle Garden (ZPG) [16] is a one-player puzzle game involving a Buddhist monk raking a sand garden. It is inspired by Japanese garden design (for example, the Komyozenji temple garden is shown in Figure 1). One common feature of such gardens is a flat region of sand or small pebbles, which is raked into a pattern. The ZPG is one example of a transport puzzle; these are problems that involve the player moving entities around a given domain (e.g., boxed around a warehouse), starting at some initial configuration, until they attain predefined goal conditions. Entities must move according to the constraints of the puzzle, and may only move between connected positions (that is, an entity may not be "lifted" off the board and replaced at a position perhaps far from its initial location). A graphical representation of the problem may use vertices to represent the set of positions an entity may occupy, with connecting edges determined either from any explicitly named connections, or from those implied by arrangement on the board or within a grid. The rest of the paper is organized as follows: We first describe related work in Section III and give an indepth description of the problem in Section III, before describing two solution methods (genetic algorithm and A*) for the ZPG in Section IV. Experimental results are presented in Section V, before we conclude with a brief discussion of the implications of our findings in terms of broader applicability.

Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, & Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.

Preventive maintenance schedules occurring in industry are often suboptimal with regard to maintenance coal-location, loss-of-production costs and availability. We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines, with the goal of reducing the direct maintenance costs and the often costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that the feasibility version is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using integer programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, the use of our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days by 12%. Compared to a integer programming approach, our algorithm is not optimal, but is much faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based< on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals.

We present tropical games, a generalization of combinatorial min-max games based on tropical algebras. Our model breaks the traditional symmetry of rational zero-sum games where players have exactly opposed goals (min vs. max), is more widely applicable than min-max and also supports a form of pruning, despite it being less effective than alpha-beta. Actually, min-max games may be seen as particular cases where both the game and its dual are tropical: when the dual of a tropical game is also tropical, the power of alpha-beta is completely recovered. We formally develop the model and prove that the tropical pruning strategy is correct, then conclude by showing how the problem of approximated parsing can be modeled as a tropical game, profiting from pruning.

We present an extension of the planning framework based on action graphs and local search to deal with PDDL2.1 temporal problems requiring concurrency, while previously the approach could only solve problems admitting a sequential solution. The paper introduces a revised plan representation supporting concurrency and some new search techniques using it, which are implemented in a new version of the LPG planner. An experimental analysis indicates that the proposed approach is suitable to temporal planning with requiring concurrency and is competitive with state-of-the-art planners.

Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the best known plan divided by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan by creating a plan neighborhood graph PNG(π) of a given plan π, and then extracts a shortest path from PNG(π). Both methods are implemented in the Aras postprocessor and are empirically shown to improve the result of several planners, including the top four planners from IPC-2008, under competition conditions.

While the string of successes found in using heuristic, best-first search methods have provided positive reinforcement for continuing work along these lines, fundamental problems arise when handling objectives whose value does not change with search operations. An extreme case of this occurs when handling the objective of generating a temporal plan with short makespan. Typically used heuristic search methods assume strictly positive edge costs for their guarantees on completeness and optimality, while the usual ``fattening'' and ``advance time'' steps of heuristic search for temporal planning have the potential of resulting in ``g-value plateaus''. In this paper we point out some underlying difficulties with using modern heuristic search methods when operating over g-value plateaus and discuss how the presence of these problems contributes to the poor performance of heuristic search planners. To further illustrate this, we show empirical results on recent benchmarks using a planner made with makespan optimization in mind.