Combinatorial auctions (CAs) have emerged as an important model in economics and show promise as a useful tool for tackling resource allocation in AI. Unfortunately, winner determination for CAs is NP-hard and recent algorithms have difficulty with problems involving goods and bids beyond the hundreds. We apply a new stochastic local search algorithm, Casanova, to this problem, and demonstrate ...

The search for finite-state controllers for partially observable Markov decision processes (POMDPs) is often based on approaches like gradient ascent, attractive because of their relatively low computational cost. In this paper, we illustrate a basic problem with gradient-based methods applied to POMDPs, where the sequential nature of the decision problem is at issue, and propose a new stochast...

Finding most probable explanations (MPEs) in graphical models, such as Bayesian belief networks, is a fundamental problem in reasoning under uncertainty, and much effort has been spent on developing effective algorithms for this NP-hard problem. Stochastic local search (SLS) approaches to MPE solving have previously been explored, but were found to be not competitive with state-of-theart branch...

The paper evaluates empirically the suitability of Stochastic Local Search algorithms (SLS) for nding most probable explanations in Bayesian networks. SLS algorithms (e.g., GSAT, WSAT [16]) have recently proven to be highly e ective in solving complex constraint-satisfaction and satis ability problems which cannot be solved by traditional search schemes. Our experiments investigate the applicab...

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a com...

We develop tools for analyzing focused stochastic local search algorithms. These are algorithms which search a state space probabilistically by repeatedly selecting a constraint that is violated in the current state and moving to a random nearby state which, hopefully, addresses the violation without introducing many new ones. A large class of such algorithms arise from the algorithmization of ...

Building on the recent success of bet-and-run approaches for restarted local search solvers, we introduce the idea of learning online adaptive restart strategies. Universal restart strategies deploy a fixed schedule that runs with utter disregard of the characteristics that each individual run exhibits. Whether a run looks promising or abysmal, it gets run exactly until the predetermined limit ...

We consider a single product maritime inventory routing problem in which the production and consumption rates are constant over the planning horizon. The problem involves a heterogeneous fleet of ships and multiple production and consumption ports with limited storage capacity. In spite of being one of the most common ways to transport goods, maritime transportation is characterized by high lev...