However, little is known regarding which algorithm is heuristic graph search algorithms. The analysis better in practice or the actual benefits of heuristic information is focused on the influence of heuristic information, in multiobjective search performance.

The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today’s society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture. In this paper, we investigate methods for automatically connecting the dots – providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events leading from the decline of home prices (2007) to the health-care debate (2009). We formalize the characteristics of a good chain and provide efficient algorithms to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate the algorithm's effectiveness in helping users understanding the news.

This paper presents a new Monte-Carlo search algorithm for very large sequential decision-making problems. We apply non-linear regression within Monte-Carlo search, online, to estimate a state-action value function from the outcomes of random roll-outs. This value function generalizes between related states and actions, and can therefore provide more accurate evaluations after fewer rollouts. A further significant advantage of this approach is its ability to automatically extract and leverage domain knowledge from external sources such as game manuals. We apply our algorithm to the game of Civilization II, a challenging multi-agent strategy game with an enormous state space and around 10^21 joint actions. We approximate the value function by a neural network, augmented by linguistic knowledge that is extracted automatically from the official game manual. We show that this non-linear value function is significantly more efficient than a linear value function, which is itself more efficient than Monte-Carlo tree search. Our non-linear Monte-Carlo search wins over 78% of games against the built-in AI of Civilization II.

Imprecise-reward Markov decision processes (IRMDPs) are MDPs in which the reward function is only partially specified (e.g., by some elicitation process). Recent work using minimax regret to solve IRMDPs has shown, despite their theoretical intractability, how the set of policies that are nondominated w.r.t. reward uncertainty can be exploited to accelerate regret computation. However, the number of nondominated policies is generally so large as to undermine this leverage. In this paper, we show how the quality of the approximation can be improved online by pruning/adding nondominated policies during reward elicitation, while maintaining computational tractability. Drawing insights from the POMDP literature, we also develop a new anytime algorithm for constructing the set of nondominated policies with provable (anytime) error bounds. These bounds can be exploited to great effect in our online approximation scheme.

We define solving techniques for the Minimum Satisfiability Problem (MinSAT), propose an efficient branch-and-bound algorithm to solve the Weighted Partial MinSAT problem, and report on an empirical evaluation of the algorithm on Min-3SAT, MaxClique, and combinatorial auction problems. Techniques solving MinSAT are substantially different from those for the Maximum Satisfiability Problem (MaxSAT). Our results provide empirical evidence that solving combinatorial optimization problems by reducing them to MinSAT may be substantially faster than reducing them to MaxSAT, and even competitive with specific algorithms. We also use MinSAT to study an interesting correlation between the minimum number and the maximum number of satisfied clauses of a SAT instance.

Classical constraint satisfaction problems (CSPs) are commonly defined on finite domains. In real life, constrained variables can evolve over time. A variable can actually take an infinite sequence of values over discrete time points. In this paper, we propose constraint programming on infinite data streams, which provides a natural way to model constrained time-varying problems. In our framework, variable domains are specified by ω-regular languages. We introduce special stream operators as basis to form stream expressions and constraints. Stream CSPs have infinite search space. We propose a search procedure that can recognize and avoid infinite search over duplicate search space. The solution set of a stream CSP can be represented by a Büchi automaton allowing stream values to be non-periodic. Consistency notions are defined to reduce the search space early. We illustrate the feasibility of the framework by examples and experiments.

Balanced multi-way number partitioning (BMNP) seeks to split a collection of numbers into subsets with (roughly) the same cardinality and subset sum. The problem is NP-hard, and there are several exact and approximate algorithms for it. However, existing exact algorithms solve only the simpler, balanced two-way number partitioning variant, whereas the most effective approximate algorithm, BLDM, may produce widely varying subset sums. In this paper, we introduce the LRM algorithm that lowers the expected spread in subset sums to one third that of BLDM for uniformly distributed numbers and odd subset cardinalities. We also propose Meld, a novel strategy for skewed number distributions. A combination of LRM and Meld leads to a heuristic technique that consistently achieves a narrower spread of subset sums than BLDM.

Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. Recently, a different approach, RIBS, was suggested which, instead of learning costs to the goal, learns costs from the start state. RIBS can solve some problems faster, but in other problems has poor performance. We present a new algorithm, f-cost Learning Real-Time A* (f-LRTA*), which combines both approaches, simultaneously learning distances from the start and heuristics to the goal. An empirical evaluation demonstrates that f-LRTA* outperforms both RIBS and LRTA*-style approaches in a range of scenarios.

Hash Distributed A* (HDA*) is a parallel A* algorithm that is proven to be effective in optimal sequential planning with unit edge costs. HDA* leverages the Zobrist function to almost uniformly distribute and schedule work among processors. This paper evaluates the performance of HDA* in optimal sequence alignment. We observe that with a large number of CPU cores HDA* suffers from an increase of search overhead caused by reexpansions of states in the closed list due to nonuniform edge costs in this domain. We therefore present a new work distribution strategy limiting processors to distribute work, thus increasing the possibility of detecting such duplicate search effort. We evaluate the performance of this approach on a cluster of multi-core machines and show that the approach scales well up to 384 CPU cores.

Bounded suboptimal search algorithms offer shorter solving times bysacrificing optimality and instead guaranteeing solution costs withina desired factor of optimal. Typically these algorithms use a singleadmissible heuristic both for guiding search and bounding solutioncost. In this paper, we present a new approach to bounded suboptimalsearch, Explicit Estimation Search, that separates these roles,consulting potentially inadmissible information to determine searchorder and using admissible information to guarantee the cost bound.Unlike previous proposals, it successfully combines estimates ofsolution length and solution cost to predict which node will lead mostquickly to a solution within the suboptimality bound. An empiricalevaluation across six diverse benchmark domains shows that ExplicitEstimation Search is competitive with the previous state of the art indomains with unit-cost actions and substantially outperformspreviously proposed techniques for domains in which solution cost andlength can differ.