Skat is Germany's national card game played by millions of players around the world. In this paper, we present the world's first computer skat player that plays at the level of human experts. This performance is achieved by improving state evaluations using game data produced by human players and by using these state evaluations to perform inference on the unobserved hands of opposing players. Our results demonstrate the gains from adding inference to an imperfect information game player and show that training on data from average human players can result in expert-level playing strength.

We discuss CueCard, the program that won the 2008 Computer Olympiad computational pool tournament. Beside addressing intrinsic interest in a complex competitive environment with unique features, our goal is to isolate the factors that contributed to the performance so that the lessons can be transferred to other, similar domains. Specifically, we distinguish among pure engineering factors (such as using a computer cluster), domain-specific factors (such as optimized break shots), and domain-independent factors (such as state clustering). Our conclusion is that each type of factor contributed to the performance of the program.

Local pattern mining is concerned with finding the set of patterns that satisfy a constraint in a database. We study local pattern mining in the context of ProbLog, a probabilistic Prolog system, and introduce an approach for finding correlated patterns in the form of queries in such a Prolog system. The approach combines principles of inductive logic programming, data mining and statistical relational learning. Experiments on a challenging biological network mining task provide evidence for the interestingness of the approach.

Fourier-based learning algorithms rely on being able to efficiently find the large coefficients of a function's spectral representation. In this paper, we introduce and analyze techniques for finding large coefficients. We show how a previously introduced search technique can be generalized from the Boolean case to the real-valued case, and we apply it in branch-and-bound and beam search algorithms that have significant advantages over the best-first algorithm in which the technique was originally introduced.

Qualitative constraint calculi are representation formalisms that allow for efficient reasoning about spatial and temporal information. Many of the calculi discussed in the field of Qualitative Spatial and Temporal Reasoning can be defined as combinations of other, simpler and more compact formalisms. On the other hand, existing calculi can be combined to a new formalism in which one can represent, and reason about, different aspects of a domain at the same time. For example, Gerevini and Renz presented a loose combination of the region connection calculus RCC-8 and the point algebra: the resulting formalism integrates topological and qualitative size relations between spatially extended objects. In this paper we compare the approach by Gerevini and Renz to a method that generates a new qualitative calculus by exploiting the semantic interdependencies between the component calculi. We will compare these two methods and analyze some formal relationships between a combined calculus and its components. The paper is completed by an empirical case study in which the reasoning performance of the suggested methods is compared on random test instances.

We address the problem of providing a logical formalization of arithmetic in declarative modelling languages for NP search problems. The challenge is to simultaneously allow quantification over an infinite domain such as the natural numbers, provide natural modelling facilities, and control expressive power of the language. To address the problem, we introduce an extension of the model expansion (MX) based framework to finite structures embedded in an infinite secondary structure, together with "double-guarded" logics for representing MX specifications for these structures. The logics also contain multi-set functions (aggregate operations). Our main result is that these logics capture the complexity class NP on "small-cost" arithmetical structures. 

This paper presents an argumentation-based interpreter for Golog programs. Traditional Golog interpreters are not designed to find the most preferred executions of a program from the perspective of an agent. Existing techniques developed to discover these executions are limited in terms of how the preferences of an agent can be expressed, and the variety of preference types that can be used to guide search for a solution. The presented work combines the use of argumentation to compare executions relative to a set of general comparison principles, and the theory behind best first search to reduce the cost of the search process. To the best of our knowledge this is the first work to integrate argumentation and the interpretation of Golog programs, and to use argumentation as a tool for best first search.

There are two basic approaches to generalize the propagation mechanism of the two-player Minimax search algorithm to multi-player (3 or more) games: the MaxN algorithm and the Paranoid algorithm. The main shortcoming of these approaches is that their strategy is fixed. In this paper we suggest a new approach (called MP-Mix) that dynamically changes the propagation strategy based on the players' relative strengths between MaxN, Paranoid and a newly presented  offensive strategy. In addition, we introduce the Opponent Impact factor for multi-player games, which measures the players' ability to impact their opponents' score, and show its relation to the relative performance of our new MP-Mix strategy. Experimental results show that MP-Mix outperforms all other approaches under most circumstances.

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.

Early research in heuristic search discovered that using inconsistent heuristics with A* could result in an exponential increase in the number of node expansions. As a result, the use of inconsistent heuristics has largely disappeared from practice. Recently, inconsistent heuristics have been shown to be effective in IDA*, especially when applying the bidirectional pathmax (BPMX) enhancement. This paper presents new worst-case complexity analysis of A*'s behavior with inconsistent heuristics, discusses how BPMX can be used with A*, and gives experimental results justifying the use of inconsistent heuristics in A* searches.