Knowledge base exchange is an important problem in the area of data exchange and knowledge representation, where one is interested in exchanging information between a source and a target knowledge base connected through a mapping. In this paper, we study this fundamental problem for knowledge bases and mappings expressed in OWL 2 QL, the profile of OWL 2 based on the description logic DL-LiteR. More specifically, we consider the problem of computing universal solutions, identified as one of the most desirable translations to be materialized, and the problem of computing UCQ- representations, which optimally capture in a target TBox the information that can be extracted from a source TBox and a mapping by means of unions of conjunctive queries. For the former we provide a novel automata-theoretic technique, and complexity results that range from NP to EXPTIME, while for the latter we show NLOGSPACE-completeness.

The Interval Algebra (IA) and a subset of the Region Connection Calculus (RCC), namely RCC-8, are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively. Such qualitative information can be formulated as a Qualitative Constraint Network (QCN). In this paper, we focus on the minimal labeling problem (MLP) and we propose an algorithm to efficiently derive all the feasible base relations of a QCN. Our algorithm considers chordal QCNs and a new form of partial consistency. Further, the proposed algorithm uses tractable subclasses of relations having a specific patchwork property for which closure under weak composition implies the consistency of the input QCN. Experimentations with QCNs of IA and RCC-8 show the importance and efficiency of this new approach.

Nowadays, Nearest Neighbor Search becomes more and more important when facing the challenge of big data. Traditionally, to solve this problem, researchers mainly focus on building effective data structures such as hierarchical k-means tree or using hashing methods to accelerate the query process. In this paper, we propose a novel unified approximate nearest neighbor search scheme to combine the advantages of both the effective data structure and the fast Hamming distance computation in hashing methods. In this way, the searching procedure can be further accelerated. Computational complexity analysis and extensive experiments have demonstrated the effectiveness of our proposed scheme.

Recent research on external-memory search has shown that disks can be effectively usedas secondary storage when performing large breadth-first searches.We introduce the Write-Minimizing Breadth-First Search (WMBFS) algorithm which is designed to minimizethe number of writes performed in an external-memory BFS. WMBFS is also designed to store the results ofthe BFS for later use.We present the results of a BFS on a single-agent version of Chinese Checkers and the Rubik's Cube edge cubes, state spaceswith about 1 trillion states each. In evaluating against a comparable approach, WMBFS reduces the I/O for the Chinese Checkers domain by over an order of magnitude.In Rubik's cube, in addition to reducing I/O, the search is also 3.5 times faster.Analysis of the results suggests the machine and state-space properties necessary for WMBFS to perform well.

There are many hard shortest-path search problems that cannot be solved, because best-first search runs out of memory space and depth-first search runs out of time. We propose Forward Perimeter Search (FPS), a heuristic search with controlled use of memory. It builds a perimeter around the root node and tests each perimeter node for a shortest path to the goal. The perimeter is adaptively extended towards the goal during the search process. We show that FPS expands in random 24-puzzles 50% fewer nodes than BF-IDA* while requiring several orders of magnitude less memory. Additionally, we present a hard problem instance of the 24-puzzle that needs at least 140 moves to solve; i.e. 26 more moves than the previously published hardest instance.

Graphical models are one of the most prominent frameworks to model complex systems and efficiently query them. Their underlying algebraic properties are captured by a valuation structure that, most usually, is a semiring. Depending on the semiring of choice, we can capture probabilistic models, constraint networks, cost networks, etc. In this paper we address the partitioning problem which occurs in many approximation techniques such as mini-bucket elimination and join-graph propagation algorithms. Roghly speaking, subject to complexity bounds, the algorithm needs to find a partition of a set of factors such that best approximates the whole set. While this problem has been addressed in the past in a particular case, we present here a general description. Furthermore, we also propose a  general partitioning scheme. Our proposal is general in the sense that it is  presented in terms of a generic semiring with the only additional requirements of a division operation and a refinement of its order. The proposed algorithm  instantiates to the particular task of computing the probability of evidence, but also applies directly to other important reasoning tasks. We demonstrate its good empirical behaviour on the problem of computing the most probable explanation.

Constructing a strong heuristic function is a central problem in heuristic search. A common approach is to combine a number of heuristics by maximizing over the values from each. If a limit is placed on this number, then a subset selection problem arises. We treat this as an optimization problem, and proceed by translating a natural loss function into a submodular and monotonic utility function under which greedy selection is guaranteed to be near-optimal. We then extend this approach with a sampling scheme that retains provable optimality. Our empirical results show large improvements over existing methods, and give new insight into building heuristics for directed domains.

The number partitioning problem seeks to divide a set of n numbers across k distinct subsets so as to minimize the sum of the largest partition. In this work, we develop a new optimal algorithm for multi-way number partitioning. A critical observation motivating our methodology is that a globally optimal k -way partition may be recursively constructed by obtaining suboptimal solutions to subproblems of size k – 1. We introduce a new principle of optimality that provides necessary and sufficient conditions for this construction, and use it to strengthen the relationship between sequential decompositions by enforcing upper and lower bounds on intermediate solutions. We also demonstrate how to further prune unpromising partial assignments by detecting and eliminating dominated solutions. Our approach outperforms the previous state-of-the-art by up to four orders of magnitude , reducing average runtime on the largest benchmarks from several hours to less than a second.

A set of constraints that cannot be simultaneously satisfied is over-constrained. Minimal relaxations and minimal explanations for over-constrained problems find many practical uses. For Boolean formulas, minimal relaxations of over-constrained problems are referred to as Minimal Correction Subsets (MCSes). MCSes find many applications, including the enumeration of MUSes. Existing approaches for computing MCSes either use a Maximum Satisfiability (MaxSAT) solver or iterative calls to a Boolean Satisfiability (SAT) solver. This paper shows that existing algorithms for MCS computation can be inefficient, and so inadequate, in certain practical settings. To address this problem, this paper develops a number of novel techniques for improving the performance of existing MCS computation algorithms. More importantly, the paper proposes a novel algorithm for computing MCSes. Both the techniques and the algorithm are evaluated empirically on representative problem instances, and are shown to yield the most efficient and robust solutions for MCS computation.

Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instanceto be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers dependent on the given instance. We devise a new classifier that selects solvers based on a cost-sensitive hierarchical clustering model. Experimental results on SAT and MaxSAT show that the new method outperforms the most effective portfolio builders to date.