We address the problem of learning preference relations on multi-attribute (or combinatorial) domains. We do so by making a very simple hypothesis about the dependence structure between attributes that the preference relation enjoys, namely separability (no preferential dependencies between attributes). Given a set of examples consisting of comparisons between alternatives, we want to output a separable CP-net, consisting of local preferences on each of the attributes, that fits the examples. We consider three forms of compatibility between a CP-net and a set of examples, and for each of them we give useful characterizations as well as complexity results.

We develop a framework for forgetting concepts and roles (aka uniform interpolation) in terminologies in the lightweight description logic EL extended with role inclusions and domain and range restrictions. Three different notions of forgetting, preserving, respectively, concept inclusions, concept instances, and answers to conjunctive queries, with corresponding languages for uniform interpolants are investigated. Experiments based on SNOMED CT (Systematised Nomenclature of Medicine Clinical Terms) and NCI (National Cancer Institute Ontology) demonstrate that forgetting is often feasible in practice for large-scale terminologies.

Abductive inference is an important AI reasoning technique to find explanations of observations, and has recently been applied to scientific discovery.  To find best hypotheses among many logically possible hypotheses, we need to evaluate hypotheses obtained from the process of hypothesis generation.  We propose an abductive inference architecture combined with an EM algorithm working on binary decision diagrams (BDDs).  This work opens a way of applying BDDs to compress multiple hypotheses and to select most probable ones from them.  An implemented system has been applied to inference of inhibition in metabolic pathways in the domain of systems biology.

We present an unified methodology for representation and development of dialectical proof procedures in both abstract and assumption-based argumentation based on the notions of legal environments and dispute derivation. A legal environment specifies the legal moves of the dispute parties while a dispute derivation describes the procedure structure. A key insight of this paper is that the opponent moves determine the soundness of a dispute while its completeness depends on the proponent moves.

To enable ontology reuse, the Web Ontology Language (OWL) allows an ontology Kv to import an ontology Kh. To reason with such a Kv, a reasoner needs physical access to the axioms of Kh. For copyright and/or privacy reasons, however, the authors of Kh might not want to publish the axioms of Kh; instead, they might prefer to provide an oracle that can answer a (limited) set of queries over Kh, thus allowing Kv to import Kh "by query." In this paper, we study import-by-query algorithms, which can answer questions about Kv U Kh by accessing only Kv and the oracle. We show that no such algorithm exists in general, and present restrictions under which importing by query becomes feasible.

Logic reasoning approaches to fault diagnosis account for the fact that a component c j may fail intermittently by introducing a parameter g j that expresses the probability the component exhibits correct behavior. This component parameter g j , in conjunction with a priori fault probability, is usedin a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on g j is not known a priori. While proper estimation of g j can have a great impact on the diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, BARINEL, that computes exact estimations of g j as integral part of the posterior candidate probability computation. BARINEL’s diagnostic performance is evaluated for both synthetic and real software systems. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.

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.

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.

Optimization in graphical models is an important problem which has been studied in many AI frameworks such as weighted CSP, maximum satisfiability or probabilistic networks. By identifying conditionally independent subproblems, which are solved independently and whose optimum is cached, recent Branch and Bound algorithms offer better asymptotic time complexity. But the locality of bounds induced by decomposition often hampers the practical effects of this result because subproblems are often uselessly solved to optimality. Following the Russian Doll Search (RDS) algorithm, a possible approach to overcome this weakness is to (inductively) solve a relaxation of each subproblem to strengthen bounds. The algorithm obtained generalizes both RDS and tree-decomposition based algorithms such as BTD or AND-OR Branch and Bound. We study its efficiency on different problems, closing a very hard frequency assignment instance which has been open for more than 10 years.

Moving target search (MTS) or the game of cops and robbers has a broad field of application reaching from law enforcement to computer games. Within the recent years research has focused on computing move policies for one or multiple pursuers (cops). The present work motivates to extend this perspective to both sides, thus developing algorithms for the target (robber). We investigate the game with perfect information for both players and propose two new methods, named TrailMax and Dynamic Abstract Trailmax, to compute move policies for the target. Experiments are conducted by simulating games on 20 maps of the commercial computer game Baldur's Gate and measuring survival time and computational complexity. We test seven algorithms: Cover, Dynamic Abstract Minimax, minimax, hill climbing with distance heuristic, a random beacon algorithm, TrailMax and DATrailMax. Analysis shows that our methods outperform all the other algorithms in quality, achieving up to 98% optimality, while meeting modern computer game computation time constraints.