The automata-based approach is based on translating a knowledge base (KB) whose satisfiability is to be checked In this work we describe the theoretical foundations into some variant of automata on infinite trees that accepts and the implementation of a new automata-based tree-shaped models of the KB, and checking such an automaton technique for reasoning over expressive Description for non-emptiness. This approach is powerful and flexible. Logics that is worst-case optimal and lends itself It is acknowledged that it provides a very robust basis to an efficient implementation. In order to show for showing worst-case optimal complexity upper bounds, the feasibility of the approach, we have realized a and has been applied for a wide range of expressive DLs working prototype of a reasoner based upon these and reasoning services (cf.

Research in AI has built upon the tools and techniques of many different disciplines, including formal logic, probability theory, decision theory, management science, linguistics and philosophy. However, the application of these disciplines in AI has necessitated the development of many enhancements and extensions. Among the most powerful of these are the methods of computational logic. I will argue that computational logic, embedded in an agent cycle, combines and improves upon both traditional logic and classical decision theory. I will also argue that many of its methods can be used, not only in AI, but also in ordinary life, to help people improve their own human intelligence without the assistance of computers.

In AI, this area Ontologies that evolve through use to support new has been studied under a variety of names such as forgetting domain tasks can grow extremely large. Moreover, and variable elimination [Eiter et al., 2006; Wang et al., large ontologies require more resources to use and 2008]. We provide a general approach for ranking knowledge have slower response times than small ones. To according to its use and cost, which can be applied to systems help address this problem, we present an online semantic that are limited by memory resources to evaluate memory forgetting algorithm that removes ontology allocation. We also provide a specific approach to select fragments containing infrequently used or cheap to which concepts to remove from an ontology, using the ranking.

We show how Finite Model Computation (FMC) of first-order theories can efficiently and transparentlybe solved by taking advantage of an extension of Answer Set Programming, called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain sizes until an answer set, representing a finite model of the original first-order theory, is found. We developed a system based on the iASP solver iClingo and demonstrate its competitiveness.

We establish complexities of the conjunctive query entailment problem for classes of existential rules (i.e. Tuple-Generating Dependencies or Datalog+/- rules). Our contribution is twofold. First, we introduce the class of greedy bounded treewidth sets (gbts), which covers guarded rules, and their known generalizations, namely (weakly) frontier-guarded rules. We provide a generic algorithm for query entailment with gbts, which is worst-case optimal for combined complexity with bounded predicate arity, as well as for data complexity. Second, we classify several gbts classes, whose complexity was unknown, namely frontier-one, frontier-guarded and weakly frontier-guarded rules, with respect to combined complexity (with bounded and unbounded predicate arity) and data complexity.

Quality control is an essential task within ontology development projects especially when the knowledge formalization is partially automatized. In this paper, we propose a reasoning-based, interactive approach to support the revision of formalized knowledge. We state consistency criteria for revision states and introduce the notion of revision closure, based on which the revision of ontologies is partially automatized. Additionally, we propose a notion of axiom impact which is used to determine a beneficial order of axiom evaluation in order to further increase the effectiveness of ontology revision. Finally, we develop the notion of decision spaces, which are structures for calculating and updating the revision closure and axiom impact. The use of decision spaces saves on average 75% of the costly reasoning operations during a revision.

The data at our disposal consists of onboard sensor measurements that have been collected from truck round-trips. We advocate the use of an explicit time representation By applying a simple discretization method, we obtain sequences in syntactic pattern recognition because it can of timed events. The behavior that is displayed in result in more succinct models and easier learning these sequences is unknown. From this data, we want to learn problems. We apply this approach to the real-world a model that we can use to monitor the driving behavior in problem of learning models for the driving behavior new data, i.e., to use it as a classifier. Our approach is to first of truck drivers. We discretize the values of learn a timed model from the unlabeled sequences using the onboard sensors into simple events.

Ensemble methods, which train multiple learners for a task, are among the state-of-the-art learning approaches. The diversity of the component learners has been recognized as a key to a good ensemble, and existing ensemble methods try different ways to encourage diversity, mostly by heuristics. In this paper, we propose the diversity regularized machine (DRM) in a mathematical programming framework, which efficiently generates an ensemble of diverse support vector machines (SVMs). Theoretical analysis discloses that the diversity constraint used in DRM can lead to an effective reduction on its hypothesis space complexity, implying that the diversity control in ensemble methods indeed plays a role of regularization as in popular statistical learning approaches. Experiments show that DRM can significantly improve generalization ability and is superior to some state-of-the-art SVM ensemble methods.

Grammatical agreement is present in many of the world's languages today and has become an essential feature that guides linguistic processing. When two words in a sentence are said to "agree", this means that they share certain features such as "gender", "number", "person" or others. The primary hypothesis of this paper is that marking agreement within one linguistic phrase reduces processing effort as phrasal constituents can more easily be recognized. The drive to reduce processing effort introduces the rise of agreement marking in a population of multiple agents by means of an incrementally aligned mapping between the most discriminatory features of a particular linguistic unit and their associative markers. A series of experiments compare feature selection methods for one-to-one agreement mappings, and show how an agreement system can be bootstrapped.

However, it has been shown that the antecedents of individual rules Decision rules, which can provide good interpretability may contain irrelevant conditions. C4.5rules (Quinlan, 1993) and flexibility for data mining tasks, uses an optimization procedure to simplify conditions. The have received very little attention in the stream optimization is done in two phases. First, each rule is generalized mining community so far. In this work we introduce by deleting conditions that do not seem to be helpful a new algorithm to learn rule sets, designed in discriminating the classes. A greedy search method is for open-ended data streams.