Information imprecision and uncertainty exist in many real world applications, and such information would be retrieved, processed, shared, reused, and aligned in the maximum automatic way possible. As a popular family of formally well-founded and decidable knowledge representation languages, fuzzy Description Logics (fuzzy DLs), which extend DLs with fuzzy logic, are very well suited to cover for representing and reasoning with imprecision and uncertainty. Thus, a requirement naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web, because DLs are the logical foundation of the Semantic Web. Currently, there have been lots of fuzzy extensions of DLs with Zadeh's fuzzy logic theory papers published, to investigate fuzzy DLs and more importantly serve as identifying the direction of fuzzy DLs study. In this paper, we aim at providing a comprehensive literature overview of fuzzy DLs, and we focus our attention on fuzzy extensions of DLs based on fuzzy set theory. Other relevant formalisms that are based on approaches like probabilistic theory or non-monotonic logics are covered elsewhere. In detail, we first introduce the existing fuzzy DLs (including the syntax, semantics, knowledge base, and reasoning algorithm) from the origin, development (from weaker to stronger in expressive power), some special techniques, and so on. Then, the other important issues on fuzzy DLs, such as reasoning, querying, applications, and directions for future research, are also discussed in detail. Also, we make a comparison and analysis.

This paper addresses the problem of implementing a Simultaneous Localization and Mapping (SLAM) algorithm combined with a non-reactive controller (such as trajectory following or path following). A general study showing the advantages of using predictors to avoid mapping inconsistences in autonomous SLAM architectures is presented. In addition, this paper presents a priority-based uncertainty map construction method of the environment by a mobile robot when executing a SLAM algorithm. The SLAM algorithm is implemented with an extended Kalman filter (EKF) and extracts corners (convex and concave) and lines (associated with walls) from the surrounding environment. A navigation approach directs the robot motion to the regions of the environment with the higher uncertainty and the higher priority. The uncertainty of a region is specified by a probability characterization computed at the corresponding representative points. These points are obtained by a Monte Carlo experiment and their probability is estimated by the sum of Gaussians method, avoiding the time-consuming map-gridding procedure. The priority is determined by the frame in which the uncertainty region was detected (either local or global to the vehicle's pose). The mobile robot has a non-reactive trajectory following controller implemented on it to drive the vehicle to the uncertainty points. SLAM real-time experiments in real environment, navigation examples, uncertainty maps constructions along with algorithm strategies and architectures are also included in this work.

User simulation is an important research area in the field of spoken dialogue systems (SDSs) because collecting and annotating real human–machine interactions is often expensive and time-consuming. However, such data are generally required for designing, training and assessing dialogue systems. User simulations are especially needed when using machine learning methods for optimizing dialogue management strategies such as Reinforcement Learning, where the amount of data necessary for training is larger than existing corpora. The quality of the user simulation is therefore of crucial importance because it dramatically influences the results in terms of SDS performance analysis and the learnt strategy. Assessment of the quality of simulated dialogues and user simulation methods is an open issue and, although assessment metrics are required, there is no commonly adopted metric. In this paper, we give a survey of User Simulations Metrics in the literature, propose some extensions and discuss these metrics in terms of a list of desired features.

Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues.

This paper integrates the responses to a set of questions from a distinguished set of panelists involved in a discussion at the Agreement Technologies workshop in Cyprus in December 2009. The panel was concerned with the relationship between the research areas of semantics, norms, and organizations, and the ways in which each may contribute to the development of the others in support of next generation agreement technologies.