This would allow teams to have an objective currency for trust transactions. These systems would allow another surface for autonomous Ali, A.S., Rana, O., and Walker, D.W. (2004): "WS-QoC: agents to integrate the social fabric with information Measuring Quality of Service Compliance," International gathered in virtual environments. Further, the system would Conference on Service Oriented Computing (ICSOC04), New increase illumination of dark networks engaged in illicit York, NY. covert activity. Participants would be assigned a score Allbeck, J., and Badler, N. (2002): "Toward Representing Agent similar to FICO scores; when an individual score falls Behaviors Modified by Personality and Emotion," Autonomous noticeably or falls below a threshold, further observation Agents and Multiagent Systems, Bologna, Italy.

The ability to model search in a constraint solver can be an essential asset for solving combinatorial problems. However, existing infrastructure for defining search heuristics is often inadequate. Either modeling capabilities are extremely limited or users are faced with a general-purpose programming language whose features are not tailored towards writing search heuristics. As a result, major improvements in performance may remain unexplored. This article introduces search combinators, a lightweight and solver-independent method that bridges the gap between a conceptually simple modeling language for search (high-level, functional and naturally compositional) and an efficient implementation (low-level, imperative and highly non-modular). By allowing the user to define application-tailored search strategies from a small set of primitives, search combinators effectively provide a rich domain-specific language (DSL) for modeling search to the user. Remarkably, this DSL comes at a low implementation cost to the developer of a constraint solver. The article discusses two modular implementation approaches and shows, by empirical evaluation, that search combinators can be implemented without overhead compared to a native, direct implementation in a constraint solver.

Discussion boards are a form of social media which allow users to discuss topics and exchange information in a complex manner, in a number of different settings. As the popularity of such message boards has increased, communities of users have emerged, and several prominent types of social role have been identified, such as Question Answerer, Celebrity, Discussion Person and Topic Initiator. Recent studies have noted the structural similarity of the egocentric network of users assigned the same role by qualitative criteria. In this paper a methodology is developed with which to cluster together users with similar ego-centric network structures. This is achieved using a mixed membership formulation which allows for the fact that different groups of users may have characteristics in common. The method is then applied to data taken from boards.ie, a medium sized message boards website. Prominent clusters of users are identified and discussed, and illustrative examples of user behaviour provided. The type of interaction, both locally and globally, taking place within forums is examined.

Participating in a community exemplifies the aspect of sharing, networking and interacting in a social media system. There has been extensive work on characterising on-line communities by their contents and tags using topic modelling tools. However, the role of sentiment and mood has not been studied. Arguably, mood is an integral feature of a text, and becomes more significant in the context of social media: two communities might discuss precisely the same topics, yet within an entirely different atmosphere. Such sentiment-related distinctions are important for many kinds of analysis and applications, such as community recommendation. We present a novel approach to identification of latent hyper-groups in social communities based on users’ sentiment. The results show that a sentiment-based approach can yield useful insights into community formation and meta-communities, having potential applications in, for example, mental health—by targeting support or surveillance to communities with negative mood—or in marketing—by targeting customer communities having the same sentiment on similar topics.

Bilingual machine-readable dictionaries are knowledge resources useful in many automatic tasks. However, compared to monolingual computational lexicons like WordNet, bilingual dictionaries typically provide a lower amount of structured information such as lexical and semantic relations, and often do not cover the entire range of possible translations for a word of interest. In this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the automated disambiguation of ambiguous translations in the lexical entries of a bilingual machine-readable dictionary. The dictionary is represented as a graph, and cyclic patterns are sought in this graph to assign an appropriate sense tag to each translation in a lexical entry. Further, we use the algorithm's output to improve the quality of the dictionary itself, by suggesting accurate solutions to structural problems such as misalignments, partial alignments and missing entries. Finally, we successfully apply CQC to the task of synonym extraction.

Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DCMDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DCMDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD - a continuous variable extension of the algebraic decision diagram (ADD) - that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DCMDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.

Qualitative information about spatial or temporal entities is represented by specifying qualitative relations between these entities. It is then possible to apply qualitative reasoning methods for tasks such as checking consistency of the given information, deriving previously unknown information or answering queries. Depending on the kind of information that is represented, qualitative reasoning methods might lead to incorrect results, and it is a topic of ongoing research efforts to determine when and why this occurs. In this paper we present two possible explanations for this behaviour: (1) the existence of implicit entities that we do not explicitly represent; (2) the existence of implicit constraints that have to be satisfied, but which are not explicitly represented. We show that both of these can lead to undetected inconsistencies. By making these implicit entities and constraints explicit, and by including them in the qualitative representation, we are able to solve problems that could not be solved qualitatively before. We present different examples of implicit entities and implicit constraints and an algorithm for solving them.

In this paper, we propose a semantic forgetting for arbitrary logic programs(or propositional theories) under answer set semantics,called HT-forgetting. The HT-forgetting preserves strong equivalence in the sense that strongly equivalent logic programs will remain strongly equivalent after forgetting the same set of atoms. The result of an HT-forgetting is always expressible by a logic program, and in particular, the result of an HT-forgetting in a Horn program is expressible in a Horn program; and a representation theorem shows that HT-forgetting can be precisely characterized by Zhang-Zhou's four forgetting postulates under the logic of here-and-there. We also reveal underlying connections between HT-forgetting and classical forgetting, and provide complexity results for decision problems.

The advance of Internet and Sensor technology has brought about new challenges evoked by the emergence of continuous data streams. While existing data-stream management systems allow for high-throughput stream processing, they lack complex reasoning capacities. We address this shortcoming and elaborate upon an approach to knowledge-intense stream reasoning based on Answer Set Programming (ASP). The emphasis thus shifts from rapid data processing to complex reasoning. To accommodate this in ASP, we develop new techniques that allow us to formulate problem encodings dealing with emerging as well as expiring data in a seamless way. We thus propose novel language constructs and modeling techniques for specifying and reasoning with time-decaying logic programs.

Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different kinds of spatial relations are important, including topology, direction, size, and distance between objects as well as changes of those relations over time. Typically these kinds of relations are treated separately, which makes it difficult to integrate all the extracted spatial information. We present a uniform and comprehensive spatial representation of moving objects that includes all the above spatial/temporal aspects, analyse different properties of this representation and demonstrate that it is suitable for video analysis.