In this paper we take a step towards using Argumentation in Social Networksand introduce Social Abstract Argumentation Frameworks, an extension of Dung'sAbstract Argumentation Frameworks that incorporates social voting.We propose a class of semantics for these new Social Abstract Argumentation Frameworks and prove some important non-trivial properties which are crucialfor their applicability in Social Networks.

Gjerstad and Dickhaut, 1998; Nicolaisen et al., 2001] and a market selection strategy which is mainly based on the history This paper describes the strategies used by of the trader's profit made with each specialist. AstonCAT-Plus, the post-tournament version of A CAT game lasts a number of days (500 days in CATthe specialist designed for the TAC Market Design 2010). Each day consists of a number of trading rounds, Tournament 2010. It details how AstonCATwhich each lasts for a known constant length of time. The Plus accepts shouts, clears market, sets transaction daily evaluation of the specialists is based on three metrics: prices and charges fees. Through empirical evaluation, (1) market share, which is the percentage of the total traders' we show that AstonCAT-Plus not only outperforms population registered in the market; (2) profit share, which is AstonCAT (tournament version) significantly the ratio of the daily profit a specialist obtains to the profit of but also achieves the second best overall all specialists and (3) transaction success rate (TSR), which score against some top entrants of the competition.

Thanks to the idea of social collaboration, Wikipedia has accumulated vast amount of semi-structured knowledge in which the link structure reflects human's cognition on semantic relationship to some extent. In this paper, we proposed a novel method RCRank to jointly compute concept-concept relatedness and concept-category relatedness base on the assumption that information carried in concept-concept links and concept-category links can mutually reinforce each other. Different from previous work, RCRank can not only find semantically related concepts but also interpret their relations by categories. Experimental results on concept recommendation and relation interpretation show that our method substantially outperforms classical methods.

Imprecision and uncertainty in the large number of input parameters are serious problems in forest fire behaviour modelling. To obtain more reliable forecasts, fast and efficient computational input parameter estimation and calibration mechanisms should be integrated. These have to respect hard real-time constraints of simulations to prevent tragedy. We propose an Evolutionary Intelligent System (EIS) for parameter calibration. Depending on disaster size, required parameter precision, and available computing resources, the hybridisation of an evolutionary algorithm (EA) with an intelligent paradigm (IP) can be configured. Experiments show that EIS generates comparable estimations to standard evolutionary calibration approaches, clearly outperforming the latter in runtime.

This paper is devoted to sequential decision making under uncertainty, in the multi-prior framework of Gilboa and Schmeidler [1989]. In this setting, a set of probability measures (priors) is defined instead of a single one, and the decision maker selects a strategy that maximizes the minimum possible value of expected utility over this set of priors. We are interested here in the resolute choice approach, where one initially commits to a complete strategy and never deviates from it later. Given a decision tree representation with multiple priors, we study the problem of determining an optimal strategy from the root according to min expected utility. We prove the intractability of evaluating a strategy in the general case. We then identify different properties of a decision tree that enable to design dedicated resolution procedures. Finally, experimental results are presented that evaluate these procedures.

Many complex robot motor skills can be represented using elementary movements, and there exist efficient techniques for learning parametrized motor plans using demonstrations and self-improvement. However with current techniques, in many cases, the robot currently needs to learn a new elementary movement even if a parametrized motor plan exists that covers a related situation. A method is needed that modulates the elementary movement through the meta-parameters of its representation. In this paper, we describe how to learn such mappings from circumstances to meta-parameters using reinforcement learning. In particular we use a kernelized version of the reward-weighted regression. We show two robot applications of the presented setup in robotic domains; the generalization of throwing movements in darts, and of hitting movements in table tennis. We demonstrate that both tasks can be learned successfully using simulated and real robots.

We compare the expressiveness of the fragments of Halpern and Shoham's interval logic (HS), i.e., of all interval logics with modal operators associated with Allen's relations between intervals in linear orders. We establish a complete set of inter-definability equations between these modal operators, and thus obtain a complete classification of the family of 212 fragments of HS with respect to their expressiveness. Using that result and a computer program, we have found that there are 1347 expressively different such interval logics over the class of all linear orders.

We focus on recovering the 2D Euclidean structure in one view from the projections of N parallel conics in this paper. This work denotes that the conic dual to the absolute points is the general form of the conic dual to the circular points, but it does not encode the Euclidean structure. Therefore, we have to recover the circular point-envelope to find out some useful information about the Euclidean structure, which relies on the fact that the line at infinity and the symmetric axis can be recovered. We provide a solution to recover the two lines and deduce the constraints for recovering the conic dual to the circular points, then apply them on the camera calibration. Our work relaxes the problem conditions and gives a more general framework than the past. Experiments with simulated and real data are carried out to show the validity of the proposed algorithm. Especially, our method is applied in the endoscope operation to calibrate the camera for tracking the surgical tools, that is the main interest-point we pay attention to.

Metaphorical interpretation and affect detection using context profiles from open-ended text input are challenging in affective language processing field. In this paper, we explore recognition of a few typical affective metaphorical phenomena and context-based affect sensing using the modeling of speakers’ improvisational mood and other participants’ emotional influence to the speaking character under the improvisation of loose scenarios. The overall updated affect detection module is embedded in an AI agent. The new developments have enabled the AI agent to perform generally better in affect sensing tasks. The work emphasizes the conference themes on affective dialogue processing, human-agent interaction and intelligent user interfaces.

Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages: it can represent a wide range of problem domains, and it supports the development of generic algorithms to solve them. Firstly, this paper presents some advances in both complete and approximate DCOP algorithms. Secondly, it explains that the DCOP model makes a number of unrealistic assumptions that severely limit its range of application. Finally, it points out hints on how to tackle such limitations.