We address the issue of manipulating games through communication. In the specific setting we consider (a variation of Boolean games), we assume there is some set of environment variables, the value of which is not directly accessible to players; each player has their own beliefs about these variables, and makes decisions about what actions to perform based on these beliefs. The communication we consider takes the form of (truthful) announcements about the value of some environment variables; the effect of an announcement about some variable is to modify the beliefs of the players who hear the announcement so that they accurately reflect the value of the announced variables. By choosing announcements appropriately, it is possible to perturb the game away from certain rational outcomes and towards others. We specifically focus on the issue of stabilisation: making announcements that transform a game from having no stable states to one that has stable configurations.

We propose a logical framework to represent and reason about agent interactions in normative systems. Our starting point is a dynamic logic of propositional assignments whose satisfiability problem is PSPACE-complete. We show that it embeds Coalition Logic of Propositional Control CL-PC and that various notions of ability and capability can be captured in it. We illustrate it on a water resource management case study. Finally, we show how the logic can be easily extended in order to represent constitutive rules which are also an essential component of the modelling of social reality.

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.

Heuristic search is a central component of many important applications in AI including automated planning.  While we can find  optimal solutions to heuristic search problems, doing so may take hours or days. For practical applications, this is unacceptably slow, and we must rely on algorithms which find solutions of high, but not optimal, quality or ones which bound the time used directly. In my dissertation, I present and analyze algorithms for the following settings: quality bounded heuristic search and time  bounded heuristic search. The central theme of my doctoral work will be that taking advantage of additional information can improve the performance of heuristic search algorithms.

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.

Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan linearizations and as a consequence are inherently robust. We exploit this robustness to do effective execution monitoring. We characterize the conditions under which a POP remains viable as the regression of the goal through the structure of a POP. We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered algebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP linearizations to accommodate unexpected changes during execution. We demonstrate the effectiveness of our approach by comparing it empirically and analytically to a standard technique for execution monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.

Complex networks pervade in diverse areas ranging from the natural world to the engineered world and from traditional application domains to new and emerging domains, including web-based social networks. Of crucial importance to the understanding of many network phenomena, dynamics and functions is the study of network structural properties. One important type of network structure is known as community structure which refers to the existence of communities that are tightly knit local groups with relatively dense connections among their members. Community detection is the problem of detecting these communities automatically. In this paper, based on the modularity measure proposed previously for community detection, we first propose a reformulation of an optimization problem for the 2-partition problem. Based on this new formulation, we can extend it naturally for tackling the general k-partition problem directly without having to tackle multiple 2-partition subproblems like what other methods do. We then propose a convex relaxation scheme to give an iterative algorithm which solves a simple quadratic program in each iteration. We empirically compare our method with some related methods and find that our method is both scalable and competitive in performance via maintaining a good tradeoff between efficiency and quality.

The original Yahoo! search engine consists of manually organized topic hierarchy of webpages for easy browsing. Modern search engines (such as Google and Bing), on the other hand, return a flat list of webpages based on keywords. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined. The main difficulty in doing so is to automatically (i.e., not manually) classify and rank a massive number of webpages into various hierarchies (such as topics, media types, regions of the world). In this paper we report our attempt towards building this integrated search engine, called SEE (Search Engine with hiErarchy). We implement a hierarchical classification system based on Support Vector Machines, and embed it in SEE. We also design a novel user interface that allows users to dynamically adjust their desire for a higher accuracy vs. more results in any (sub)category of the hierarchy. Though our current search engine is still small (indexing about 1.2 million webpages), the results, including a small user study, have shown a great promise for integrating such techniques in the next-generation search engine.