We address the problem of simulate democratic elections via a set of competing agents.We propose a logical model based on a set of non-cooperative agents which compete for attracting a maximum number of votes from a population. Each agent builds a set of strategies (formed by the promises, actions and proposals of the agent) used to convince to the potential voters.

Intelligent question answering systems provide specific answers to users' questions. This paper describes a unique AI system that uses online knowledge basesand reasoning tools to answers questions posed by the Sporcle quiz site.

Subset space logic (SSL, henceforth) was presented in early In this paper, we consider public announcement logic (PAL, 90s as a bimodal logic to formalize reasoning about sets and henceforth) in several different geometric models, and prove points (Moss and Parikh 1992). The language of SSL has its completeness of those models. Moreover, we also consider two modal operators K and . A subset space model is a some applications of our ideas in different fields varying triple S 〈S, σ, v〉 where S is a nonempty set, σ is a collection from game theory to epistemic logic. What makes our of subsets (not necessarily a topology), v is a valuation work novel is the fact that PAL has never been investigated function. Semantics of SSL for modal operators is given in geometric and topological models with further applications.

Consensus clustering and meta clustering are two important extensions of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings, and meta clustering aims to group similar input clusterings together so that users only need to examine a small number of different clusterings. In this paper, we present a new approach, MCC (stands for multiple consensus clustering), to explore multiple clustering views of a given dataset from the input clusterings by combining consensus clustering and meta clustering. In particular, given a set of input clusterings of a particular data set, MCC employs meta clustering to cluster the input clusterings and then uses consensus clustering to generate a consensus for each cluster of the input clusterings. Extensive experimental results on 11 real world data sets demonstrate the effectiveness of our proposed method.

We discuss in this paper, from a pragmatic and operational point of view, the need of a clear differentiation between functional and semantic “roles.” In the first case, according to the linguistic and computational linguistics tradition, roles are seen as relations linking a semantic predicate to its arguments. In the second, in conformity with the ontological and Semantic Web practice, roles are equated to ordinary concepts to be inserted into a standard ontology. As we will show here, the two notions can successfully co-exist in the framework of a high level conceptual modeling language.

A software practitioner is interested in the solution to “for a given project, what is the minimum number of software metrics that should be considered for building an effective defect prediction model?” During the development life cycle various software metrics are collected for different reasons. In the case of a metricsbased defect prediction model, an intelligent selection of software metrics prior to building defect predictors is likely to improve model performance. This study utilizes the proposed threshold-based feature selection technique to remove irrelevant and redundant software metrics (a.k.a. features or attributes). A comparative investigation is presented for evaluating the size of the selected feature subsets. The case study is based on software measurement data obtained from a real-world project, and the defect predictors are trained using three commonly used classifiers. The empirical case study results demonstrate that an effective defect predictor can be built with only three metrics; and moreover, model performances improved when over 98.5% of the software metrics were eliminated.

We consider the problem of planning, on the fly, a path whereby a robotic vehicle will cover every point in an initially unknown spatial environment. We describe four strategies (Iterated WaveFront, Greedy-Scan, Delayed Greedy-Scan and Closest-First Scan) for generating cost-effective coverage plans in real time for unknown environments. We give theorems showing the correctness of our planning strategies. Our experiments demonstrate that some of these strategies work significantly better than others, and that the best ones work very well; e.g., in environments having an average of 64,000 locations for the robot to cover, the best strategy returned plans with less than 6% redundant coverage, and took only an average of 0.1 milliseconds per action.

Historically, causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However various connectives such as “and,” prepositions such as “as” and other syntactic structures are highly ambiguous in nature, and it is clear that one cannot solely rely on lexico-syntactic markers for detection of causal phenomenon in discourse. This paper introduces the theory of granularity and describes different approaches to identify granularity in natural language. As causality is often granular in nature, we use granularity relations to discover and infer the presence of causal relations in text. We compare this with causal relations identified using just causal markers. We achieve a precision of 0.91 and a recall of 0.79 using granularity for causal relation detection, as compared to a precision of 0.79 and a recall of 0.44 using pure causal markers for causality detection.

The aim of Belief Merging is to aggregate possibly conflicting pieces of information issued from different sources. The quality of the resulting set is usually considered in terms of a closeness criterion between the resulting belief set and the initial belief sets. The notion of distance between belief sets is thus a crucial issue when we face the merging problem. The aim of this paper is twofold: introducing a syntactical way to calculate distances and proposing the use of a distance based on prime implicants and prime implicates that considers the importance of each propositional symbol in the belief set.

With the rapid development of technologies for fast access to the Internet and the popularization of digital cameras, enormous digital images are posted and shared online everyday. Simultaneously, web images are usually organized by topics of events and are often assigned appropriate topic-related text descriptions. Given a set of images along with corresponding texts, a challenging problem is how to utilize the available information to perform image retrieval tasks, such as image classification and image clustering. Previous works on image categorization focus on either adopting text or image features, or simply combining these two types of information together. In this paper, we propose two novel approaches (Dynamic Weighting and Region-based Semantic Concept Integration) to categorize the images under the "supervision" of topic-related text descriptions; In addition, we provide a comparative experimental investigation on utilizing text and image information to tackle image classification. Empirical experiments on a manually collected image dataset (consisting of images related to the events after disasters) demonstrate the efficacy of our proposed classification methods.