In this paper we describe an evolutionary algorithm for assigning students to courses in a situation where each student specifies a set of courses in order of preference, each course has a limited enrollment, and the object is to maximize the overall student satisfaction by assigning each student to a course as high on his or her preference list as possible. Results of using the algorithm on historical data are compared to the success of a human in making the assignments. This work was done as part of a summer undergraduate research project while the second author was still a student. We also report preliminary results for using this problem as the basis for an assignment in a course in Artificial Intelligence.

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.

We present the first effective SAT heuristics for planning with expressive planning languages such as ADL. Recently, SAT heuristics for STRIPS planning have been introduced. In this work we show that the basic ideas in the heuristic can be generalized to actions with conditional effects but without disjunction, and that disjunction requires a more fundamental analysis of the STRIPS heuristic, which, despite complications, will still lead to a natural heuristic which can be implemented efficiently. The experimental analysis shows substantial and systematic improvements over the state of the art in planning with SAT with ADL.

In this paper, we discuss the question of closure conditions for dialogues in three different frameworks: W. C. Mann's DMT framework, Vanderveken's illocutionary theory of discourse and Asher and Lascarides SDRT approach. We are interested in formal frameworks that aim to describe the logical structure of conversations between diversely bounded agents who are — to some extent — rational, intelligent, linguistically competent and who possess some awareness of their environment and some knowledge of the circumstances of their interactions. We use the notion of closure conditions as a benchmark for theory comparison.

We introduce a search engine and information retrieval system for providing access to opinion data. Natural language technology of generalization of syntactic parse trees is introduced as a similarity measure between subjects of textual opinions to link them on the fly. Information extraction algorithm for automatic summarization of web pages in the format of Google sponsored links is presented. We outline the usability of the implemented system, integrated opinion delivery environment (IODE).

The Cultural Geography (CG) model is a governmentowned, open-source agent-based model designed to Balancing the ratio of exploration and exploitation is an address the behavioral response of civilian populations in important problem in reinforcement learning [1]. If you conflict environments [3]. Agents within the CG Model examine the relationship between agent and the select their action according to a constant temperature environment in reinforcement learning, agent has two setting over the course of a model run. To enhance the action selections in its environment: exploration and functionality of agents in selecting their actions and to get exploitation. The agent can choose to explore its more realistic results with better utilities we changed this environment and try new actions in search for better ones constant to a dynamic parameter which depends on time in to be adopted in the future, or exploit already tested actions Time Based Selection and on utility in Aggregate Utility and adopt them.

Paraphrase identification is a core Natural Language Processing task that involves assessing the semantic similarity of two texts. To foster systematic studies of this task, standardized datasets were created on which various approaches could be compared more fairly. However, a better understanding and more precise operational definition of a paraphrase are needed before any further datasets or systematic evaluations of the task of paraphrase identification are proposed. This study develops the concept of paraphrasing as a writing strategy. Six types of paraphrases are defined through the creation of a relatively large corpus of student-generated paraphrases. These paraphrases are analyzed along several dozen linguistic dimensions ranging from cohesion to lexical diversity. The most significant indices from these dimensions were then used to build a prediction model that could identify true and false paraphrases and each of the six paraphrase types.

Community mining is applied in order to identify groups of users which share, e.g., common interests or expertise. This paper presents an approach for mining descriptive patterns in order to characterize communities in terms of their distinctive features: For an efficient discovery approach, we introduce optimistic estimates for obtaining an upper bound for the community quality. We present an evaluation using data from the real-world social bookmarking system BibSonomy.

This paper investigates feature level fusion for enhancing fault detection from vibration signals in an ocean turbine. Changes in vibration signatures from such rotating machinery typically indicate the presence of a problem such as a shift in its orientation or mechanical impact from its environment. We applied feature level fusion to vibration data acquired from two accelerometers attached to a box fan, and then assessed the abilities of twelve well known machine learners to detect changes in state from the raw accelerometer data and from the fused data. Analysis of the performance of these classifiers showed an overall performance improvement in all twelve classifiers in detecting the state of the fan from the fused data versus from the data from the two individual sensor channels.

Suboptimal search algorithms offer shorter solving times by sacrificing guaranteed solution optimality. While optimal searchalgorithms like A* and IDA* require admissible heuristics, suboptimalsearch algorithms need not constrain their guidance in this way. Previous work has explored using off-line training to transform admissible heuristics into more effective inadmissible ones. In this paper we demonstrate that this transformation can be performed on-line, during search. In addition to not requiring training instances and extensive pre-computation, an on-line approach allows the learned heuristic to be tailored to a specific problem instance. We evaluate our techniques in four different benchmark domains using both greedy best-first search and bounded suboptimal search. We find that heuristics learned on-line result in both faster search andbetter solutions while relying only on information readily available in any best-first search.