CL - ProQuest
500 ấn phẩm có sẵn
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chỉ phục vụ cho Cán bộ ĐHQG TP.HCM và các trường thành viên. Để đọc nội dung
toàn văn, xin liên hệ:
Phòng Phục vụ Độc giả, Thư viện Trung tâm - ĐHQG TP.HCM,
ĐT: 37242181, ext.2935
hoặc E-Mail:
phucvu@vnuhcm.edu.vn
Những tài liệu tải lên gần đây
- Ấn phẩmAn integrated approach to rule refinement for instructable knowledge-based agents(George Mason University, 2007) Boicu, Cristina E.Our research addresses the problem of developing knowledge-based agents that incorporate the knowledge of subject matter experts. Our approach is to develop a learning and problem solving agent, which can be directly taught by a subject matter expert by explaining it how to solve specific problems, and by critiquing its attempts to solve new problems. Because the accuracy of the agent's reasoning depends on the rules from its knowledge base, the process of rule improvement is very important. This dissertation presents an integrated set of methods to assist a subject matter expert in refining the rules from an agent's knowledge base, to incorporate his problem solving expertise. This dissertation presents methods to discover incompletely refined rules and to propose suggestions for their improvement; to guide the expert during the rule refinement process, focusing his attention on the reasoning steps that need to be analyzed; to refine the applicability condition of over-generalized and over-specialized rules; to modify a learned rule using a lazy refinement method; and to extend the agent's ontology to eliminate the rules' exceptions. These methods complement each other and create an integrated approach to the rule refinement problem in an evolving representation space, resulting in refined problem solving rules, which will assure a higher degree of correctness of the solutions generated by the agent. These rule refinement methods have been implemented in the Disciple learning agent shell, and have been evaluated during several experiments in complex application domains.
- Ấn phẩmQuantum Monte Carlo calculations applied to magnetic molecules(Iowa State University, 2006) Engelhardt, Larry Paul
- Ấn phẩmRobust semantic role labeling(University of Colorado at Boulder, 2006) Pradhan, Sameer S.The natural language processing community has recently experienced a growth of interest in domain independent semantic role labeling. the process of semantic role labeling entails identifying all the predicates in a sentence, and then, identifying and classifying sets of word sequences, that represent the arguments (or, semantic roles) of each of these predicates. In other words, this is the process of assigning a WHO did WHAT to WHOM, WHEN, WHERE, WHY, HOW etc. structure to plain text, so as to facil itate enhancements to algorithms that deal with various higher-level natural language processing tasks, such as - information extraction, question answering, summarization, machine translation, etc., by providing them with a layer of semantic structure on top of the syntactic structure that they currently have access to. In recent years, there have been a few attempts at creating hand-tagged corpora that encode such information. Two such corpora are FrameNet and PropBank. One idea behind creating these cor¬pora was to make it possible for the community at large, to train supervised machine learning classi ers that can be used to automatically tag vast amount of unseen text with such shallow semantic information. There are various types of predicates, the most common being verb predicates and noun predicates. Most work prior to this thesis was focused on arguments of verb predicates. This thesis primarily addresses three issues: i) improving performance on the standard data sets, on which others have previously reported results, by using a better machine learning strategy and by incorporating novel features, ii) extending this work to parse arguments of nominal predicates, which also play an important role in conveying the semantics of a passage, and iii) investigating methods to improve the robustness of the classi er across di erent genre of text.
- Ấn phẩmAutomatic text classification using a multi-agent framework(Indiana University, 2006) Fu, Yueyu
- Ấn phẩmAENeID: Agent Emergent Network Intrusion Detection(Nova Southeastern University, 2007) Gowing, Glyn ThomasComputer networks continue to be the targets of numerous types of attacks, which can expose sensitive data or simply deny service to legitimate users. Current intrusion detection technologies utilize signature bases that allow them to rapidly and accurately identify known attacks. This, however, leaves them vulnerable to previously unknown attacks. An adaptive approach, capable of recognizing novel attacks, is warranted. The proposed research presents an adaptive agent-based intrusion detection sys¬tem. The approach is innovative in several respects: the agents self-organize into a scale-free peer-to-peer network, emergent behavior is facilitated by allowing simple communication between the agents, and the system is adaptive both to recognize new attacks and to the loss of agents, and can withstand the loss of up to 25% of its agents without impairing its functionality. This combination of innovations represents a significant advance in the application of artificial intelligence techniques to intrusion detection.
- Ấn phẩmQuantitative organizational modeling and design for multi-agent systems(University of Massachusetts Amherst, 2006) Horling, BryanAs the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants’ interactions. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents particular and different roles, responsibilities and peers. These additional constraints can allow agents to operate effectively within a large-scale system, with little or no sacrifice in utility. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand and model the behavior of candidate designs. In the multi-agent systems community, relatively little attention has been paid to understanding and comparing organizations at a quantitative level. In this thesis, I show that it is possible to develop such an understanding, and in particular I show how quantitative information can form the basis of a predictive, proscriptive organizational model. This can in turn lead to more efficient, robust and context-sensitive systems by increasing the level of detail at which competing organizational designs are evaluated. To accomplish this, I introduce a new, domain-independent organizational design representation able to model and predict the quantitative performance characteris¬tics of agent organizations. This representation, capable of capturing a wide range of multi-agent characteristics in a single, succinct model, supports the selection of an appropriate design given a particular operational context. I demonstrate the repre¬sentational capabilities and efficacy of the language by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. In addition to their predictive ability, these same mod¬els also describe the range of possible organizations in those domains. I show how general search techniques can be used to explore this space, using those quantitative predictions to evaluate alternatives and enable automated organizational design.