Distributed multi‐agent information filtering—A comparative study

Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev R.; Mostafa, Javed; Palakal, Mathew

doi:10.1002/asi.20176

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…In recent years, Mukhopadhyay et al examined application of distributed agents to information filtering and classification tasks [19,20]. Their research using an Acquaintance List learning algorithm and other collaboration strategies showed that learning helped distributed agents do classification better without consuming too much communication resources.…”

Section: Related Workmentioning

confidence: 99%

Collaborative classifier agents

Mostafa

2007

Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries

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We developed a multi-agent framework where agents had limited/distributed knowledge for document classification and collaborated with each other to overcome the knowledge distribution. Each agent was equipped with a certain learning algorithm for predicting potential collaborators, or helping agents. We conducted experimental research on a standard news corpus to examine the impact of two learning algorithms: Pursuit Learning and Nearest Centroid Learning. For a fundamental retrieval operation, namely classification, both algorithms achieved competitive classification effectiveness and efficiency. Subsequently, the impact of the learning exploration rate and the maximum collaboration range on classification effectiveness and efficiency were examined. Close investigation of agent learning dynamics revealed increasing and stabilizing patterns that were enhanced by the learning algorithms.

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Section: Related Workmentioning

confidence: 99%

Collaborative classifier agents

Mostafa

2007

Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries

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“…It identifies the desirable features of an IIR agent, including intelligent search, navigation guide, auto-notification, personal information management, personal preferred interface, and tools for easy page-reading. In [3] discusses two distributed information filtering approaches that are distributed with respect to knowledge or functionality, to overcome the limitations of single-agent centralized information filtering. Large-scale experimental studies involving the well-known TREC data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches.…”

Section: Multi-agentmentioning

confidence: 99%

Intelligent Retrieval Agent based on Distributed Environment

Kim

2006

Fourth International Conference on Software Engineering Research, Management and Applications (SERA'06)

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Intelligent search agent is popularly used for searching relative information in the Internet and there are a lot of tools that are used to satisfy the needs of the users. These intelligent agents are independent to each other and there is insufficient cooperation to make an efficient search of information in the distributed environment. Therefore, a typical search agent is difficult to use in the distributed environment and can contain irrelevant information for the users. To solve the problem in a distributed environment, we use the CORBA architecture to create an agency by the broker agent and provide more reliable information to the users. Also, the propose intelligent information search system used the NFC and filtering technique by the multi-agents for a rapid and reliable searching of information.

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“…This agent module embedded in information retrieval system can analyze a certain user's behaviors, e.g., navigation (or browsing) and interactions for supporting them (Cothey, 2002;Hargittai, 2002). Such agent system can carry out manipulating the searching results by not only sorting (or ranking) them in order of relevance (Deters, 2001;Ruthven et al, 2003), but also filtering out noisy information from the list of them (Jung and Jo, 2003;Mukhopadhyay et al, 2005). Moreover, in multi-agent systems (MAS), a mediator agent plays an important role of coordinating the personal agents, in order to allow them to exchange meaningful information (or knowledge) between each other.…”

Section: Introductionmentioning

confidence: 99%

Ontological framework based on contextual mediation for collaborative information retrieval

Jung¹

2006

Inf Retrieval

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On the heterogeneous web information spaces, users have been suffering from efficiently searching for relevant information. This paper proposes a mediator agent system to estimate the semantics of unknown web spaces by learning the fragments gathered during the users' focused crawling. This process is organized as the following three tasks; (i) gathering semantic information about web spaces from personal agents while focused crawling in unknown spaces, (ii) reorganizing the information by using ontology alignment algorithm, and (iii) providing relevant semantic information to personal agents right before focused crawling. It makes the personal agent possible to recognize the corresponding user's behaviors in semantically heterogeneous spaces and predict his searching contexts. For the experiments, we implemented comparison-shopping system with heterogeneous web spaces. As a result, our proposed method efficiently supported the users, and then, network traffic was also reduced.

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Distributed multi‐agent information filtering—A comparative study

Cited by 8 publications

References 15 publications

Collaborative classifier agents

Collaborative classifier agents

Intelligent Retrieval Agent based on Distributed Environment

Ontological framework based on contextual mediation for collaborative information retrieval

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