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Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large-scale information infrastructure, e.g., digital libraries. To provide large-scale information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single-agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium-scale applications. In this article, we discuss two distributed (multi-agent) 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 (Text REtrieval Conference) data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches.

There has been considerable interest in recent years in providing automated information services, such as information classification, by means of a society of collaborative agents. These agents augment each other's knowledge structures (e.g., the vocabularies) and assist each other in providing efficient information services to a human user. However, when the number of agents present in the society increases, exhaustive communication and collaboration among agents result in a large communication overhead and increased delays in response time. This paper introduces a method to achieve selective interaction with a relatively small number of potentially useful agents, based on simple agent modeling and acquaintance lists. The key idea presented here is that the acquaintance list of an agent, representing a small number of other agents to be collaborated with, is dynamically adjusted. The best acquaintances are automatically discovered using a learning algorithm, based on the past history of collaboration. Experimental results are presented to demonstrate that such dynamically learned acquaintance lists can lead to high quality of classification, while significantly reducing the delay in response time.

Recent advances in processor, networking and software technologies have made distributed computing a reality in today's world. Distributed systems offer many advantages, ranging from a higher performance to the effective utilization of physically dispersed resources. Many diverse application domains can benefit by exploiting principles of distributed computing. Information filtering is one such application domain. In this article, we present a design of a homogeneous distributed multi-agent information filtering system, called D-SIFTER. D-SIFTER is based on the language-dependent model of Java RMI. The detailed design process and various experiments carried out using D-SIFTER are also described. The results indicate that the distributed inter-agent collaboration improves the overall filtering performance.

The problems of joint limit, and minimum roll and pitch restoring moment during UVMS operation is studied in the paper. As for the problem that too many subtasks will increase the number of fuzzy rules in traditional task prioritization algorithm, the paper proposed a new fused multiple subtask motion planning method by fusing the joint limit information into the weighted pseudo-inverse matrix. A numerical simulation is carried out to demonstrate the efficiency of the proposed method.

Information services such as searching, retrieval, and filtering are playing a dominant role in our life during the current information age. One critical functionality of these information services is to obtain effective classification for input documents. Thesaurus(vocabulary)-based document representation followed by clustering constitutes a popular approach to document classification. However, two alternatives exist to construct the information classificationsystem. The first one uses a single, monolithic, huge thesaurus and classifies all documents by one centralized machine. The second one exploits distributed computing environmentsby allowing multiple agents with small thesauri to collaborate with each other over a computer network. The objective of this paper is to compare these two approaches (i.e., single-agent and multi-agent) in terms of various criteria including response time, quality of classification, and economic/privacy considerations. Two experimental studies, involving classification of Computer Science and Medline documents, are presented to compare the performanceof a single-agent system with that of a multi-agent system in real world settings. These results indicate that a collaborative multi-agent system constitutes a attractive methodologyfor classifying a large volume of information efficiently, when the thesaurus is large.