Inexperienced users typically obtain one of three possible outcomes when they search for online information: they are buried under an information avalanche, they are unable to locate any useful information at all, or they find what they need in roughly the amount they need. Unfortunately, the latter outcome is the most rare. Unfamiliarity with search tactics creates difficulties for many users of online retrieval systems. When faced with poor results, even experienced searchers may use vocabulary incorrectly and often fail to reformulate their queries. Far from being the answer to everyone's information dreams, distributed sources of online information, i.e., the World Wide Web (WWW), compound the problem and may often turn into an information nightmare. To address this problem, intelligent online search assistants, or agents, are being developed for information retrieval applications. There are many approaches, both theoretical and implemented, to using intelligent software agents for...

In the SmartPush project professional editors add semantic metadata to information flow when the content is created. This metadata is used to filter the information flow to provide the end users with a personalized news service. Personalization and delivery process is modeled as software agents, to whom the user delegates the task of sifting through incoming information. The key components of the SmartPush architecture have been implemented, and the focus in the project is shifting towards a pilot implementation and testing the ideas in practice.

Although the number and availability of electronic information sources are increasing, current information technology requires manual manipulation and userspecification of all details. Once accessed, information must be filtered in the context of the user's task. Current systems lack the ability to get contextual information or use it to automate filtering. At Carnegie Mellon University, we have been engaged in the RETSINA project, which aims to develop a reusable multiagent software infrastructure that allows heterogeneous agents on the Internet, possibly developed by different designers, to collaborate with each other to manage information in the context of user-specified tasks. In this chapter, we will provide a brief overview of the whole system and then focus on its capability for in-context information management. This research has been supported in part by DARPA contract F30602-98-2-0138, and by ONR Grant N00014-96-1222. 1 Introduction The Web is full of information resourc...

We propose an approach to achieve appropriate exchange of services and data in distributed systems subject to semantic heterogeneity. We assume differentiated ontologies: that terms have formal definitions as concepts related to other concepts, that local concepts inherit from concepts that are shared, and that most or all primitives are shared. We then develop measures of description compatibility using the structure of the source and target definitions. We evaluate these measures by generating description-logic ontologies in artificial worlds. In our simulations, the "meaning" of a concept is its denotation in a finite universe of instances. The accuracy of the description compatibility measures can thus be judged by their success in predicting the overlap of concept denotations. Description compatibility can be used to guide agent search for services across communities that subscribe to differentiated ontologies. 1 1 Introduction We are interested in the semantics of agent commu...

The promises of network-accessible information are increasingly difficult to achieve. These difficulties are due to a variety of causes, such as, the rapid growth in the volume of network-available information and the increasing complexity, diversity and terminological fluctuations of the different information sources available. This paper presents a conceptual architecture for the organisation information space across collections of component systems in multi-databases that provides serendipity, exploration and contextualisation support so that users can achieve logical connections between concepts they are familiar with and schema terms employed in multi-database systems. Large-scale searching for multi-database schema information is guided by a combination of lexical, structural and semantic aspects of schema terms in order to reveal more meaning both about the contents of a requested information term and about its placement within the distributed information space. 1

In this paper a novel approach concerned with the general framework of Information Management, is presented. We use a Multi-Agent System to cope with the problem of Distributed Information Retrieval. The Distributed Information Retrieval task deals with the collection of information from multiple and usually heterogeneous information sources that exist in a distributed environment, which in our case is the World Wide Web. 1 Overview of the system's architecture The advent of large wide-area networks, Internet is the most characteristic example, has caused a vast increase both in the information availability and in the number of the information sources. This evolution offers great promise for obtaining and sharing diverse information conveniently. However, the multitude, diversity and the dynamic nature of on-line information sources make accessing any specific piece of information an extremely difficult task. One way to address these issues is to use information agents. These Distribu...

We describe the Do-I-Care agent, which uses machine learning to detect “interesting ” changes to Web pages previously found to be relevant. Because this agent focuses on changes to known pages rather than discovering new pages, we increase the likelihood that the information found will be interesting. The agent’s accuracy in finding interesting changes and in learning is improved by exploiting regularities in how pages are changed. Additionally, these agents can be used collaboratively by cascading them and by propagating interesting findings to other users ’ agents.

The Web is a vast, dynamic source of information and resources. Because of its size and diversity, it is increasingly likely that if the information one seeks is not already there, it will be soon. Unfortunately, finding the right places to look, and persistently revisiting those places until the information is available is an onerous task. In this paper, we describe Do-I-Care (DICA), an agent that uses both technical and social mechanisms to ease the burden of locating "interesting" new information and resources on the Web. DICA monitors Web pages previously found by the agent's user to be relevant for any changes. It then compares these changes against a user model, classifies them as potentially interesting or not, and reports the interesting changes to the user. The user model is derived by accepting relevance feedback on changes previously found. Because the agent focuses on changes to known pages rather than discovering new pages, we increase the likelihood that the information ...

In this paper, a Multi-Agent System for Distributed Information Discovery and Retrieval is presented. Data mining enables extraction of hypermedia information from heterogeneous information sources existing on the World Wide Web. The goal is to construct a distributed deductive database system of meta-information about these sources using an open architecture and an extensible ontology of descriptive attributes. Each component database is maintained by an information source agent. Prolog-style queries, entered via a user agent, express user requests for information items of interest. The query evaluation is performed utilising intermediary architectural layers of facilitator, matchmaker and descriptor agents that enable intelligent query routing exhibiting co-operative answering behaviour.

The PRISM (Polar Radar for Ice Sheet Measurement) project is developing mobile, autonomous sensors for the measurement and study of the mass balance of the polar ice sheets. These sensors consist of intelligent radars integrated into robotic vehicles. They autonomously decide where and how to measure by examining a variety of information including onboard sensor data and collections of a priori knowledge. These data include the health and status of the rover, health and status of the sensors themselves, the state of the environment as measured by the sensors, satellite measurements of the area indicating expected ice sheet motion, and so on. All of this information is used to direct the data collection process by allowing for the dynamic configuration of sensors and the motion of the rovers that carry them. The PRISM intelligent sensor and rover control system is built upon a multiagent collaborative architecture that involves a number of distinct data collection and data dissemination agents functioning continuously and autonomously in a distributed computing framework. A critical component of this system is an agent service called the Matchmaker. The Matchmaker coordinates requests for information and services within the agent community and allows decision-making agents to locate and communicate with the data source-agents that can fulfill these requests.