Intelligent Information Agents Research Articles

Demos

In today's dynamic network environments like smart cities, information evolves in distributed resources and in different forms or modalities. An agent or a system that wants to be actively part of this evolution has to be interactive, adaptive, autonomous and intelligent. The paper presents a new version of a distributed model-framework (called Demos) based on autonomous, intelligent agents with anticipatory responses. The first version of the system was proposed by , and the new features (LG Graphs, SPNs and NNs) were embedded in the version presented here. The Demos model is mainly based on the development of an adaptive distributed knowledge base system. Knowledge is represented in a form of frames with internal stochastic Petri-net graph for local representations (KR). A major advantage for the Demos model having a distributed (possibly across the net) is the adaptive knowledge base. Here the authors present the design of an adaptive knowledge model and its challenge, which lays principally on how to learn new knowledge by synthesising SPN forms, and how to develop anticipatory responses at an agent's site. The development of the Demos prototype has a great range of applicability, such as in autonomous negotiating teams, autonomous distributed units for energy efficient distribution, autonomous multiple mobile robots space exploration and maps generation, autonomous intelligent information agents (WWW), automatic information synthesis and fusion, etc. Here the authors have used this model for management of the electric power on a grid of a smart city.

Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs

The use of agent technology in a dynamic environment is rapidly growing as one of the powerful technologies and the need to provide the benefits of the Intelligent Information Agent technique to massive open online courses, is very important from various aspects including the rapid growing of MOOCs environments, and the focusing more on static information than on updated information. One of the main problems in such environment is updating the information to the needs of the student who interacts at each moment. Using such technology can ensure more flexible information, lower waste time and hence higher earnings in learning. This paper presents Intelligent Topic-Based Information Agent to offer an updated knowledge including various types of resource for students. Using dominant meaning method, the agent searches the Internet, controls the metadata coming from the Internet, filters and shows them into a categorized content lists. There are two experiments conducted on the Intelligent Topic-Based Information Agent: one measures the improvement in the retrieval effectiveness and the other measures the impact of the agent on the learning. The experiment results indicate that our methodology to expand the query yields a considerable improvement in the retrieval effectiveness in all categories of Google Web Search API. On the other hand, there is a positive impact on the performance of learning session.

Adaptivity: A Quality Goal for Agent-Oriented Models?

If software systems are to behave more intelligently, they will need to be able to adapt to the changing external world. The agent-oriented paradigm offers the potential to build adaptive systems. This paper explores the nature of adaptivity in multi-agent systems by exploring how to represent adaptivity in agent-oriented models. Rather than focussing on mechanisms for individual agents to be highly adaptive, we advocate regarding adaptivity as a quality goal of an overall multi-agent system. Discussing agent-oriented models should help guide where design tradeoffs need to be made. Learning can be approached in a similar way to adaptivity, where learning is viewed as an attribute of the system rather than a fundamental property of individual agents. Examples are given from use of automated teller machines at mobile sites and intelligent information agents.

SEQUENTIAL PATTERN MINING AND NONMONOTONIC REASONING FOR INTELLIGENT INFORMATION AGENTS

With the explosive growth of information available on the Internet, more effective data mining and data reasoning mechanism is required to process the sheer volume of information. Belief revision logic offers the expressive power to represent information retrieval contexts, and it also provides a sound inference mechanism to model the nonmonotonicity arising in changing retrieval contexts. Contextual knowledge for information retrieval can be extracted via efficient sequential pattern mining. We present a pattern taxonomy extraction model which efficiently performs the task of discovering descriptive frequent sequential patterns by pruning the noisy associations. This paper illustrates a novel approach of integrating the sequential data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Initial experiments show that our belief revision logic and sequential pattern mining based intelligent information agents outperform the vector space model based information agents. Our work opens the door to the development of next generation of intelligent information agents to alleviate the information overload problem.

User profiling in personal information agents: a survey

Personal information agents have emerged in the last decade to help users to cope with the increasing amount of information available on the Internet. These agents are intelligent assistants that perform several information-related tasks such as finding, filtering and monitoring relevant information on behalf of users or communities of users. In order to provide personalized assistance, personal agents rely on representations of user information interests and preferences contained in user profiles. In this paper, we present a summary of the state-of-the-art in user profiling in the context of intelligent information agents. Existing approaches and lines of research in the main dimensions of user profiling, such as acquisition, learning, adaptation and evaluation, are discussed.

Using methods of declarative logic programming for intelligent information agents

At present, the search for specific information on the World Wide Web is faced with several problems, which arise on the one hand from the vast number of information sources available, and on the other hand, from their intrinsic heterogeneity, since standards are missing. A promising approach for solving the complex problems emerging in this context is the use of multi-agent systems of information agents, which cooperatively solve advanced information-retrieval problems. This requires advanced capabilities to address complex tasks, such as search and assessment of information sources, query planning, information merging and fusion, dealing with incomplete information, and handling of inconsistency. In this paper, our interest lies in the role which some methods from the field of declarative logic programming can play in the realization of reasoning capabilities for information agents. In particular, we are interested to see how they can be used, extended, and further developed for the specific needs of this application domain. We review some existing systems and current projects, which typically address information-integration problems. We then focus on declarative knowledge-representation methods, and review and evaluate approaches and methods from logic programming and nonmonotonic reasoning for information agents. We discuss advantages and drawbacks, and point out the possible extensions and open issues.

Ontology negotiation between intelligent information agents

This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assumptions – to communicate in solving tasks when they encounter each other on the web. In particular, we have focused on information search tasks.We have developed a protocol that allows agents to discover ontology conflicts and then, through incremental interpretation, clarification and explanation, establish a common basis for communicating with each other. We have implemented this protocol in a set of Java classes that can be added to a variety of agents, irrespective of their underlying ontological assumptions. We have demonstrated the use of the protocol, through this implementation, in a test-bed that includes two large scientific archives: NASA's Global Change Master Directory and NOAA's Wind and Sea Index.This paper presents an overview of different methods for resolving ontology mismatches and motivates the Ontology Negotiation Protocol (ONP) as a method that addresses some problems with other approaches. Much remains to be done. The protocol must be tested in larger and less familiar contexts (for example, numerous archives that have not been preselected) and it must be extended to accommodate additional forms of clarification and ontology evolution.

Information agent technology for the Internet: A survey

The vast amount of heterogeneous information sources available on the Internet demands advanced solutions for acquiring, mediating, and maintaining relevant information for the common user. Intelligent information agents are autonomous computational software entities that are especially meant to (1) provide pro-active resource discovery, (2) resolve information impedance of information consumers and providers, and (3) offer value-added information services and products. These agents are supposed to cope with the difficulties associated with the information overload of the user, preferably just in time. Based on a systematic classification of intelligent information agents, this paper presents an overview of the basic key enabling technologies needed to build such agents, and respective examples of information agent systems currently deployed on the Internet.

Intelligent information agents: Review and challenges for distributed information sources

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 information retrieval purposes. These approaches range from desktop agents specialized for a single user to networks of agents used to collect data from distributed information sources, including Web sites. This article presents an overview of intelligent software agents in information retrieval, including an explanation of agents and agent architectures, and presents several agent systems. We distinguish between agents as individual entities, whose properties and characteristics we describe separately, and agent systems as collections of agents utilized for information retrieval tasks, which we discuss in terms of individual implementations. © 1998 John Wiley & Sons, Inc.

Intelligent information agents: Review and challenges for distributed information sources

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 information retrieval purposes. These approaches range from desktop agents specialized for a single user to networks of agents used to collect data from distributed information sources, including Web sites. This article presents an overview of intelligent software agents in information retrieval, including an explanation of agents and agent architectures, and presents several agent systems. We distinguish between agents as individual entities, whose properties and characteristics we describe separately, and agent systems as collections of agents utilized for information retrieval tasks, which we discuss in terms of individual implementations. © 1998 John Wiley & Sons, Inc.

INFORMATION AGENTS

France Telecom, the owner of Questel ORBIT, has become the latest large company to invest in intelligent information agents which are promising to revolutionise the way online services are searched. In France Telecom's case, it has picked a business with some pedigree — General Magic, in which it has just taken a minority stake, was spun off from Apple and now numbers AT&T, Motorola and Sony among its investors. Based in California, it employs just 120 staff but is at the cutting edge in the race to develop information agents — software that can retrieve data intelligently from multiple information sources without any human intervention.

AN ORGANIZATIONAL FRAMEWORK FOR COOPERATING INTELLIGENT INFORMATION SYSTEMS

There is a growing belief that the next generation of information processing systems will be based on the paradigm of Intelligent and Cooperative Information Systems (ICISs). Such systems will involve information agents — distributed over the nodes of a common communication network — which work in a synergistic manner by exchanging information and expertise, coordinating their activities and negotiating how to solve parts of a common information-intensive problem. Along with motivating the importance of such systems the paper gives an overview of related research areas, notably Distributed Artificial Intelligence (DAI) and Distributed Databases (DDBs), and presents a generic architecture which views an ICIS as a community of communicating and cooperating intelligent information agents.