Intelligent agents in ontology-based applications

1 Abstract—The building of exhaustive ontologies leads to well known problems such as terminology, scope, encoding and context, which can only be resolved in a process of intense communication of the potential users. We propose an environment that enables users to define rules, parameters, constraints for an agent-based system which sustains (self-) organization of small sets of concepts extracted from a specific set of user provided documents and their relations. The system allows users to build or train agents, which carry small ontologies together with specific sample documents, and a generic set of rules, which enables the agents to negotiate their local ontological relations with each other.

Journal of Logic and Computation, 2009

In the article, we present Dynamo (an acronym of DYNAMic Ontologies), a tool based on an adaptive multi-agent system to construct and maintain an ontology from a domain specific set of texts. The originality of our proposal is that the adaptative multi-agent system is used both to represent the ontology itself and to produce the ontology. This enables us to propose a system building and maintaining dynamically an ontology according to interactions with the user (also called the ontologist). We present our system and the mechanisms used to build and maintain the ontology from the texts and for the interactions with the ontologist. We also give results of the evaluation of our system.

The Knowledge Engineering Review, 2002

It is now more than ten years since researchers in the US Knowledge Sharing Effort envisaged a future where complex systems could be built by combining knowledge and services from multiple knowledge bases and the first agent communication language, KQML, was proposed (Neches et al., 1991). This model of communication, based on speech acts, a declarative message content representation language and the use of explicit ontologies defining the domains of discourse (Genesereth & Ketchpel, 1994), has become widely recognised as having great benefits for the integration of disparate and distributed information sources to form an open, extensible and loosely coupled system. In particular, this idea has become a key tenet in the multi-agent systems research community.

Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing, 2003

Developing a knowledge-sharing capability across distributed heterogeneous data sources remains a significant challenge. Ontology-based approaches to this problem show promise by resolving heterogeneity, if the participating data owners agree to use a common ontology (i.e., a set of common attributes). Such common ontologies offer the capability to work with distributed data as if it were located in a central repository. This knowledge sharing may be achieved by determining the intersection of similar concepts from across various heterogeneous systems. However, if information is sought from a subset of the participating data sources, there may be concepts common to the subset that are not included in the full common ontology, and therefore are unavailable for knowledge sharing. One way to solve this problem is to construct a series of ontologies, one for each possible combination of data sources. In this way, no concepts are lost, but the number of possible subsets is prohibitively large. This paper describes a software agent case study that demonstrates a flexible and dynamic approach for the fusion of data across combinations of participating heterogeneous data sources to maximize knowledge sharing. The software agents generate the largest intersection of shared data across any selected subset of data sources. This ontology-based agent approach maximizes knowledge sharing by dynamically generating common ontologies over the data sources of interest. The approach was validated using data provided by five (disparate) national laboratories by defining a local ontology for each laboratory (i.e., data source). In this experiment, the ontologies are used to specify how to format the data using XML to make it suitable for query. Consequently, software agents are empowered to provide the ability to dynamically form local ontologies from the data sources. In this way, the cost of developing these ontologies is reduced while providing the broadest possible access to available data sources.

Proceedings of the 2008 Conference on Formal Ontology in Information Systems Proceedings of the Fifth International Conference, 2008

FAIA covers all aspects of theoretical and applied artificial intelligence research in the form of monographs, doctoral dissertations, textbooks, handbooks and proceedings volumes. The FAIA series contains several sub-series, including "Information Modelling and Knowledge Bases" and "Knowledge-Based Intelligent Engineering Systems". It also includes the biennial ECAI, the European Conference on Artificial Intelligence, proceedings volumes, and other ECCAI-the European Coordinating Committee on Artificial Intelligence-sponsored publications. An editorial panel of internationally well-known scholars is appointed to provide a high quality selection.

2005

The volume aims at providing a comprehensive review of the diverse efforts covering the gap existing between the two main perspectives on the topic of ontologies for multi-agent systems, namely: How ontologies should be modelled and represented in order to be effectively used in agent systems, and on the other hand, what kind of capabilities should be exhibited by an agent in order to make use of ontological knowledge and to perform efficient reasoning with it. The volume collects the most significant papers of the AAMAS 2002 and ...

2008

Abstract:- The complex systems are designed using multi-agent concepts. Agent interaction is complex and requires appropriate models for a communication and cooperation. Also the interaction between the users and the system agents must be done in an efficient way. One of the basic conditions is that to use a convenient "language", a common way of understanding. The ontology is the appropriate concept that allows doing it. The operations on the ontologies cover many of such requirements. Due to the complexity of systems interaction that has an impact on the different ontologies used in them. Our model tries to define a specific operation deriving an ontology form another one. The competence descriptions in education are given as an application. The research for this paper has been partial supported by the project PN II 91-047/2007. Key-Words:- Ontology, competence description, multi-agent systems

Communications of the Association for Information Systems, 2004

A search on Google for the keywords "intelligent agents" will return more than 330,000 hits; "multi-agent" returns almost double that amount. Over 5,000 citations appear on www.citeseer.com. What is agent technology and what has led to its enormous popularity in both the academic and commercial worlds? Agent-based system technology offers a new paradigm for designing and implementing software systems. The objective of this tutorial is to provide an overview of agents, intelligent agents and multi-agent systems, covering such areas as: 1. what an agent is, its origins and what it does, 2. how intelligence is defined for and differentiates an intelligent agent from an agent, 3. how multi-agent systems coordinate agents with competing goals to achieve a meaningful result, and 4. how an agent differs from an object of a class or an expert system. Examples are presented of academic and commercial applications that employ agent technology. The potential pitfalls of agent development and agent usage are discussed.

IEEE Internet Computing, 1997

1999

This paper discusses an approach t o adding explicit ontologies in multiagent systems based on logic programming. Ontologies are content t heories about knowledge domains, developed to clarify knowledge structure and e nhancing knowledge reuse and s t andardization. Ontologies allow explicit organization of knowledge in agent-based applications, and u nambiguous description of characteristics and p r o perties of agents. We c o n s i d er in detail the u s e of explicit ontologies in CaseLP, a d eclarative logical framework f o r p r o t otyping agent-based applications. Our running example comes from the domain of sport results, to which CaseLP has been applied. Concepts s u ch a s s p o r t , competition, competitors are included in the o n tology, a s w ell as relationships that r e l a te t hese concepts. We i n troduce an agent level ontology to f o r m alize attributes and f u nctionalities of CaseLP agents, for example their kind, architecture and services, either at t he domain level or at t he agent level. Domain and agent level ontologies are exploited in CaseLP to perform semantic checks of agent architectural descriptions, to c heck agent b e havioural rules used by an agent t o provide i t s services, and a s a k n o wledge repository to b e e x p l o i t ed during agent execution.