Knowledge Components Using Agent Technology for a Diagnostic System (original) (raw)
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An ontology-based software agent system case study
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.
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Ibm Journal of Research and Development, 2005
A problem of knowledge management in information systems designed for open heterogeneous environments is considered in the paper. Agent approach is discussed as a design foundation and technological solution supporting the realization of such systems. A generic architecture of the system with explicit knowledge is proposed to aid the construction of decentralized decision-support systems. Selected implementation details of the realized platform for rule-based knowledge exchange conclude the work.
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Aircraft diagnosis is a highly complex topic. Many knowledge sources are required and have to be integrated into a diagnosis system. This paper describes the instantiation of a multi-agent system for case-based aircraft diagnosis based on the SEASALT architecture. This system will extend an existing rulebased diagnosis system, to make use of the experience on occurred faults and their solutions. We describe the agents within our diagnosis system and the knowledge modeling for the case-based reasoning systems. In addition we give an overview over the current implementation.
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In recent years there has been considerable interest in the possibility of building complex problem solving systems as groups of cooperating experts. This has led us to develop a multiagent expert systems capable to run on servers that can support a large group of users (clients) who communicate with the system over the network. The system provides an architecture to coordinate the behavior of several specific agent types. Two types of agents are involved. One type works on the server computer and the other type works on the client computers. The society of agents in our system consists of expert systems agents (diagnosis agents, and a treatment agent) working on the server side, each of which contains an autonomous knowledge-based system. Typically, agents will have expertise in distinct but related domains. The whole system is capable of solving problems, which require the cumulative expertise of the agent community. Besides to the user interface agent who employs an intelligent data collector, so-called communication model in KADS, working on the client sides. We took the advantage of a successful pre-existing expert systems-developed at CLAES (Central Laboratory for Agricultural Expert Systems, Egypt)-for constructing an architecture of a community of cooperating agents. This paper describes our experience with decomposing the diagnosis expert systems into a multi-agent system. Experiments on a set of test cases from real agricultural expert systems were preformed. The expert systems agents are implemented in Knowledge Representation Object Language (KROL) and JAVA languages using KADS knowledge engineering methodology on the WWW platform. q
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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.
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