Browsing by Author "Koggalahew, D.N."

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    Collaborative Knowledge Centralization Approach via self Propagating Multi Agent Community Development
    (Uva Wellassa University of Srilanka, 2011) Koggalahew, D.N.; Amararachchi, J.L.; Pilapitiya, S.U.; Tharanga, K.G.D.
    Note: See the PDF Version Most researchers in Artificial Intelligence to date, have dealt with developing theories, techniques, and systems to study and understand the behavior and reasoning properties of a single cognitive entity. Al has matured, and its' endeavors were capable of addressing more complex, realistic, and large-scale problems. Such problems are beyond the capabilities of an individual agent. The capacity of an intelligent agent is limited by its knowledge and its computing resources Sycara, 1998). Multi agent systems can be defined as loosely coupled networks of independent entities called agents, which have individual capabilities, knowledge and resources, and which interact to share their knowledge and resources, and to solve problems beyond their individual capabilities (Wikipedia, 2010). The issue of knowledge sharing has been an important topic in multi-agent research. SESEME will address most of the above mentioned limitations in this domain and the final deliverable will be an agent society which is capable of self-learning and training new agents. Agents' communication is one of the defining characteristics of a multi agent system. In traditional linguistic analysis, the communication is taken to have a certain form (syntax), to carry a certain meaning (semantics), and to be influenced by various circumstances of the communication. SESEME can be differentiated from many other past efforts that have been carried on. SESEME addresses the problem of self-learning by agent itself (Capable of taking the knowledge as it is and no human enrolment).Efficient Methodology of Knowledge Representation using ontology and its own experience. Introducing a new methodology of knowledge distribution and train other agents. Instantiate new agents relevant to the domain. The system starts its learning process once it receives a text document (in .doc, pdf and .html formats) or even it receives an URL from the domain expert. First the system identifies the given content by using natural language processing and it ignores the ambiguity, complexity and the conflicts among the read content. The read content will be used to create or update its knowledge over the specified domain. The Centralized Self learning module (CESLM) is been facilitated with some additional features like adoption of existing ontology and domain experts feedbacks. Each sub agent consists of an ontology that represents its basic knowledge retrieved from CESLM and the system facilitates the updating of sub agent's knowledge