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Agent and Multi-Agent system is one of the main research directions in distributed artificial intelligence. It has been considered that the Agent technology is an important method to build the model of distributed industrial system, the most natural way to design and implement the distributed Intelligent Manufacturing environment, and one of the significant technologies to construct the manufacturing system of next generation. The manufacturing system based on Agent is the specific application of the theory and method of multi-Agent system in manufacturing domain. On the basis of briefly summarizing the technology of Agent and Multi-Agent system, the architecture of Intelligent Manufacturing System based on Multi-Agent is put forward, among which, agent represents the basic processing entity. The scheduling optimization algorithm of Intelligent Manufacturing is provided, which is feasible proved by the result of stimulation experiment. Compared with the traditional artificial scheduling, it has distinct advantages. Finally, the model of communication and negotiation between Agents in the Intelligent Manufacturing System is studied, which utilizes the features of Agent such as autonomy, social interaction, response capability, preliminary capability, etc so that Intelligent Manufacturing System could make distributed decisions. The cooperation of system is completed by communication and negotiation. During the process of interaction, Agent is endowed with a role to meet the demand of flexibility of building models in the complicated process of Intelligent Manufacturing, as well as to construct an extended frame for reusable components. [ABSTRACT FROM AUTHOR]

For a web-based dynamic learning environment, personalized support for learners becomes more important. In order to achieve optimal efficiency in a learning process, individual learner΄s cognitive learning style should be taken into account. It is necessary to provide learners with an individualized learning support system. In this paper, a framework of web learning system based on genetic algorithm and Pervasive Agent Ontology is presented. The proposed framework utilizes genetic algorithm for representing and extracting a dynamic learning process and learning pattern to support students΄ deep learning in web-based learning environment. Aiming at the problems in current Web environment, we put forward the information integration method of Semantic Web based on Pervasive Agent Ontology (SWPAO method), which will integrate, analyze and process enormous web information and extract answers for students on the basis of semantics. And experiments do prove that it is feasible to use the method to develop an individual Web-based learning system, which is valuable for further study in more depth. [ABSTRACT FROM AUTHOR]

The precision and efficiency of the similarity computing of documents is the foundation and key of other documents processing. In this paper, the DF and TF-IDF algorithms are improved. First, DF΄s time complexity is linear which suits mass documents processing, but it has the fault that exceptional useful features may be deleted, so we make up that by adding the count of the words at the important places. Second, we rectify the weight of feature by the result of feature selection phase. In this way, we improve the precision of documents similarity without adding much time and space complexity. [ABSTRACT FROM AUTHOR]

Semantic web and ontology are the key technologies of Question Answering system. Ontology is becoming the pivotal methodology to represent domain-specific conceptual knowledge in order to promote the semantic capability of a QA system. In this paper we present a QA system in which the domain knowledge is represented by means of Ontology. In addition, a Chinese Natural Language human-machine interface is implemented mainly through a NL parser in this system. An initial evaluation result shows the feasibility to build such a semantic QA system based on Ontology, the effectivity of personalized semantic QA, the extensibility of ontology and knowledge base, and the possibility of self-produced knowledge based on semantic relations in the ontology.And experiments do prove that it is feasible to use the method to develop a QA System, which is valuable for further study in more depth. [ABSTRACT FROM AUTHOR]