Data Mining for Decision Making in Multi-Agent Systems

The intelligent agent paradigm has generated such a remarkable interest in many application domains over the last two decades. It is growing to be a continuously evolving and expanding area. Agents, Software Agents or Intelligent Agents are intelligent in the sense that they are adaptive, independent, and possess reasoning capability. They can plan and execute tasks in cooperation with other agents in order to satisfy their goals. A MultiAgent System (MAS) is defined as a loosely coupled network of problem solvers that work together to solve problems that are beyond the individual capabilities or knowledge of each problem solver (Agent). The increasing interest in MAS research is due to significant advantages inherent in such systems, including their ability to solve problems that may be too large for a centralized single agent, provide enhanced speed and reliability, and tolerate uncertain data and knowledge. Some of the key research issues related to problem-solving activities of agents in a multi-agent system MAS are in the areas of coordination, negotiation, and communication. With advances in Web technologies, collaborative applications are now server based and the user interface is typically a Web browser. Thus, a collaborative application can be a Webbased solution that runs on a local server that allows people communicate and work together, share information and documents, and talk in real-time over the Internet. Recently, much research has been conducted in distributed artificial intelligence and collaborative applications. Several interesting methodologies and systems have been developed in areas such as distributed multi-agent systems for decision support, web search and information retrieval, information systems modeling, and supply chain management. This chapter considers applying different data mining techniques for the decision making process in a Multi-Agent System for Collaborative E-learning (MASCE). The dynamism in elearning can be made more powerful with the help of intelligent agents. Intelligent agents – the so called e-assistants or helper programs can reside inside a computer and make the learning in e-learning occur dynamically to suit the need of the user. They can track the user’s likes and dislikes in different areas, the level of knowledge and the learning style and accordingly recommend the best matching helpers for collaboration. A previous research outlined the development and the implementation processes of a MultiAgent System for Collaborative E-learning (MASCE) which is designed to be used to assist