Data Mining for Decision Making in Multi-Agent Systems
K. Hani, K. Hoda, S. Sally · InTech eBooks · 2011
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 Multi-Agent 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 agentsthe 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 Multi-Agent System for Collaborative E-learning (MASCE) which is designed to be used to assist www.intechopen.com Multi-Agent Systems -Modeling, Interactions, Simulations and Case Studies 274the teaching and learning processes.This system considers the blended learning environment as a supplement to the face-to-face lecture.The goal is to incorporate the intelligence of the multi-agent system in a way that enables it to actively and intelligently support the educational processes, where multiple agents can interact to exchange information so that students may collaborate on how best to gain knowledge.In this chapter we are going to outline the application of different data mining techniques to discover important information previously unknown from the large database tables obtained from MASCE.The use of data mining facilitates the decision making process.As the world grows in complexity, overwhelming us with the data it generates, data mining becomes our only hope for explaining the patterns that underlie it.Intelligently analyzed data is a valuable resource.It can lead to new insights and, in commercial settings, to competitive advantages.Data mining is defined as the process of discovering patterns in data.The process must be automatic or (more usually) semiautomatic.The patterns discovered must be meaningful in that they lead to some advantage.The data is invariably present in substantial quantities.Useful patterns allow us to make nontrivial predictions on new data.In other words, they help to explain something about the data.First, we are going to apply Rough Sets techniques to the decision tables in order to obtain decision rules that can be used to classify new unseen cases.Second, Decision Tree algorithms such as ID3 will be applied to the same decision tables to obtain decision trees that can be used in classification of new objects.Third, a hybrid data mining algorithm combining rough sets and decision trees is applied and the results are compared with the previous two techniques to determine which is most suitable for decision making in this particular application of multi-agent systems. Multi-agent systems and their applicationsAgents and Multi-Agent Systems (MAS) have emerged as a powerful technology to cope with the increasing complexity of a variety of Information Technology scenarios.We are not going to explain the full details of the agents because these are covered in other chapters.We are only going to provide a basic overview. Multi-agent overviewThe most widely accepted definition for the "agent" term is that "an agent acts on behalf of someone else, after having been authorized".This definition can be applied to software agents, which are instantiated and act instead of a user or a software program that controls them.The difficulty in defining an agent arises from the fact that the various aspects of agency are weighted differently, with respect to the application domain at hand.Wooldridge & Jennings have succeeded in combining general agent features into the following generic abstract definition integrating all the characteristics into the notion of an agent: "An agent is an autonomous software entity that -functioning continuously -carries out a set of goal-oriented tasks on behalf of another entity, either human or software system.This software entity is able to perceive its environment through sensors and act upon it through effectors, and in doing so, employ some knowledge or representation of the user's preferences" (Wooldridge, 1999).www.intechopen.com