A multiagent system using associate rule mining (ARM), a Collaborative filtering approach

Agent Oriented Programming (AOP) is a recent promising software paradigm that brings concepts from the theories of artificial intelligence into the mainstream realm of distributed systems, and yet it is rather difficult to find a successful application of agent oriented system (specifically) when large-scale systems are considered. When adopting an agent-oriented approach to solve a problem, there are a number of domain independent issues that must always be solved, such as how to model agent behavior to predict future action and how to allow agents to communicate rather than expecting developers to develop this core infrastructure themselves. In our paper, we address several problems that exist in a socialized e-learning environment and provide solutions to these problems through smart and collaborative agent behavior modeling which learn and adapt themselves through prior experiences, thereby assisting in successful implementation of this large scale e-learning system. In this paper, the author (s) proposes an implementation of a complete distributed e-learning system based on Collaborative filtering (CF) method. The system has intelligent collaborative filtering based tutoring system (ICFTS) capabilities that allow contents, presentation and navigation to be adapted according to the learner's requirements. In order to achieve that development, two concepts were put together: multi-agent systems and data mining techniques (specifically, the ARM algorithm). All the implementation code is developed using MATLAB GUI environment. To our best knowledge, very few literatures discusses a portion of e-learning environment using adaptive software agents, but none of the current literatures addresses a complete implementation of their learning system in detail. The goal of the paper is to implement one such multi-agent based e-learning system which learns from its prior user experiences on top of an agent-oriented middleware that provides the domain-independent infrastructure, allowing the developers to focus on building the key logic behind it. In this system, the agents follow an adaptive cognitive learning approach, where the agent learns through user behaviors via a collaborative filtering technique, or experiencing and then processing and remembering the information in an e-learning environment. The paper will utilize agent (a piece of code) based environment in our e-learning system using ARM [1][2]. The paper follows a learning approach based cognitive domain of Bloom's Taxonomy such as Analyze, Evaluate, Create, Apply, understand and remember.