Study on sociotype communication in heterogeneous multi-agents environment
Puah, Yuan Jian
Date of Issue2014
School of Computer Engineering
Heterogeneous multi-agent environment involve a problem space which house more than 1 type of agents. Where multi-agent is concerned, communication has always been the key thought process. This is because different agents have different architectures and in order for them to collaborate within the same environment, there should be some form of communication involved. This paper will examine the communication process of 2 Neural Network intelligent agents, one is the Multilayer Perceptron (MLP) and the other is the Temporal Difference-Fusion Architecture for Learning and Cognition (FALCON) with the help of a grid-based path-finding environment, known as Mine Navigation. The experiment was broken into 2 groups, each with 1 FALCON and 1 MLP agent. The first group being the control group consists of the agents with no communication; while the other, the test group is made up of agents capable of communication. The results from the two groups were collected and compared to draw out the relationship between communication and the performance of these agents. The results show that with communication, FALCON agent is able to perform better under controlled scenario and the MLP agent is able to be trained faster. With the success of this experiment, future work may consider increasing the complexity of the problem space by introducing more dimensions, or alternating the agents of different artificial and computational intelligence.
Final Year Project (FYP)
Nanyang Technological University