NEGOTIATION AGENT BEHAVIORS BASED ON REINFORCEMENT LEARNING APPROACHES AND FUZZY ARTMAP NEURAL NETWORKS
Amine Chohra, Arash Bahrammirzaee, Kurosh Madani · International Journal of Computing · 2014
Behaviors, in which the characters conciliatory, neutral, or aggressive define a ‘psychological’ aspect of human personality, play an important role for negotiation agent. Elsewhere, learning in negotiation is fundamental for understanding human behaviors and developing new concepts. In this paper, a negotiation strategy essentially based on such human personality behaviors is suggested for SISINE project which aims to develop innovative teaching methodology of negotiation skills. For this purpose, first, reinforcement learning (Q-learning and Sarsa-Learning) approaches are developed, analyzed, and compared in order to acquire the strategy negotiation behaviors. Second, a Fuzzy ArtMap Neural Network (FAMNN) is developed to acquire this strategy. Third, a Field Programmable Gate Array (FPGA) architecture is suggested for the FAMNN integration. The suggested strategy displays the ability to provide agents, through a basic buying strategy, with a first intelligence level in a social and cognitive system for learning negotiation strategies (human-agent and agent-agent).