A Genetic Algorithm-Oriented Model of Agent Persuasion for Multi-agent System Negotiation

In recent years, reaching agreements is an important problem in multi-agent systems (MAS), which require different types of dialogues between agents. Persuasion is one of them and it is traditionally based on first order logic. However, this type of technique is not adequate for solving complex problems. This situation requires developing new models for optimizing an agent persuasion process. This work presents a persuasion model for MAS based in genetic algorithms (GA) for reaching agreements in problem solving. The objective of this work is to optimize the negotiation between agents that solve complex problems. First, it was designed the persuasion model. Then, it was implemented and evaluated in experimental scenarios and it was compared its results against traditional models. The experimental results showed that a GA-oriented persuasion model optimizes the negotiation in MAS by improving execution time, which also eventually will optimize the processes carried out by MAS.