Trust-Based Decision Making in a Self-Adaptive Agent Organization

Interaction between agents is one of the key factors in multiagent societies. Using interaction, agents communicate with each other and cooperatively execute complex tasks that are beyond the capability of a single agent. Cooperatively executing tasks may endanger the success of an agent if it attempts to cooperate with peers that are not proficient or reliable. Therefore, agents need to have an evaluation mechanism to select peers for cooperation. Trust is one of the measures commonly used to evaluate the effectiveness of agents in cooperative societies. Since all interactions are subject to uncertainty, the risk behavior of agents as a contextual factor needs to be taken into account in decision making. In this research, we propose the concept of adaptive risk and agent strategy along with an algorithm that helps agents make decisions in an self-adaptive society utilizing an agent's own experience and recommendation-based trust. Trust-based decision making increases the profit of the system along with lower task failure in comparison to a no-trust model in which agents do not utilize evaluation mechanisms for choosing their cooperation peers.