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

Abstract

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.

A Trust-Based Agent Learning Model for Service Composition in Mobile Cloud Computing Environments

“…Xie et al [33] designed the double excitation and deception detection based trust model. Ahmadi and Allan [34] proposed a trust-based decision making model for multi-agent societies. Sule and Li [35] and AbdAllah et al [36] presented the mechanisms of trust evaluation methods for the cloud-based applications.…”

Cooperation between Trust and Routing Mechanisms for Relay Node Selection in Hybrid MANET-DTN

“…For example, in sociology trust is represented as a relationship in nature [14,15], but in psychology it is trust in a meaning of a personal attribute view. When we apply trust to MANET or even to newer hybrid MANET-DTN we can classify trust into the following four categories [16]:…”