Through the use of a multi-agent system composed of emotionally enhanced sets of agents, we investigate how emotion plays a role in inter-agent communication, cooperation, goal achievement and perception. The behaviors of each agent are derived from its perception of the environment, its goals, knowledge gained from other agents and based on its own current emotional state, which is predicated on fuzzy sets. The state of each interacting agent is determined by its level of frustration combined with its interaction with other agents. The set of actions an agent may perform including that of its own ability to sense and understand the environment is limited by its current level of emotional context. In this work we focus on the analysis of the interaction of agents and review the grouping effect that arises from the inter-agent communications combined with the intra-agent emotional status as they perform the task required by the environment. The environment is based on previous work performed on navigational map learning by context chaining and abstraction.