This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applications, lack the aspect of empathy, known to improve cooperation in human teams. Emphasizing both cognitive and emotional aspects of empathy, the introduced model navigates communication uncertainties and ambiguities, transforming these challenges into opportunities for learning and adaptation in dynamic environments. A significant feature of this model is its handling of imprecision through fuzzy logic, using fuzzy similarity measures in the decision process. The main objective of the presented research is to introduce a new model for improving cooperativeness in multi-agent systems with the use of cognitive empathy. Future research focus on implementing the model on physical platform and optimize the artificial empathy algorithms in the decision-making module.

In this paper, we present a novel method of communicating knowledge between units in a swarm, applying artificial empathy and imprecise cognition to improve cooperation and adaptation. This research focuses on building a swarming agent’s individual knowledge base, relying on knowledge imprecisely shared by another unit and one’s own experience. The fuzzy similarity is used to fill the missing parameters, a subjective subset of which describes each agent. The obtained knowledge is then implemented as a particular set of actions, distinctive for each swarming agent. The actions bring the swarming unit closer to the desired state, appointed with the use of uncertainty combined with imprecision. Each agent compares their future possibilities with other’s prospects and decides whether to help, continue with their own action, or learn new knowledge from others.