Group Cohesion in Multi-Agent Scenarios as an Emergent Behavior

The integration of artificial intelligence into multi-agent systems (MAS) has garnered an ever increasing attention among AI researchers. Of special interest are agents that exhibit social behavioral patterns, such as coordination, cooperation and conflict resolution [1]. Thereby, researchers have relied on classical approaches [9; 18; 20]. In recent publications, researchers used reinforcement learning to train their agents in multi-agent environments [4; 13; 28]. This subfield of machine learning allows agents to deduce optimal behavior solely from the problem formulation using a reward function that gives (delayed) feedback on actions. Social behavior, such as cooperation, eventually emerges as agents optimize their behavior to reach a predefined goal. Using AI frameworks that explicitly integrate psychological insights into human behavior might seem as a viable alternative when designing social multi-agent systems. In fact, over the course of the last four decades, cognitive scientists have developed so-called cognitive architectures to provide unified models of cognition and serve as frameworks for introducing human-like behavioral patterns into AI agents [14; 16]. Some cognitive architectures, like Soar [17; 23] and Icarus [15] are purely symbolic: They emulate aspects of planing and reasoning through the vehicle of production rules.