Learning In Multi-Agent System :Self Motivation Aproach

Abstract

Learning in Multi-Agent Systems (MAS) is a fundamental field within artificial intelligence, aiming to design autonomous agents capable of adapting to dynamic environments. As these environments become more complex, agents require learning strategies that allow them not only to react but also to evolve and build their own behavioral models over time. Among the various learning paradigms, constructivist approaches inspired by Piagetian theories have gained attention. These approaches consider agents as entities capable of constructing their internal knowledge through experience, without relying on predefined behaviors. In parallel, intrinsic motivation the internal drive to explore and learn offers a promising mechanism to encourage agents to engage with their environment meaningfully, especially in the absence of external rewards. Integrating these two concepts can lead to the development of self-motivated agents capable of autonomous and progressive learning. While several studies have implemented constructivist learning or intrinsic motivation in MAS independently, few approaches combine them effectively. Moreover, existing models often suffer from slow convergence, limited scalability, or simplistic behavior hierarchies. There remains a need for architectures that integrate self-motivation with constructivist learning to enhance autonomy, exploration efficiency, and adaptability in agents operating in complex, unknown environments.