Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges
This work addresses the problem of designing effective rational ML agents for researchers and practitioners in AI, but it is incremental as it focuses on systematization rather than new methods.
The paper tackles the fragmented integration of machine learning into rational agent architectures by systematizing existing approaches using the Belief-Desire-Intention paradigm, identifying key research opportunities and open challenges.
Thanks to the remarkable human-like capabilities of machine learning (ML) models in perceptual and cognitive tasks, frameworks integrating ML within rational agent architectures are gaining traction. Yet, the landscape remains fragmented and incoherent, often focusing on embedding ML into generic agent containers while overlooking the expressive power of rational architectures--such as Belief-Desire-Intention (BDI) agents. This paper presents a fine-grained systematisation of existing approaches, using the BDI paradigm as a reference. Our analysis illustrates the fast-evolving literature on rational agents enhanced by ML, and identifies key research opportunities and open challenges for designing effective rational ML agents.