Advances in Machine Learning & Artificial Intelligence(AMLAI)

Capitol Technology University, Laurel, Maryland, USA

Publications

• Research Article   
  PlanningEFEMix: Hybrid Active Inference for Sequential Decision-Making under Uncertainty
  Author(s): Bhagyeshkumar Chokhawala* and Dr. Atif Farid Mohammad
  
  Sequential decision-making under uncertainty remains a central challenge in artificial intelligence and machine learning, especially in environments where agents must act under partial observability, noisy feedback, sparse preference signals, and shifting context. Reinforcement learning, probabilistic planning, and representation learning each provide useful mechanisms for action selection, but each approach also has limitations when deployed as a single decision paradigm. Model-free reinforcement learning can be data intensive and unstable under noise; POMDP-based planning offers principled belief management but depends on accurate transition and observation models; contrastive representation learning improves discrimination but does not by itself define a policy objective; and deterministic Active Inference provides a coherent mechanism for uncertainty reduction and goal satisfactio.. Read More»
  

  Abstract HTML PDF