Symbolic Active Inference, developed by Research Fellows Jean-Francois Cloutier, represents an innovative approach to combining symbolic reasoning with active inference principles. The framework aims to bridge the gap between traditional symbolic AI and the free energy principle by implementing active inference using symbolic representations and logical reasoning. This implementation allows for explicit modeling of beliefs, goals, and actions using symbolic structures while maintaining the core mathematical principles of active inference - namely the minimization of variational free energy and expected free energy.
The approach enables systems to perform goal-directed reasoning and planning through symbolic manipulation while grounding these processes in the formal theory of active inference. Key aspects include the representation of generative models using symbolic structures, belief updating through logical inference, and action selection based on expected free energy minimization. This synthesis provides several advantages: it makes active inference more interpretable through explicit symbolic representations, enables complex reasoning about abstract concepts and relations, and allows for more efficient computation compared to purely numerical implementations. The framework has been demonstrated through implementations in domains like robotic planning and symbolic problem-solving, showing how symbolic representations can be effectively integrated with active inference's information-theoretic principles. This work represents an important step in developing hybrid AI systems that combine the strengths of both symbolic and probabilistic approaches to intelligence.
All Open Source information on Symbolic Cognitive Robotics can be found at