FALCON: a fusion architecture for learning, cognition, and navigation
Ah‐Hwee Tan · 2005
This work presents a natural extension of self-organizing neural network architecture for learning cognitive codes across multi-modal patterns involving sensory input, actions, and rewards. The proposed cognitive model, called FALCON, enables an autonomous agent to adapt and function in a dynamic environment. Simulations based on a minefield navigation task indicate that the system is able to adapt amazingly well and learns rapidly through it's interaction with the environment in an online and incremental manner. The scalability and robustness of the system is further enhanced by an online code evaluation and pruning procedure, that maintains the number of cognitive codes at a manageable size without degradation of system performance.