Neurosolver learning to solve Towers of Hanoi puzzles

Neurosolver is a neuromorphic planner and a general problem solving (GPS) system. To acquire its problem solving capability, Neurosolver uses a structure similar to the columnar organization of the cortex of the brain and a notion of place cells. The fundamental idea behind Neurosolver is to model world using a state space paradigm, and then use the model to solve problems presented as a pair of two states of the world: the current state and the desired (i.e., goal) state. Alternatively, the current state may be known (e.g., through the use of sensors), so the problem is fully expressed by stating just the goal state. Mechanically, Neurosolver works as a memory recollection system in which training samples are given as sequences of states of the subject system. Neurosolver generates a collection of interconnected nodes (inspired by cortical columns), each of which represents a single point in the problem state space, with the connections representing state transitions. A connection map between states is generated during training, and using this learned memory information, Neurosolver is able to construct a path from its current state, to the goal state for each such pair for which a transitions is possible at all. In this paper we show that Neurosolver is capable of acquiring from scratch the complete knowledge necessary to solve any puzzle for a given Towers of Hanoi configuration.