SOM-Based System for Sequence Chunking and Planning

Martin Takác, Alistair Knott, Mark Sagar

Abstract: In this paper we present a connectionist architecture called C-block combining several powerful and cognitively relevant features. It can learn sequential dependencies in incoming data and predict probability distributions over possible next inputs, notice repeatedly occurring sequences, automatically detect sequence boundaries (based on surprise in prediction) and represent sequences declaratively as chunks/plans for future execution or replay. It can associate plans with reward, and also with their effects on the system state. It also supports plan inference from an observed sequence of behaviours: it can recognize possible plans, along with their likely intended effects and expected reward, and can revise these inferences as the sequence unfolds. Finally, it implements goal-driven behaviour, by finding and executing a plan that most effectively reduces the difference between the current system state and the agent’s desired state (goal). C-block is based on modified self-organizing maps that allow fast learning, approximate queries and Bayesian inference.