Decision-making

In dynamic environments, an agent must be highly reactive and able to make decisions rapidly. It is desirable to have large library of behaviours that can be quickly matched to specific situations. The research question is how such libraries should be built. The partners have broad expertise in automated decision-making ranging from control-theoretic to symbolic methods. Two approaches will be investigated:

1. Symbolic learning using high-level languages that allow hybrid systems that combine reactive behaviours with deliberation through "any time" algorithms; and
2. Hierarchical reinforcement learning algorithms that decompose the learning problem into more manageable subtasks.

Research in decision making will be led by Maurice Pagnucco.