Learning from Human Demonstrations for Real-Time Case-Based Planning
Santiago Onta · 2009
One of the main bottlenecks in deploying casebased planning systems is authoring the case-base of plans. In this paper we will present a collection of algorithms that can be used to automatically learn plans from human demonstrations. Our algorithms are based on the basic idea of a plan dependency graph, which is a graph that captures the dependencies among actions in a plan. Such algorithms are implemented in a system called Darmok 2 (D2), a case-based planning system capable of general game playing with a focus on real-time strategy (RTS) games. We evaluate D2 with a collection of three different games with promising results.