Transfer Learning of Hierarchical Task-Network Planning Methods in a Real-Time Strategy Game
We describe a new integrated and automated AI planning and learning architecture, called Learn2SHOP. Learn2SHOP departs significantly from the previous works on AI planning and learning in that its modular architecture integrates Hierarchical Task Network (HTN) planning, concept learning, and computer simulations. Using simulations during the planning and learning process enables the system to get information about the outcomes of the actions. We have implemented Learn2SHOP and tested it on a transfer-learning task. The objective of transfer learning is transferring knowledge and skills learned from a wide variety of previous situations to the current, and likely different, previously unencountered problems(s). The experiments with Learn2SHOP have demonstrated the advantages of integrating planning, learning, and simulation in a real-time strategy game engine.