We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTNMAKER takes as inpu...
Three key components of an autonomous intelligent system are planning, execution, and learning. This paper describes how the Soar architecture supports planning, execution, and le...
We describe proof planning: a technique for both describing the hierarchical structure of proofs and then using this structure to guide proof attempts. When such a proof attempt fa...
Artificial intelligence (AI) technology can have a dramatic impact on the quality of video games. AI planning techniques are useful in a wide range of game components, including m...
While much work on learning in planning focused on learning domain physics (i.e., action models), and search control knowledge, little attention has been paid towards learning use...
Nan Li, William Cushing, Subbarao Kambhampati, Sun...