Computer Science Department
School of Computer Science, Carnegie Mellon University
Acquiring Domain-Specific Planners by Example
Elly Winner, Manuela Veloso
Previous approaches to efficient general-purpose planning have focused on reducing the search involved in an existing general-purpose planning algorithm. An interesting alternative is to use example plans in a particular domain to demonstrate how to solve problems in that domain and to use that information to solve new problems independently of a domain-independent planner. Others have used example plans for case-based planning, but the retrieval and adaptation mechanisms were still domain-independent and efficiency issues were still a concern.
In my thesis, I propose to introduce algorithms to extract complex, repeating processes, in the form of domain-specific planning programs, from example plans. I will investigate the application of these learned programs to modelling agent preferences and choices. I will also investigate how the programs can be used, extended, and repaired dynamically as an agent encounters new problems and acquires new experience. Finally, I will compare the template-based planning paradigm to existing general-purpose and domain-specific planning programs with a full evaluation on new and existing planning domains. I expect the core contribution of this thesis to be a new planning paradigm in which domain-specific planners are learned by example.