Accession Number:

ADA461131

Title:

Acquiring Domain-Specific Planners by Example

Descriptive Note:

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

2003-01-01

Pagination or Media Count:

40.0

Abstract:

Intelligent problem solving requires the ability to select actions autonomously from a specific state to reach objectives. Planning algorithms provide approaches to look ahead and select a complete sequence of actions. Given a domain description consisting of preconditions and effects of the actions the planner can take, an initial state, and a goal, a planning program returns a sequence of actions to transform the initial state into a state in which the goal is satisfied. Classical planning research has addressed this problem in a domain-independent manner--the same algorithm generates a complete plan for any domain specification. This feature comes at a cost which domain-independent planners incur either in high search efforts or in tedious hand-coded domain knowledge. 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.

Subject Categories:

  • Information Science
  • Numerical Mathematics
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE