Despite the attractive generality of domain independent planning techniques, their inefficiency has severely hampered the attempts to scale them to complex real world planning domains. One very promising solution to this involves enabling the planner to improve performance from experience by reusing previously generated plans to solve new planning problems. Developing effective plan reuse frameworks is thus currently a very active area of research in automated planning, machine learning and case based reasoning communities. Successful design of such planning systems requires a thorough understanding of the fundamental tradeoffs between storage, retrieval and modification in plan reuse. In the previous work, a domain independent planning framework called PRAIR which facilitates flexible modification of existing plans to solve new planning problems in the context of hierarchical least commitment planning has been developed. Experiments with PRAIR have demonstrated its potential to bring about order of magnitude improvements in planning performance by incrementally modifying existing plans in a variety of domains. PRAIR framework thus promises to be an ideal test bed for investigating the issues of utility of reuse. This research, a unified reuse framework is proposed based on PRAIR modification framework, an is used to study the utility tradeoffs involved in plan reuse This framework will also be used to study ways of integrating reuse with other speedup learning techniques such as abstraction and search control rules. The effectiveness of the resulting reuse framework will be demonstrated by experimenting with the classical planning domains and more complex real world applications such as process planning. Results from this research will help to design plan reuse frameworks with demonstrably favorable utility tradeoffs thereby facilitating scaling up of domain independent planning techniques to more complex domains. This research will thus have a significant impact on automated planning, machine learning, and case based reasoning communities. //

Agency
National Science Foundation (NSF)
Institute
Division of Information and Intelligent Systems (IIS)
Type
Standard Grant (Standard)
Application #
9210997
Program Officer
Larry H. Reeker
Project Start
Project End
Budget Start
1992-07-01
Budget End
1996-03-31
Support Year
Fiscal Year
1992
Total Cost
$90,000
Indirect Cost
Name
Arizona State University
Department
Type
DUNS #
City
Tempe
State
AZ
Country
United States
Zip Code
85281