The automated planning community has traditionally focused on the efficient synthesis of plans given a complete model of the domain and user preferences. In the past several years, this line of work met with significant successes, and the future course of the community seems to be set on efficient planning with even richer domain and preference models. While this line of research has its applications, there are also many domains, including, perhaps most prominently, military planning scenarios, where the first bottleneck is getting the domain and user preference model at any level of completeness. This incompleteness is caused both because of domain modeling burden, and also because of lack of full knowledge (e.g. commanders often do not know or are unable to specify full details of their own objectives).
To support planning in such scenarios, recently, the PI has argued for a model-lite planning technology aimed at handling incompletely specified domain and user preference models. This view was first presented as a senior member track paper at the National Conference on Artificial Intelligence in 2007 (1), and has since been fleshed out in several conference and workshop papers (c.f. (10; 40; 34; 18)).
Based on the promising preliminary results, in this research, the PI proposes to develop a comprehensive framework for model-lite planning under incomplete domain and user preference models. The proposed research tasks can be broadly split into three categories, aimed respectively at planning techniques to handle incompleteness in preference models, planning techniques to handle incompleteness in domain models, and learning techniques to refine both preference and domain models (the former through elicitation and the latter through execution and replanning).
Model-lite planning technology developed in this research is expected to significantly increase the reach and application of automated planning techniques. It will be especially useful in many military planning domains, where the modeling burden and dynamically changing and/or partially articulated planning objectives make traditional planning technology unsuitable. It will also be useful in web-service composition and workflow management scenarios.
The URL http://rakaposhi.eas.asu.edu/onr-model-lite contains
of the proposal as well as latest developments in the project.