Foundations of Model-Lite Planning
Supported by ONR grant N00014-09-1-0017
Dr. Behzad Kamgar-Parsi
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
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.
Relevant publications (containing preliminary work)
Model-lite Planning for the Web Age Masses: The Challenges of
Planning with Incomplete and Evolving Domain Theories
AAAI 2007. (Slides and
A sneaky reprise at Festivus@ICAPS 2007
Model-Lite Case-Based Planning
Hankz Hankui Zhuo, Subbarao Kambhampati, Tuan Nguyen.
Refining Incomplete Domain Models through Plan Traces.
Hankz Hankui Zhuo, Tuan Nguyen and Subbarao Kambhampati.
Action Model Acquisition from Noisy Plan Traces.
Hankz Hankui Zhuo, Subbarao Kambhampati.
Planning for Human-Robot Teaming in Open Worlds.
Kartik Talamadupula, J. Benton, Subbarao Kambhampati,
Paul Schermerhorn, and Matthias Scheutz.
ACM Transactions on Intelligent Systems and Technology.
(Special Issue on Applications of Automated Planning). Vol
1. No. 2. 2010.
Generating diverse plans to handle unknown and partially known user preferences
Tuan Nguyen, Minh Do, Alfonso Gerevini, Ivan Serina, Biplav Srivastava
and Subbara Kambhampati.
Artificial Intelligence Journal. Vol 190. Pages 1-31. Oct 2012.
Integrating a closed-world planner with an open-world robot: A case
Kartik Talamadupula, J. Benton, Paul Schermerhorn, Matthias Scheutz
and Subbarao Kambhampati
Assessing and Generating Robust Plans with Partial Domain Models
Tuan A. Nguyen and Subbarao Kambhampati and Minh B. Do.
ICAPS 2010 Workshop on Planning under Uncertainty. 2010.
Learning Probabilistic Hierarchical Task Networks to Capture User Preferences.
Nan Li, Subbarao Kambhampati & Sungwook Yoon. IJCAI 2009. (To appear)
Planning with Partial Preference Models.
Tuan Nguyen, Minh Do, Biplav Srivastava and Subbarao Kambhampati. IJCAI 2009. (To appear)
Domain Independent Approaches for Finding Diverse Plans
Biplav Srivastava, Subbarao Kambhampati, Tuan Nguyen, Minh Do, Alfonso
Gerevini, Ivan Serina.
IJCAI 2007. (Talk slides)
Towards Model-lite Planning: A Proposal For Learning & Planning with
Incomplete Domain Models
Sungwook Yoon and Subbarao Kambhampati
ICAPS 2007 Workshop on AI Planning and Learning.
Model-Lite Planning: Diverse Multi-option plans & Dynamic Objective Functions.
Daniel Bryce, William Cushing & Subbarao Kambhampati
ICAPS 2007 Workshop on Planning and Plan Execution for Real World
Hierarchical Strategy Learning with Hybrid Representations
Sungwook Yoon and Subbarao Kambhampati
AAAI 2007 Workshop on Acquiring Planning Knowledge via Demonstration. 2007.
Last modified: Wed Mar 9 20:40:02 MST 2011