Monroe Plan Corpus
The Monroe Plan Corpus is a collection of automatically generated plan sessions together
with the corresponding plan. These have been generated using an AI planner.
Given a
plan library for a domain, we stochastically choose a goal and
generate a start state. Using a modified version of the SHOP2 Planner,
we randomly generate a valid plan for the goal and start state.
Using this process, we have created an artificial corpus for the
Monroe domain - a disaster management domain.
The corpus contains contains 5000 plan sessions and 10 goal schemas
with an average of 9.6 actions per session.
This corpus was created as part of a project for goal schema and parameter recognition.
It may be freely downloaded and used, although we ask that you let us know if you
are using it so we can more persuasively convince our sponsors that it is useful.
Also, please cite the following paper when referencing the corpus:
- Nate Blaylock and James Allen.
Generating artificial corpora for plan recognition.
In Liliana Ardissono, Paul Brna, and Antonija Mitrovic, editors,
User Modeling 2005, number 3538 in Lecture Notes in Artificial Intelligence,
pages 179-188. Springer, Edinburgh, July 24-29 2005.
[pdf]
Documentation
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Work that Uses the Corpus
(Please send me a note if you know of a paper that should be included here.)
- Sindhu Raghavan and Raymond Mooney.
Bayesian Abductive Logic Programs.
In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10),
Atlanta, GA, July 2010.
- Dan Tecuci and Bruce Porter.
Memory Based Goal Schema Recognition.
In Proceedings of the 22nd Florida Artificial Intelligence Research Society Conference (FLAIRS-22), Sanibel Island, FL, 2009.
- Dan Tecuci and Bruce Porter.
An Episodic Based Approach to Complex Event Processing.
In AAAI Spring Symposium on Intelligent Event Processing, Stanford, CA, 2009.
- Dan Tecuci.
A Generic Memory Module for Events.
PhD thesis, University of Texas at Austin, Computer Sciences Department. August 2007.
- Nate Blaylock and James Allen.
Hierarchical instantiated goal recognition.
In AAAI Workshop on Modeling Others from Observations (MOO-2006),
Boston, July 2006.
[pdf]
- Nate Blaylock and James Allen.
Fast hierarchical goal schema recognition.
In Proceedings of the Twenty-First National Conference on
Artificial Intelligence (AAAI-06), Boston, MA, July 2006.
[pdf]
- Nathan J. Blaylock.
Towards Tractable Agent-based Dialogue.
PhD thesis, University of Rochester, Dept. of Computer Science,
August 2005.
[pdf]
- Nate Blaylock and James Allen.
Generating artificial corpora for plan recognition.
In Liliana Ardissono, Paul Brna, and Antonija Mitrovic, editors,
User Modeling 2005, number 3538 in Lecture Notes in Artificial Intelligence,
pages 179-188. Springer, Edinburgh, July 24-29 2005.
[pdf]
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