Organizers
Michael L. Littman
and Håkan
L. S. Younes.
Students: John Asmuth,
Paul Batchis, David Weissman.
Documentation and Links
- NEW (Oct. 2007): MDPSim 2.1: The PPDDL Plan Evaluation Simulator.
- Talk slides from the results
presentation at ICAPS in Vancouver.
- Data Poster from ICAPS.
- The PPDDL evaluation simulator is now available in gzipped, tar format:
mdpsim-1.3.tar.gz.
- Domain generators used for the competition:
Blocksworld, Boxworld,
Fileworld.
-
Competition domains (observable versions)
- More
example domains
- Summary of runs by team/problem.
Columns: 1: ID; 2: group; 3: problem; 4: attempts; 5: fail; 6:
success; 7: time average (?); 8: value
- Competition log files
(compressed tar)
- The Winners!
- Online Proceedings.
-
IPC 2004 Probabilistic Planning Track: FAQ 1.01 (November 1, 2003)
-
PPDDL 1.0 guide
-
IPC 2004 Probabilistic Planning Track: FAQ 0.5 (September 13, 2003)
-
IPC 2004 Probabilistic Planning Track: FAQ 0.1 (May 7, 2003,
prepared for the
ICAPS-03 Workshop on the Competition: Impact, Organization,
Evaluation, Benchmarks).
Software
To install the plan evaluator (under Unix), save the
tar file
(for example: use wget http://www.cs.rutgers.edu/~mlittman/topics/ipc04-pt/mdpsim-1.3.tar.gz or save from browser). Uncompress (for
example: gunzip mdpsim-1.3.tar.gz).
Extract everything (for example: tar xvf mdpsim-1.3.tar). Look at the
file mdpsim/README for more instructions.
For background information about PDDL, see the
PDDL 2.1 Manual.
Participants
-
-
- Zhengzhu Feng
UMass (with Eric Hansen)
Symbolic heuristic search
-
- Olga Skvortsova
Dresden University of Technology (with Eldar Karabaev)
First-order value iteration in fluent calculus
- Pascal Poupart
University of Toronto
Greedy linear value-approximation
- Charles Gretton
Australian National University
Re-engineered version of NMRDPP
-
-
- Robert L Givan
Purdue (with Alan Fern, Sungwook Yoon)
Policy iteration with policy language bias (learn from sample problems)
-
-
-
-
-
- Blai Bonet
Universidad Simon Bolivar
GPT: value function; simulation; plan search
- Nilufer Onder
Michigan Technological University (with Li and Garrett Whelan)
POP-style planner (possibly goal oriented, no sensing only)
- Florent Teichteil-Konigsbuch
ONERA-DCSD (with Patrick Fabiani)
Explicit state enumeration and DBNs, value functions
This material is based upon work supported by the National Science
Foundation under Grant No. 0315909. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the author(s) and do not necessarily reflect the views of the National
Science Foundation.