Probabilistic Graphplan (PGP/TGP) Home Page
School of Computer Science,
Carnegie Mellon University, Pittsburgh PA 15213-3891
People: Avrim Blum and John Langford
About PGP/TGP
PGraphplan and TGraphplan are Graphplan-based planners for
STRIPS-style domains that include probabilistic actions.
Given a start state, a time horizon, and a
set of goals, PGraphplan finds the contingent plan with
highest probability of success within the horizon. It
does this by performing a standard "top-down dynamic
programming" approach, but using the planning graph to
constrain the search space. TGraphplan finds potentially
sub-optimal plans, but in general runs much more quickly
than PGraphplan, and uses
a search much closer in spirit to the original Graphplan
algorithm. This web page contains pointers to the ECP
paper describing these planners, and to code and domains
for those who with to try them out.
Source code and domains:
The domain format should be clear from the examples, but feel free to
send us email if you are having trouble creating your own. Code for
TGraphplan should be here soon.
Algorithms and Complexity |
Computer Science Department | School of Computer Science
This page maintained by Avrim Blum (avrim@cs.cmu.edu).