Interested in planning under uncertainty in dynamic environments, for autonomous systems, our approach aims at combining Classical AI planning and Game theory. Roughly, our idea is to make game-theoretic approaches more efficient by taking advantage of the capabilities of classical AI planning to handle sets of states instead of single states. More technically, our planner is based on the use of Petri nets with labelled colored tokens. When a token marks a place, it carries on its label a particular binding for the predicate's variables. From the initial marking, tokens are propagated through the Petri net so that we can build the graph of the tokens'possible motions.
Tokens belong to different classes.
A transition can be triggered iff all of its preconditions are marked by same color tokens. In all cases where full splitting is not being used, it is necessary to compute mutex relations. For the backward search, we used some code kindly provided by Subbarao Kambhampati. The current version is implemented in Lisp. The current version is implemented in Lisp, with some code kindly provided by Subbarao Kambhampati for the backward search.