We desribe a variation of the planning domain definition language, PDDL, that permits the
modeling of probabilistic planning problems with rewards. This language, PPDDL1. 0, was …

Abstract The 2004 International Planning Competition, IPC-4, included a probabilistic
planning track for the first time. We describe the new domain specification language we …

Many traditional solution approaches to relationally specified decision-theoretic planning
problems (eg, those stated in the probabilistic planning domain description language, or …

The notion of plan quality in automated planning is a practically very important issue. In
many real-world planning domains, we have to address problems with a large set of …

We describe the version of the GPT planner used in the probabilistic track of the 4th
International Planning Competition (IPC-4). This version, called mGPT, solves Markov …

This manual describes the syntax of PDDL, the Planning Domain De nition Language, the
problem-speci cation language for the AIPS-98 planning competition. The language has …

State trajectory and preference constraints are the two language features introduced in
PDDL3 (Gerevini & Long 2005) for describing benchmarks of the 5th international planning …

The Graphplan planner has enjoyed considerable success as a planning algorithm for
classical STRIPS domains. In this paper we explore the extent to which its representation …

A decision process in which rewards depend on history rather than merely on the current
state is called a decision process with non-Markovian rewards (NMRDP). In decision …

Mips is a planning system that applies binary decision diagrams (BDDs) to compactly
represent world states in a planning problem and efficiently explore the underlying state …