Revisiting Goal Probability Analysis in Probabilistic Planning

Abstract

Maximizing goal probability is an important objective in probabilistic planning, yet algorithms for its optimal solution are severely underexplored. There is scant evidence of what the empirical state of the art actually is. Focusing on heuristic search, we close this gap with a comprehensive empirical analysis of known and adapted algorithms. We explore both, the general case where there may be 0-reward cycles, and the practically relevant special case of acyclic planning, like planning with a limited action-cost budget. We consider three different algorithmic objectives. We design suitable termination criteria, search algorithm variants, dead-end pruning methods using classical planning heuristics, and node selection strategies. Our evaluation on more than 1000 benchmark instances from the IPPC, resource-constrained planning, and simulated penetration testing reveals the behavior of heuristic search, and exhibits several improvements to the state of the art.

Occupation Measure Heuristics for Probabilistic Planning

“…All rights reserved. and Singh 1995, Bonet and Geffner 2003), LAO* (Hansen and Zilberstein 2001), FRET (Kolobov et al 2011, Steinmetz, Hoffmann, andBuffet 2016), and i-dual (Trevizan et al 2016). However, in contrast to the situation in deterministic planning, the success of these algorithms has been limited by the lack of effective domain-independent heuristics dedicated to the probabilistic planning setting.…”