TY - JOUR
T1 - Reachability analysis for uncertain SSPs
AU - Buffet, Olivier
PY - 2007/8
Y1 - 2007/8
N2 - Stochastic Shortest Path problems (SSPs) can be efficiently dealt with by the Real-Time Dynamic Programming algorithm (RTDP). Yet, RTDP requires that a goal state is always reachable. This article presents an algorithm checking for goal reachability, especially in the complex case of an uncertain SSP where only a possible interval is known for each transition probability. This gives an analysis method for determining if SSP algorithms such as RTDP are applicable, even if the exact model is not known. As this is a time-consuming algorithm, we also present a simple process that often speeds it up dramatically. Yet, the main improvement still needed is to turn to a symbolic analysis in order to avoid a complete state-space enumeration.
AB - Stochastic Shortest Path problems (SSPs) can be efficiently dealt with by the Real-Time Dynamic Programming algorithm (RTDP). Yet, RTDP requires that a goal state is always reachable. This article presents an algorithm checking for goal reachability, especially in the complex case of an uncertain SSP where only a possible interval is known for each transition probability. This gives an analysis method for determining if SSP algorithms such as RTDP are applicable, even if the exact model is not known. As this is a time-consuming algorithm, we also present a simple process that often speeds it up dramatically. Yet, the main improvement still needed is to turn to a symbolic analysis in order to avoid a complete state-space enumeration.
KW - Reachability analysis
KW - Stochastic shortest-path problems
KW - Uncertain model
UR - http://www.scopus.com/inward/record.url?scp=34548475808&partnerID=8YFLogxK
U2 - 10.1142/S0218213007003527
DO - 10.1142/S0218213007003527
M3 - Article
SN - 0218-2130
VL - 16
SP - 725
EP - 749
JO - International Journal on Artificial Intelligence Tools
JF - International Journal on Artificial Intelligence Tools
IS - 4
ER -