Leveraging probabilistic reasoning in deterministic planning for large-scale autonomous search-and-tracking

TY - CHAP

T1 - Leveraging probabilistic reasoning in deterministic planning for large-scale autonomous search-and-tracking

AU - Bernardini, Sara

AU - Fox, Maria

AU - Long, Derek

AU - Piacentini, Chiara

PY - 2016/3/30

Y1 - 2016/3/30

N2 - Search-And-Tracking (SaT) is the problem of searching for a mobile target and tracking it once it is found. Since SaT platforms face many sources of uncertainty and operational constraints, progress in the field has been restricted to simple and unrealistic scenarios. In this paper, we propose a new hybrid approach to SaT that allows us to successfully address largescale and complex SaT missions. The probabilistic structure of SaT is compiled into a deterministic planning model and Bayesian inference is directly incorporated in the planning mechanism. Thanks to this tight integration between automated planning and probabilistic reasoning, we are able to exploit the power of both approaches. Planning provides the tools to efficiently explore big search spaces, while Bayesian inference, by readily combining prior knowledge with observable data, allows the planner to make more informed and effective decisions. We offer experimental evidence of the potential of our approach.

AB - Search-And-Tracking (SaT) is the problem of searching for a mobile target and tracking it once it is found. Since SaT platforms face many sources of uncertainty and operational constraints, progress in the field has been restricted to simple and unrealistic scenarios. In this paper, we propose a new hybrid approach to SaT that allows us to successfully address largescale and complex SaT missions. The probabilistic structure of SaT is compiled into a deterministic planning model and Bayesian inference is directly incorporated in the planning mechanism. Thanks to this tight integration between automated planning and probabilistic reasoning, we are able to exploit the power of both approaches. Planning provides the tools to efficiently explore big search spaces, while Bayesian inference, by readily combining prior knowledge with observable data, allows the planner to make more informed and effective decisions. We offer experimental evidence of the potential of our approach.

UR - http://www.scopus.com/inward/record.url?scp=84989838603&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:84989838603

VL - 2016-January

SP - 47

EP - 55

BT - Proceedings International Conference on Automated Planning and Scheduling, ICAPS

PB - AAAI Press

T2 - 26th International Conference on Automated Planning and Scheduling, ICAPS 2016

Y2 - 12 June 2016 through 17 June 2016

ER -