Deterministic versus Probabilistic Methods for Searching for an Evasive Target. / Bernardini, Sara; Fox, Maria; Long, Derek; Piacentini, Chiara.

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). 2017. p. 3709-3715.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Deterministic versus Probabilistic Methods for Searching for an Evasive Target. / Bernardini, Sara; Fox, Maria; Long, Derek; Piacentini, Chiara.

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). 2017. p. 3709-3715.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Bernardini, S, Fox, M, Long, D & Piacentini, C 2017, Deterministic versus Probabilistic Methods for Searching for an Evasive Target. in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). pp. 3709-3715. <https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14889>

APA

Bernardini, S., Fox, M., Long, D., & Piacentini, C. (2017). Deterministic versus Probabilistic Methods for Searching for an Evasive Target. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) (pp. 3709-3715) https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14889

Vancouver

Bernardini S, Fox M, Long D, Piacentini C. Deterministic versus Probabilistic Methods for Searching for an Evasive Target. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). 2017. p. 3709-3715

Author

Bernardini, Sara ; Fox, Maria ; Long, Derek ; Piacentini, Chiara. / Deterministic versus Probabilistic Methods for Searching for an Evasive Target. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). 2017. pp. 3709-3715

BibTeX

@inproceedings{a3ea8f4569ac4d2faf8b09f89ca838b6,
title = "Deterministic versus Probabilistic Methods for Searching for an Evasive Target",
abstract = "Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods.",
author = "Sara Bernardini and Maria Fox and Derek Long and Chiara Piacentini",
year = "2017",
month = feb,
day = "12",
language = "English",
pages = "3709--3715",
booktitle = "Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)",

}

RIS

TY - GEN

T1 - Deterministic versus Probabilistic Methods for Searching for an Evasive Target

AU - Bernardini, Sara

AU - Fox, Maria

AU - Long, Derek

AU - Piacentini, Chiara

PY - 2017/2/12

Y1 - 2017/2/12

N2 - Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods.

AB - Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i.e., a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods.

M3 - Conference contribution

SP - 3709

EP - 3715

BT - Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)

ER -