2023-12-02-bakshi23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Stealthy Terrain-Aware Multi-Agent Active Search	Poster	eE3fsO5Mi2	Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents’ location from the targets. This problem is applicable to reconnaissance tasks wherein the safety of the search agents can be compromised as the targets may be adversarial. Prior work usually focuses either on adversarial search, where the risk of revealing the agents’ location to the targets is ignored or evasion strategies where efficient search is ignored. We present the Stealthy Terrain-Aware Reconnaissance (STAR) algorithm, a multi-objective parallelized Thompson sampling-based algorithm that relies on a strong topographical prior to reason over changing visibility risk over the course of the search. The STAR algorithm outperforms existing state-of-the-art multi-agent active search methods on both rate of recovery of targets as well as minimising risk even when subject to noisy observations, communication failures and an unknown number of targets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bakshi23a	0	Stealthy Terrain-Aware Multi-Agent Active Search	1782	1796	1782-1796	1782	false	Bakshi, Nikhil Angad and Schneider, Jeff	given family Nikhil Angad Bakshi given family Jeff Schneider	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/bakshi23a/bakshi23a.pdf