Multi-robot perimeter patrol in adversarial settings

Agmon, Noa; Kraus, Sarit; Kaminka, Gal A.

doi:10.1109/robot.2008.4543563

Cited by 182 publications

(210 citation statements)

References 8 publications

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“…The key point was that if a bandit were only activated finitely many times under a policy, the remaining rewards from that bandit were discounted to 0 by the assumption of Eq. (2).…”

Section: Assumptions C and C And Zeno's Hypothesismentioning

confidence: 99%

Multi-Armed Bandits Under General Depreciation and Commitment

Cowan

Katehakis

2014

Prob. Eng. Inf. Sci.

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Generally, the multi-armed has been studied under the setting that at each time step over an infinite horizon a controller chooses to activate a single process or bandit out of a finite collection of independent processes (statistical experiments, populations, etc.) for a single period, receiving a reward that is a function of the activated process, and in doing so advancing the chosen process. Classically, rewards are discounted by a constant factor β ∈ (0, 1) per round.In this paper, we present a solution to the problem, with potentially non-Markovian, uncountable state space reward processes, under a framework in which, first, the discount factors may be non-uniform and vary over time, and second, the periods of activation of each bandit may be not be fixed or uniform, subject instead to a possibly stochastic duration of activation before a change to a different bandit is allowed. The solution is based on generalized restart-in-state indices, and it utilizes a view of the problem not as "decisions over state space" but rather "decisions over time".

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“…The key point was that if a bandit were only activated finitely many times under a policy, the remaining rewards from that bandit were discounted to 0 by the assumption of Eq. (2).…”

Section: Assumptions C and C And Zeno's Hypothesismentioning

confidence: 99%

Multi-Armed Bandits Under General Depreciation and Commitment

Cowan

Katehakis

2014

Prob. Eng. Inf. Sci.

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“…As mentioned in the introduction, Stackelberg games have been widely applied to security domains, although most of this work has considered static targets (e.g., Korzhyk et al, 2010;Krause, Roper, & Golovin, 2011;Letchford & Vorobeychik, 2012 Agmon, Kraus, and Kaminka (2008) proposed algorithms for computing mixed strategies for setting up a perimeter patrol in adversarial settings with mobile robot patrollers. Similarly, Basilico, Gatti, and Amigoni (2009) computed randomized leader strategies for robotic patrolling in environments with arbitrary topologies.…”

Section: Related Workmentioning

confidence: 99%

Protecting Moving Targets with Multiple Mobile Resources

Fang¹,

Jiang²,

Tambe³

2013

jair

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In recent years, Stackelberg Security Games have been successfully applied to solve resource allocation and scheduling problems in several security domains. However, previous work has mostly assumed that the targets are stationary relative to the defender and the attacker, leading to discrete game models with finite numbers of pure strategies. This paper in contrast focuses on protecting mobile targets that leads to a continuous set of strategies for the players. The problem is motivated by several real-world domains including protecting ferries with escort boats and protecting refugee supply lines. Our contributions include: (i) A new game model for multiple mobile defender resources and moving targets with a discretized strategy space for the defender and a continuous strategy space for the attacker.(ii) An efficient linear-programming-based solution that uses a compact representation for the defender's mixed strategy, while accurately modeling the attacker's continuous strategy using a novel sub-interval analysis method. (iii) Discussion and analysis of multiple heuristic methods for equilibrium refinement to improve robustness of defender's mixed strategy. (iv) Discussion of approaches to sample actual defender schedules from the defender's mixed strategy. (iv) Detailed experimental analysis of our algorithms in the ferry protection domain.

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“…Robots are then distributed from their initial positions to their sub-regions, and finally, each robot patrols its sub-region in order to detect a possible intrusion of an adversary agent into that sub-region. Agmon et al (2008b) studied patrolling a cyclic boundary, in which the robots' goal is to maximize their rewards by detecting an adversary agent, which attempts to penetrate through a point on the boundary unknown to the robots. In their scenario, the full-knowledge adversary knows the location of the robots and the patrol strategy and needs a time interval of length t to accomplish the intrusion.…”

Section: Adversarial Repeated Coveragementioning

confidence: 99%

Multi-robot repeated area coverage

2013

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We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Clusterbased algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effects of the variables: Environment Representation, and the Robots' Visual Range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results can be used as a framework for choosing an appropriate combination of repeated coverage algorithm, environment representation, and the robots' visual range based on the particular scenario and the metric to be optimized.

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Multi-robot perimeter patrol in adversarial settings

Cited by 182 publications

References 8 publications

Multi-Armed Bandits Under General Depreciation and Commitment

Multi-Armed Bandits Under General Depreciation and Commitment

Protecting Moving Targets with Multiple Mobile Resources

Multi-robot repeated area coverage

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