An Overview of Recent Application Trends at the AAMAS Conference: Security, Sustainability, and Safety

Jain, Manish; An, Bo; Tambe, Milind

doi:10.1609/aimag.v33i3.2420

Cited by 14 publications

(20 citation statements)

References 48 publications

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“…Given that OP T T SP ≥ |V T SP |, we have that such a tour has a cost not larger than OP T T SP − 1 + 2(1 − β)|V T SP | ≤ OP T T SP (3 − 2β). Therefore, the tour is a (3 − 2β)-approximation for TSP (1,2). Since TSP(1,2) is not approximable in polynomial time for any approximation ratio smaller than α, we have the constraint that 3 − 2β ≥ α, and therefore that β ≤ 3−α 2 .…”

Section: Lemmamentioning

confidence: 99%

Adversarial patrolling with spatially uncertain alarm signals

Basilico

Nittis

Gatti

2017

Artificial Intelligence

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When securing complex infrastructures or large environments, constant surveillance of every area is not affordable. To cope with this issue, a common countermeasure is the usage of cheap but wide-ranged sensors, able to detect suspicious events that occur in large areas, supporting patrollers to improve the effectiveness of their strategies. However, such sensors are commonly affected by uncertainty. In the present paper, we focus on spatially uncertain alarm signals. That is, the alarm system is able to detect an attack but it is uncertain on the exact position where the attack is taking place. This is common when the area to be secured is wide, such as in border patrolling and fair site surveillance. We propose, to the best of our knowledge, the first Patrolling Security Game where a Defender is supported by a spatially uncertain alarm system, which non-deterministically generates signals once a target is under attack. We show that finding the optimal strategy is FNP-hard even in tree graphs and APX-hard in arbitrary graphs. We provide two (exponential time) exact algorithms and two (polynomial time) approximation algorithms. Finally, we show that, without false positives and missed detections, the best patrolling strategy reduces to stay in a place, wait for a signal, and respond to it at best. This strategy is optimal even with non-negligible missed detection rates, which, unfortunately, affect every commercial alarm system. We evaluate our methods in simulation, assessing both quantitative and qualitative aspects.

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Section: Lemmamentioning

confidence: 99%

Adversarial patrolling with spatially uncertain alarm signals

Basilico

Nittis

Gatti

2017

Artificial Intelligence

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“…The problem of patrolling environments with autonomous mobile robots has attracted the attention of many researchers both within the Autonomous Robotics and the Multiagent Systems communities [2,14]. It can be considered as an instance of a general class of problems related to surveillance and security [19] and can be generalized from detecting intruders to detecting any kind of threats.…”

Section: Patrolling Environments To Prevent Intrusionsmentioning

confidence: 99%

“…Given this model, a game theoretical perspective is adopted [19]: the patroller (i.e., the autonomous mobile robot) and the intruder are considered to play a strategic game that develops along a discrete timespan. At each time step, one of the two players performs an action (for the patroller, moving from one location of the environment to another location; for the intruder, entering or not the environment) and the combination of their actions determines the final outcome of the game (e.g., the intruder has been detected or the intruder successfully entered the environment).…”

Section: Patrolling Environments To Prevent Intrusionsmentioning

confidence: 99%

Moving From ‘How to go There?’ to ‘Where to go?’: Towards Increased Autonomy of Mobile Robots

Amigoni

Basilico

2014

New Trends in Medical and Service Robots

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Autonomous mobile robots have seen a wide spread development in recent years, due to their possible applications (e.g., surveillance and search and rescue). Several techniques have been proposed for solving the path planning problem, in which a user specifies spatial targets and the robots autonomously decide how to go there. In contrast, the problem of where to go next, in which the targets themselves are autonomously decided by the robots, is largely unexplored and lacking an assessed theoretical basis. In this work, we make a step towards a framework for casting and addressing this problem. The framework includes the following dimensions: the amount of knowledge about the environment the robots have, the kind of that knowledge, the criteria used to evaluate the success of the decisions, the number of decision makers, and the possible adversarial nature of the settings. We focus on applications relative to exploration and patrolling.

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“…The area of applications of Agents can be structured in three areas [2], say security, sustainability and safety, pointing out real systems and algorithms at work in airports, fire brigades or armed forces. In Security, we find Game Theory for deploying resources to defend targets of terrorism (ARMOR, IRIS) and for scheduling assistants for (GUARDS, PROTECT).…”

Section: Solving Real Problensmentioning

confidence: 99%