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MultiRobot Area Patrol under Frequency Constraints
"... This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for evaluation ..."
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Cited by 64 (15 self)
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This paper discusses the problem of generating patrol paths for a team of mobile robots inside a designated target area. Patrolling requires an area to be visited repeatedly by the robot(s) in order to monitor its current state. First, we present frequency optimization criteria used for evaluation of patrol algorithms. We then present a patrol algorithm that guarantees maximal uniform frequency, i.e., each point in the target area is covered at the same optimal frequency. This solution is based on finding a circular path that visits all points in the area, while taking into account terrain directionality and velocity constraints. Robots are positioned uniformly along this path, using a second algorithm. Moreover, the solution is guaranteed to be robust in the sense that uniform frequency of the patrol is achieved as long as at least one robot works properly.
A Realistic Model of FrequencyBased MultiRobot Polyline Patrolling
"... There is growing interest in multirobot frequencybased patrolling, in which a team of robots optimizes its frequency of point visits, for every point in a target work area. In particular, recent work on patrolling of open polygons (e.g., openended fences) has proposed a general cooperative patrol ..."
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Cited by 43 (9 self)
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There is growing interest in multirobot frequencybased patrolling, in which a team of robots optimizes its frequency of point visits, for every point in a target work area. In particular, recent work on patrolling of open polygons (e.g., openended fences) has proposed a general cooperative patrolling algorithm, in which robots move back and forth along the polygon, in an synchronized manner, such that their assigned areas of movement overlap. If the overlap factor is carefully chosen—based on the motion models of the robots—specific performance criteria are optimized. Unfortunately, previous work has presented analysis of motion models in which there are no errors in the movement of the robots, and no velocity changes. We go a step beyond existing work, and develop a realistic model of robot motion, that considers velocity uncertainties. We mathematically analyze the model and show how to use it to find optimal patrolling parameters, given known bounds of uncertainty on the motion. We then use the model to analyze the independentlyprogrammed patrolling movements of physical robots, in extensive experiments. We show that the model predicts the behavior of the robots much more accurately than previouslydescribed models.
Effective solutions for realworld Stackelberg games: When agents must deal with human uncertainties
 in Proc. of the 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS ’09). IFAAMAS
, 2009
"... How do we build multiagent algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human interaction, such as oligopolistic markets and security domains. In Stackelberg games, one player, the leader, commits to a str ..."
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Cited by 29 (17 self)
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How do we build multiagent algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human interaction, such as oligopolistic markets and security domains. In Stackelberg games, one player, the leader, commits to a strategy and the follower makes their decision with knowledge of the leader’s commitment. Existing algorithms for Stackelberg games efficiently find optimal solutions (leader strategy), but they critically assume that the follower plays optimally. Unfortunately, in realworld applications, agents face human followers (adversaries) who — because of their bounded rationality and limited observation of the leader strategy — may deviate from their expected optimal response. Not taking into account these likely deviations when dealing with human adversaries can cause an unacceptable degradation in the leader’s reward,
Computing TimeDependent Policies for Patrolling Games with Mobile Targets
"... We study how a mobile defender should patrol an area to protect multiple valuable targets from being attacked by an attacker. In contrast to existing approaches, which assume stationary targets, we allow the targets to move through the area according to an a priori known, deterministic movement sche ..."
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Cited by 22 (1 self)
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We study how a mobile defender should patrol an area to protect multiple valuable targets from being attacked by an attacker. In contrast to existing approaches, which assume stationary targets, we allow the targets to move through the area according to an a priori known, deterministic movement schedules. We represent the patrol area by a graph of arbitrary topology and do not put any restrictions on the movement schedules. We assume the attacker can observe the defender and has full knowledge of the strategy the defender employs. We construct a gametheoretic formulation and seek defender’s optimal randomized strategy in a Stackelberg equilibrium of the game. We formulate the computation of the strategy as a mathematical program whose solution corresponds to an optimal timedependent Markov policy for the defender. We also consider a simplified formulation allowing only stationary defender’s policies which are generally less effective but are computationally significantly cheaper to obtain. We provide experimental evaluation examining this tradeoff on a set of test problems covering various topologies of the patrol area and various movement schedules of the targets.
Algorithms and Complexity Results for PursuitEvasion Problems
, 2009
"... We study pursuitevasion problems where a number of pursuers have to clear a given graph. We study when polynomialtime algorithms exist to determine how many pursuers are needed to clear a given graph and how a given number of pursuers should move on the graph to clear it with either a minimum sum ..."
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Cited by 19 (2 self)
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We study pursuitevasion problems where a number of pursuers have to clear a given graph. We study when polynomialtime algorithms exist to determine how many pursuers are needed to clear a given graph and how a given number of pursuers should move on the graph to clear it with either a minimum sum of their travel distances or minimum taskcompletion time. We generalize prior work to both unitwidth arbitrarylength and unitlength arbitrarywidth graphs and derive both algorithms and complexity results for a variety of graph topologies. In this context, we describe a polynomialtime algorithm, called CLEARTHETREE, that is much shorter and algorithmically simpler than the stateoftheart algorithm for the minimum pursuer problem on trees. Our theoretical research lays a firm theoretical foundation for pursuit evasion on graphs and informs practitioners about which problems are easy and which ones are hard.
Adversarial Uncertainty in MultiRobot Patrol
"... We study the problem of multirobot perimeter patrol in adversarial environments, under uncertainty of adversarial behavior. The robots patrol around a closed area using a nondeterministic patrol algorithm. The adversary’s choice of penetration point depends on the knowledge it obtained on the patro ..."
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Cited by 19 (1 self)
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We study the problem of multirobot perimeter patrol in adversarial environments, under uncertainty of adversarial behavior. The robots patrol around a closed area using a nondeterministic patrol algorithm. The adversary’s choice of penetration point depends on the knowledge it obtained on the patrolling algorithm and its weakness points. Previous work investigated full knowledge and zero knowledge adversaries, and the impact of their knowledge on the optimal algorithm for the robots. However, realistically the knowledge obtained by the adversary is neither zero nor full, and therefore it will have uncertainty in its choice of penetration points. This paper considers these cases, and offers several approaches to bounding the level of uncertainty of the adversary, and its influence on the optimal patrol algorithm. We provide theoretical results that justify these approaches, and empirical results that show the performance of the derived algorithms used by simulated robots working against humans playing the role of the adversary is several different settings. 1
A Robust Approach to Addressing Human Adversaries in Security Games
"... Abstract. Gametheoretic approaches have been proposed for addressing the complex problem of assigning limited security resources to protect a critical set of targets. However, many of the standard assumptions fail to address human adversaries who security forces will likely face. To address this ch ..."
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Cited by 12 (6 self)
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Abstract. Gametheoretic approaches have been proposed for addressing the complex problem of assigning limited security resources to protect a critical set of targets. However, many of the standard assumptions fail to address human adversaries who security forces will likely face. To address this challenge, previous research has attempted to integrate models of human decisionmaking into the gametheoretic algorithms for security settings. The current leading approach, based on experimental evaluation, is derived from a wellfounded solution concept known as quantal response and is known as BRQR. One critical difficulty with opponent modeling in general is that, in security domains, information about potential adversaries is often sparse or noisy and furthermore, the games themselves are highly complex and large in scale. Thus, we chose to examine a completely new approach to addressing human adversaries that avoids the complex task of modeling human decisionmaking. We leverage and modify robust optimization techniques to create a new type of optimization where the defender’s loss for a potential deviation by the attacker is bounded by the distance of that deviation from the expectedvaluemaximizing strategy. To demonstrate the advantages of our approach, we introduce a systematic way to generate meaningful reward structures and compare our approach with BRQR in the most comprehensive investigation to date involving 104 security settings where previous work has tested only up to 10 security settings. Our experimental analysis reveals our approach performing as well as or outperforming BRQR in over 90 % of the security settings tested and we demonstrate significant runtime benefits. These results are in favor of utilizing an approach based on robust optimization in these complex domains to avoid the difficulties of opponent modeling. 1
Path Disruption Games
"... We propose Path Disruption Games (PDGs), which consider collaboration between agents attempting stop an adversary from travelling from a source node to a target node in a graph. PDGs can model physical or network security domains. The coalition attempts to stop the adversary by placing checkpoints i ..."
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Cited by 8 (3 self)
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We propose Path Disruption Games (PDGs), which consider collaboration between agents attempting stop an adversary from travelling from a source node to a target node in a graph. PDGs can model physical or network security domains. The coalition attempts to stop the adversary by placing checkpoints in intermediate nodes in the graph, to make sure the adversary cannot travel through them. Thus, the coalition wins if it controls a node subset whose removal from the graph disconnects the source and target. We analyze this domain from a cooperative game theoretic perspective, and consider how the gains can be distributed between the agents controlling the vertices. We also consider power indices, which express the influence of each checkpoint location on the outcome of the game, and can be used to identify the most critical locations where checkpoints should be placed. We consider both general graphs and the restricted case of trees, and consider both a model with no cost for placing a checkpoint and a model with where each vertex has its own cost for placing a checkpoint.
Solving the Continuous Time Multiagent Patrol Problem
"... Abstract — This paper compares two algorithms to solve a multiagent patrol problem with uncertain durations. The first algorithm is reactive and allows adaptive and robust behavior, while the second one uses planning to maximize longterm information retrieval. Experiments suggest that on the conside ..."
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Cited by 6 (0 self)
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Abstract — This paper compares two algorithms to solve a multiagent patrol problem with uncertain durations. The first algorithm is reactive and allows adaptive and robust behavior, while the second one uses planning to maximize longterm information retrieval. Experiments suggest that on the considered instances, using a reactive and local coordination algorithm performs almost as well as planning for longterm, while using much less computation time. Keywords: Multiagent, UAV, Online, Patrol. I.
On Events in MultiRobot Patrol in Adversarial Environments
"... The problem of multirobot patrol in adversarial environments has been gaining considerable interest during the recent years.In this problem, a team of mobile robots is required to repeatedly visit some target area in order to detect penetrations that are controlled by an adversary. Little has been ..."
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The problem of multirobot patrol in adversarial environments has been gaining considerable interest during the recent years.In this problem, a team of mobile robots is required to repeatedly visit some target area in order to detect penetrations that are controlled by an adversary. Little has been written so far on the nature of the event of penetration, and it is commonly assumed that the goal of the robots is to detect the penetration at any time during its occurrence.In this paper we offer a new definition of an event, with correlation to a utility function such that the detection of the event by the robots in different stages of its occurrence grants the robots a different reward. The goal of the robots is, then, to maximize their utility from detecting the event. We provide three different models of events, for which we