Autonomous Vehicle-Target Assignment: A Game-Theoretical Formulation

Arslan, Gürdal; Marden, Jason R.; Shamma, Jeff S.

doi:10.1115/1.2766722

Cited by 339 publications

(275 citation statements)

References 21 publications

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“…Nonetheless, log-linear learning has received significant research attention [1,2,6,9,10,15,32]. These results range from analyzing convergence rates [6,25] to the necessity of the structural requirements [1].…”

Section: Introductionmentioning

confidence: 99%

Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation

Marden

Shamma

2010

2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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135

306

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Log-linear learning is a learning algorithm with equilibrium selection properties. Log-linear learning provides guarantees on the percentage of time that the joint action profile will be at a potential maximizer in potential games. The traditional analysis of log-linear learning has centered around explicitly computing the stationary distribution. This analysis relied on a highly structured setting: i) players' utility functions constitute a potential game, ii) players update their strategies one at a time, which we refer to as asynchrony, iii) at any stage, a player can select any action in the action set, which we refer to as completeness, and iv) each player is endowed with the ability to assess the utility he would have received for any alternative action provided that the actions of all other players remain fixed. Since the appeal of log-linear learning is not solely the explicit form of the stationary distribution, we seek to address to what degree one can relax the structural assumptions while maintaining that only potential function maximizers are the stochastically stable action profiles. In this paper, we introduce slight variants of log-linear learning to include both synchronous updates and incomplete action sets. In both settings, we prove that only potential function maximizers are stochastically stable. Furthermore, we introduce a payoff-based version of log-linear learning, in which players are only aware of the utility they received and the action that they played. Note that log-linear learning in its original form is not a payoff-based learning algorithm. In payoff-based log-linear learning, we also prove that only potential maximizers are stochastically stable. The key enabler for these results is to change the focus of the analysis away from deriving the explicit form of the stationary distribution of the learning process towards characterizing the stochastically stable states. The resulting analysis uses the theory of resistance trees for regular perturbed Markov decision processes, thereby allowing a relaxation of the aforementioned structural assumptions.

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Section: Introductionmentioning

confidence: 99%

Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation

Marden

Shamma

2010

2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

Self Cite

135

306

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“…However, our method differs from this literature, because we are able to directly specify agents' utility functions. 2 From this landscape, the work most similar to ours is [17], in which an autonomous vehicle-target assignment problem is addressed using a potential game formulation. In their model, vehicles (agents) operate individually to optimise a global utility.…”

Section: Related Workmentioning

confidence: 99%

“…They are characterised as those games that admit a potential function, which is a real-valued function on the joint strategy space whose gradient is the gradient of the constituents' private utility functions [21]. The class of finite potential games have long been used to model congestion problems on networks [37], and, recently, they have been used to analyse distributed methods of solving target assignment problems [17,38] and job scheduling [5]. Formally, a function P : S → R is a potential for Γ if:…”

Section: Potential Gamesmentioning

confidence: 99%

“…This is because in many such design problems, the private motivations of the agents can be constructed in such a way that they are aligned with the global system objectives (as in [15,16,17]). In particular, in this work we use such self-interested agents to construct a MAS-based control mechanism for the dynamic task allocation and scheduling problem at hand, because we believe that self-interested agents can provide the degree of robustness and flexibility needed in large distributed applications.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized Dynamic Task Allocation Using Overlapping Potential Games

Chapman

Micillo

Kota

et al. 2010

The Computer Journal

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This paper reports on a novel decentralised technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a stochastic game formulation of these problems in which tasks have varying hard deadlines and processing requirements. We then introduce a new technique for approximating this game using a series of static potential games, before detailing a decentralised method for solving the approximating games that uses the distributed stochastic algorithm. Finally, we discuss an implementation of our approach to a task allocation problem in the RoboCup Rescue disaster management simulator. The results show that our technique performs comparably to a centralised task scheduler (within 6% on average), and also, unlike its centralised counterpart, it is robust to restrictions on the agents' communication and observation ranges.

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“…In the recent past, considerable efforts have been devoted to the problem of how to cooperatively assign and schedule tasks that are defined over an extended geographical area (Alighanbari and How, 2008;Arslan et al, 2007;Beard et al, 2002;Moore and Passino, 2007;Smith and Bullo, 2009). In these papers, the main focus is in developing distributed algorithms that operate with knowledge about the task locations and with limited communication between robots.…”

Section: A Static and Dynamic Vehicle Routingmentioning

confidence: 99%

UAV Routing and Coordination in Stochastic, Dynamic Environments

Enright¹,

Pavone

Savla

2014

Handbook of Unmanned Aerial Vehicles

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Recent years have witnessed great advancements in the science and technology for unmanned aerial vehicles (UAVs), e.g., in terms of autonomy, sensing, and networking capabilities. This chapter surveys algorithms on task assignment and scheduling for one or multiple UAVs in a dynamic environment, in which targets arrive at random locations at random times, and remain active until one of the UAVs flies to the target's location and performs an on-site task. The objective is to minimize some measure of the targets' activity, e.g., the average amount of time during which a target remains active. The chapter focuses on a technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. The main advantage of this approach is its ability to provide analytical estimates of the performance of the UAV system on a given problem, thus providing insight into how performance is affected by design and environmental parameters, such as the number of UAVs and the target distribution. In addition, the approach provides provable guarantees on the system's performance with respect to an ideal optimum. To illustrate this approach, a variety of scenarios are considered, ranging from the simplest case where one UAV moves along continuous paths and has unlimited sensing capabilities, to the case where the motion of the UAV is subject to curvature constraints, and finally to the case where the UAV has a finite sensor footprint. Finally, the problem of cooperative routing algorithms for multiple UAVs is considered, within the same queueing-theoretical framework, and with a focus on control decentralization.

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Autonomous Vehicle-Target Assignment: A Game-Theoretical Formulation

Cited by 339 publications

References 21 publications

Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation

Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation

Decentralized Dynamic Task Allocation Using Overlapping Potential Games

UAV Routing and Coordination in Stochastic, Dynamic Environments

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