Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics

Yazidi, Anis; Pinto-Orellana, Marco Antonio; Hammer, Hugo Lewi; Mirtaheri, Peyman

doi:10.1109/tcyb.2019.2958627

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Although other ergodic schemes such as L R−P were used in games (Viswanathan & Narendra, 1974) with limited information, they did not gain popularity at least when it comes to applications due to their inability to converge to Nash equilibrium. LA has found numerous applications in game theoretical applications such as sensor fusion without knowledge of the ground truth (Yazidi et al, 2022), for distributed power control in wireless networks and more particularly NOMA (Rauniyar et al, 2020), optimization of cooperative tasks (Zhang et al, 2020), for content placement in cooperative caching (Yang et al, 2020), congestion control in Internet of Things (Gheisari & Tahavori, 2019), reaching agreement in Ultimatum games utilizing a continuous space strategy rather than working within a discrete actions space (De Jong et al, 2008), QoS satisfaction in autonomous mobile edge computing (Apostolopoulos et al, 2018), opportunistic spectrum access (Cao & Cai, 2018) scheduling domestic shiftable loads in smart grids (Thapa et al, 2017), anti-jamming channel selection algorithm for interference mitigation (Jia et al, 2017), relay selection in vehicular adhoc networks (Tian et al, 2017), load balancing by invoking the feedback from a purely local agent (Schaerf et al, 1994) etc.…”

Section: Related Workmentioning

confidence: 99%

Adaptive learning with artificial barriers yielding Nash equilibria in general games

Hassan,

Oommen,

Yazidi

2023

The Knowledge Engineering Review

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Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barriers, a phenomenon which is often referred to as lock in probability leading to an exclusive choice of one action after convergence. Within the field of LA and reinforcement learning in general, there is a sacristy of theoretical works and applications of schemes with artificial barriers. In this paper, we devise a LA with artificial barriers for solving a general form of stochastic bimatrix game. Classical LA systems possess properties of absorbing barriers and they are a powerful tool in game theory and were shown to converge to game’s of Nash equilibrium under limited information. However, the stream of works in LA for solving game theoretical problems can merely solve the case where the Saddle Point of the game exists in a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point exists for a pure strategy. Furthermore, we provide experimental results that are in line with our theoretical findings.

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Section: Related Workmentioning

confidence: 99%

Adaptive learning with artificial barriers yielding Nash equilibria in general games

Hassan,

Oommen,

Yazidi

2023

The Knowledge Engineering Review

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“…LA have found numerous applications in domains which can be perceived as being game-theoretic. These include sensor fusion without the knowledge of the ground truth (Yazidi et al 2020), distributed power control in wireless networks and more particularly, NOMA (Rauniyar et al 2020), optimization of cooperative tasks (Zhang, Wang, and Gao 2020), content placement in cooperative caching (Yang et al 2020), congestion control in the Internet of Things (Gheisari and Tahavori 2019), QoS satisfaction in autonomous mobile edge computing (Apostolopoulos, Tsiropoulou, and Papavassiliou 2018), opportunistic spectrum access (Cao and Cai 2018), scheduling domestic shiftable loads in smart grids (Thapa et al 2017), antijamming channel selection algorithm for interference mitigation (Jia et al 2017), and relay selection in vehicular adhoc networks (Tian et al 2017) etc.…”

Section: La In Game Theorymentioning

confidence: 99%

Learning Automata with Artificial Reflecting Barriers in Games with Limited Information

Hassan

Oommen

Yazidi

2022

FLAIRS

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This paper deals with the problem of solving stochastic games (which have numerous business and economic applications), using the interesting tools of Learning Automata (LA), the precursors to Reinforcement Learning (RL). Classical LA systems that possess properties of absorbing barriers, have been used as powerful tools in game theory to devise solutions that converge to the game's Nash equilibrium under limited information(Sastry, Phansalkar, and Thathachar 1994). Games with limited information are intrinsically hard because the player does not know the actions chosen of other players, neither their outcomes. The player might not be even aware of the fact that he/she is playing against an opponent. With the state-of-the-art, the numerous works in LA applicable for solving game theoretical problems, can merely solve the case where the game possesses a Saddle Point in a pure strategy. They are unable to reach mixed Nash equilibria when a Saddle Point is non-existent in pure strategies. Additionally, within the field of LA and RL in general, the theoretical and applied schemes of LA with artificial barriers are scarce, even though incorporating artificial barriers in LA has served as a powerful and yet under-explored concept, since its inception in the 1980’s. More recently, the phenomenon of introducing artificial non-absorbing barriers was pioneered, and this renders the LA schemes to be resilient to being trapped in absorbing barriers. In this paper, we devise a LA with artificial barriers for solving a general form of stochastic bimatrix games. The problem’s complexity has been augmented with the scenario that we consider games in which there is no Saddle Point. By resorting to the above-mentioned powerful concept of artificial reflecting barriers, we propose a LA that converges to an optimal mixed Nash equilibrium even though there may be no Saddle Point when a pure strategy is invoked.

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“…Although other ergodic schemes such as L R−P were used in games [18] with limited information, they did not gain popularity at least when it comes to applications due to their inability to converge to Nash equilibrium. LA has found numerous applications in game theoretical applications such as sensor fusion without knowledge of the ground truth [19], for distributed power control in wireless networks and more particurly NOMA [20], optimization of cooperative tasks [21], for content placement in cooperative caching [22], congestion control in Internet of Things [23], QoS satisfaction in autonomous mobile edge computing [24], opportunistic spectrum access [25] scheduling domestic shiftable loads in smart grids [26], anti-jamming channel selection algorithm for interference mitigation [27], relay selection in vehicular ad-hoc networks [28] etc. Objective and Contribution of this paper: In this paper, we propose an algorithm addressing bimatrix games which is a more general version of the zero-sum game treated in [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games

Hassan¹,

Yazidi²,

Oommen³

2022

Preprint

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Artificial barriers in Learning Automata (LA) is a powerful and yet under-explored concept although it was first proposed in the 1980s [1]. Introducing artificial non-absorbing barriers makes the LA schemes resilient to being trapped in absorbing barriers, a phenomenon which is often referred to as lock in probability leading to an exclusive choice of one action after convergence. Within the field of LA and reinforcement learning in general, there is a sacristy of theoretical works and applications of schemes with artificial barriers. In this paper, we devise a LA with artificial barriers for solving a general form of stochastic bimatrix game. Classical LA systems possess properties of absorbing barriers and they are a powerful tool in game theory and were shown to converge to game's of Nash equilibrium under limited information [2]. However, the stream of works in LA for solving game theoretical problems can merely solve the case where the Saddle Point of the game exists in a pure strategy and fail to reach mixed Nash equilibrium when no Saddle Point exists for a pure strategy. In this paper, by resorting to the powerful concept of artificial barriers, we suggest a LA that converges to an optimal mixed Nash equilibrium even though there may be no Saddle Point when a pure strategy is invoked. Our deployed scheme is of Linear Reward-Inaction (LR−I ) flavor which is originally an absorbing LA scheme, however, we render it non-absorbing by introducing artificial barriers in an elegant and natural manner, in the sense that that the well-known legacy LR−I scheme can be seen as an instance of our proposed algorithm for a particular choice of the barrier. Furthermore, we present an S Learning version of our LA with absorbing barriers that is able to handle S-Learning environment in which the feedback is continuous and not binary as in the case of the LR−I .Reward-ǫPenalty (LR−ǫP ) scheme proposed by Lakshmivarahan and Narendra [3] almost four decades ago, is the only LA scheme that was shown to converge to the optimal mixed Nash equilibrium when no Saddle Point exists in pure strategy, and the proofs were limited to only zero-sum games. In this paper, we tackle a general game as opposed to the particular case of zero-sum games proposed by Lakshmivarahan and Narendra in [3], and provide a novel scheme and proofs characterizing its behavior. Furthermore, we provide experimental results that are in line with our theoretical findings.

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Solving Sensor Identification Problem Without Knowledge of the Ground Truth Using Replicator Dynamics

Cited by 5 publications

References 35 publications

Adaptive learning with artificial barriers yielding Nash equilibria in general games

Adaptive learning with artificial barriers yielding Nash equilibria in general games

Learning Automata with Artificial Reflecting Barriers in Games with Limited Information

Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games

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