mina montazeri - Academia.edu (original) (raw)

Uploads

Papers by mina montazeri

Research paper thumbnail of Incentive Mechanism in the Sponsored Content Market with Network Effect

arXiv (Cornell University), Mar 24, 2023

Research paper thumbnail of Incentive Mechanism in the Sponsored Content Market With Network Effects

IEEE Transactions on Computational Social Systems

Research paper thumbnail of Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

Reliability Engineering & System Safety

This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and ... more This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its revenue while concurrently retaining its reliability by scheduling preventive maintenance. The maintenance scheduling provides some safety constraints which should be satisfied at all times. Satisfying the critical safety and reliability constraints while the generation units have an incomplete information of each others' bidding strategy is a challenging problem. Bi-level optimization and reinforcement learning are state of the art approaches for solving this type of problems. However, neither bi-level optimization nor reinforcement learning can handle the challenges of incomplete information and critical safety constraints. To tackle these challenges, we propose the safe deep deterministic policy gradient reinforcement learning algorithm which is based on a combination of reinforcement learning and a predicted safety filter. The case study demonstrates that the proposed approach can achieve a higher profit compared to other state of the art methods while concurrently satisfying the system safety constraints.

Research paper thumbnail of Distributed Mechanism Design in Continuous Space for Federated Learning over Vehicular Networks

IEEE Transactions on Vehicular Technology

Research paper thumbnail of Fault diagnosis of autonomous underwater vehicle using neural network

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of Optimal Mechanism Design in the Sponsored Content Service Market

IEEE Communications Letters, 2021

We adopt contract theory to design a mechanism for interaction between the content provider (CP) ... more We adopt contract theory to design a mechanism for interaction between the content provider (CP) and mobile users (MUs) with asymmetric information. To incentivize MUs to consume more content, a content scheme is proposed in which the CP provides some financial assistance to MUs. This scheme yields both the MUs and CP benefit due to gained utility and advertisement revenue in response to high data usage, respectively. We prove that a unique functional strategy exists to allocate the content demand and financial assistance based on MUs’ types. This strategy satisfies the incentive compatibility, individual rationality and maximizes the CP’s profit.

Research paper thumbnail of Deep Reinforcement Learning-Aided Bidding Strategies for Transactive Energy Market

Research paper thumbnail of Learning Pareto Optimal Solution of a Multi-Attribute Bilateral Negotiation Using Deep Reinforcement

Electronic Commerce Research and Applications

Research paper thumbnail of Incentive Mechanism in the Sponsored Content Market with Network Effect

arXiv (Cornell University), Mar 24, 2023

Research paper thumbnail of Incentive Mechanism in the Sponsored Content Market With Network Effects

IEEE Transactions on Computational Social Systems

Research paper thumbnail of Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

Reliability Engineering & System Safety

This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and ... more This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its revenue while concurrently retaining its reliability by scheduling preventive maintenance. The maintenance scheduling provides some safety constraints which should be satisfied at all times. Satisfying the critical safety and reliability constraints while the generation units have an incomplete information of each others' bidding strategy is a challenging problem. Bi-level optimization and reinforcement learning are state of the art approaches for solving this type of problems. However, neither bi-level optimization nor reinforcement learning can handle the challenges of incomplete information and critical safety constraints. To tackle these challenges, we propose the safe deep deterministic policy gradient reinforcement learning algorithm which is based on a combination of reinforcement learning and a predicted safety filter. The case study demonstrates that the proposed approach can achieve a higher profit compared to other state of the art methods while concurrently satisfying the system safety constraints.

Research paper thumbnail of Distributed Mechanism Design in Continuous Space for Federated Learning over Vehicular Networks

IEEE Transactions on Vehicular Technology

Research paper thumbnail of Fault diagnosis of autonomous underwater vehicle using neural network

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of Optimal Mechanism Design in the Sponsored Content Service Market

IEEE Communications Letters, 2021

We adopt contract theory to design a mechanism for interaction between the content provider (CP) ... more We adopt contract theory to design a mechanism for interaction between the content provider (CP) and mobile users (MUs) with asymmetric information. To incentivize MUs to consume more content, a content scheme is proposed in which the CP provides some financial assistance to MUs. This scheme yields both the MUs and CP benefit due to gained utility and advertisement revenue in response to high data usage, respectively. We prove that a unique functional strategy exists to allocate the content demand and financial assistance based on MUs’ types. This strategy satisfies the incentive compatibility, individual rationality and maximizes the CP’s profit.

Research paper thumbnail of Deep Reinforcement Learning-Aided Bidding Strategies for Transactive Energy Market

Research paper thumbnail of Learning Pareto Optimal Solution of a Multi-Attribute Bilateral Negotiation Using Deep Reinforcement

Electronic Commerce Research and Applications

Log In