haojianye@126.com
CCF青年人才托举计划， 天津大学北洋青年****
副教授
驾驶行为分析与数据可视化技术服务

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基于社会规范涌现的多智能体协同技术研究

Incorporating Fairness into Agent Interactions Modeled as Two-player Normal-form Games

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Designing Minimal Effective Normative Systems with the Help of Lightweight Formal Methods

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Accelerating Norm Emergence through Hierarchical Heuristic Learning

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Optimal personalized defense strategy against man-in-the-middle attack

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Socially-aware Multiagent Learning towards Socially Optimal Outcomes

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Reinforcement Social Learning of Coordination in Cooperative Multiagent Systems

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The Dynamics of Reinforcement Social Learning in Cooperative Multiagent Systems

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Heuristic Collective Learning for Efficient and Robust Emergence of Social Norms

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Spatial evolutionary game-theoretic perspective on agent-based complex negotiations

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Analyzing Multi-agent Systems with Probabilistic Model Checking Approach

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An Adaptive Markov Strategy for Effective Network Intrusion Detection

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Reciprocal Social Strategy in Social Repeated Games

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Toward Efficient Agreements in Real-time Multilateral Agent-based Negotiations

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Incorporating Fairness into Infinitely Repeated Games with Conflicting Interests for Conflicts Elimination

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Learning to Achieve Socially Optimal Solutions in General-sum Games

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Probabilistic Model Checking Multi-agent Behaviors in Dispersion Games Using Counter Abstraction

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An Efficient Negotiation Protocol to Achieve Socially Optimal Allocation

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An Adaptive Markov Strategy for Defending Smart Grid False Data Injection from Malicious Attackers

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The Dynamics of Reinforcement Social Learning in Networked Cooperative Multiagent Systems

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Improving EGT- based Robustness Analysis of Negotiation Strategies in Multi-agent Systems via Model Checking

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Introducing Decision Entrustment Mechanism into Repeated Bilateral Agent Interactions to Achieve Socially Optimal Solutions

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Multiagent Reinforcement Social Learning towards Coordination in Cooperative Multiagent Systems

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An Efficient and Robust Negotiating Strategy in Bilateral Negotiations over Multiple Item

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Achieving Socially Optimal Outcomes in Multiagent Systems with Reinforcement Social Learning