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Mixed Cooperative-Competitive Communication Using Multi-agent Reinforcement Learning
By using communication between multiple agents in multi-agent environments, one can reduce the effects of partial observability by combining one...
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Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling
This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives...
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A human learning optimization algorithm with competitive and cooperative learning
Human learning optimization (HLO) is a simple yet powerful metaheuristic developed based on a simplified human learning model. Competition and...
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Game-theoretic multi-agent motion planning in a mixed environment
The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed...
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Learning-Based Social Coordination to Improve Safety and Robustness of Cooperative Autonomous Vehicles in Mixed Traffic
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability...
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Experimental Design Method to Finetune Cooperative Coevolutionary Algorithms Solving Multiobjective Problems
In today’s business environment, organizations face complex challenges that require efficient and effective solutions. Process optimization and...
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Cooperative and non-cooperative algorithms for distributed parallel jobs scheduling
This paper deals with multi-factory parallel job scheduling in which independent factories try to satisfy market demand by cooperating with each...
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Hindsight-aware deep reinforcement learning algorithm for multi-agent systems
Classic reinforcement learning algorithms generate experiences by the agent's constant trial and error, which leads to a large number of failure...
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Team formation through an assessor: choosing MARL agents in pursuit–evasion games
Team formation in multi-agent systems usually assumes the capabilities of each team member are known, and the best formation can be derived from that...
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A cooperative genetic algorithm based on extreme learning machine for data classification
It is a challenging task to optimize network structure and connection parameters simultaneously in a single hidden layer feedforward neural network...
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Multi-agent reinforcement learning for autonomous vehicles: a survey
In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed traffic. This cohabitation raises serious challenges, both in...
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Human–Robot Cooperation in Economic Games: People Show Strong Reciprocity but Conditional Prosociality Toward Robots
Understanding how people socially engage with robots is becoming increasingly important as these machines are deployed in social settings. We...
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Distributed Reinforcement Learning for Robot Teams: a Review
Purpose of ReviewRecent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands...
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Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems
This chapter proposes an artificial intelligenceArtificial intelligence based solution for the efficient operation of a heterogeneous cluster of...
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The State of the Art
In this chapter, existing works on mixed-traffic behavior modeling in shared spaces and their limitations are discussed. As discussed in Section 2.4,...
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Individual Rationality and Real-World Strategic Interactions: Understanding the Competitive-Cooperative Spectrum
We are interested in game-theoretic models of (bounded) rationality, and specifically in investigating (in)adequacy of the traditional models of...
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Design of AI speech recognition platform based on BP network for English blended teaching mode
Artificial intelligence speech recognition technology has a broad application prospect in the field of education. In order to improve the efficiency...
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Strangers on a Team?: Human Companions, Compared to Strangers or Individuals, are More Likely to Reject a Robot Teammate
As robots become more common, people interact with them individually, with strangers, and with friends. For example, when coming across a robot in a...
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Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which has registered tremendous success in solving various...
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Distributed Multi-agent Target Search and Tracking With Gaussian Process and Reinforcement Learning
Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially...