Multiagent Systems: A Survey from a Machine Learning Perspective

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Items 201–300 of 1,001

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22. A Review of Multi-Robot Systems and Soft Robotics: Challenges and Opportunities
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28. Distributed scalable multi-robot learning using particle swarm optimization
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32. Multiagent learning in the presence of memory-bounded agents
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33. International Journal INFORMATION THEORIES & APPLICATIONS
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35. Value-Based Planning for Teams of Agents in Stochastic Partially Observable Environments
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38. Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence
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40. Multi-Agent Systems on Sensor Networks: A Distributed Reinforcement Learning Approach
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41. Transfer learning in multi-agent systems through parallel transfer
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42. Individual and collaborative behaviors in a team of homogeneous robotic soccer agents
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43. Learning in Network Contexts: Experimental Results from Simulations
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44. Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review
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45. MRL-CC: a novel cooperative communication protocol for QoS provisioning in wireless sensor networks
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46. Spatial Intention Maps for Multi-Agent Mobile Manipulation
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47. Concurrency and knowledge-level communication in agent languages
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48. A neuro-fuzzy approach to self-management of virtual network resources
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49. Parallel learning in heterogeneous multi-robot swarms
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50. An analysis framework for ad hoc teamwork tasks
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51. A Survey on Trusted Distributed Artificial Intelligence
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52. A multi-agent reinforcement learning approach to robot soccer
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53. A multi-agent reinforcement learning algorithm based on Stackelberg game
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54. Continuous-time path planning for multi-agents with fuzzy reinforcement learning
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55. A Comparative Analysis of SVM and ELM Classification on Software Reliability Prediction Model
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56. Speeding up learning automata based multi agent systems using the concepts of stigmergy and entropy
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57. Learning parallel portfolios of algorithms
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58. Intercell Interference Management in OFDMA Networks: A Decentralized Approach Based onReinforcement Learning
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59. Multidisciplinary and Historical Perspectives for Developing Intelligent and Resource-Efficient Systems
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60. Ad hoc teamwork by learning teammates’ task
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61. A Survey on Agent-Oriented Oriented Software Engineering Research
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62. Multi‐Vehicle Cooperative Control Using Mixed Integer Linear Programming
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63. CERN for AI: a theoretical framework for autonomous simulation-based artificial intelligence testing and alignment
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64. Scalable multiagent learning through indirect encoding of policy geometry
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66. Influence of virtual agent politeness behaviors on how users join small conversational groups
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67. Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments
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68. Conflict resolution in multi-agent based Intelligent Environments
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69. Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
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70. Learning to Coordinate with Deep Reinforcement Learning in Doubles Pong Game
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71. Modeling multidisciplinary design with multiagent learning
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73. Adaptive Learning: A New Decentralized Reinforcement Learning Approach for Cooperative Multiagent Systems
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74. Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions
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75. A Landscape of Agent Systems in the Real World
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76. Learning to select a coordination mechanism
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77. SMS mobile botnet detection using a multi-agent system
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78. Controllability and stabilizability analysis of signed consensus networks
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79. Distributed cooperative optimization for multiple heterogeneous Euler-Lagrangian systems under global equality and inequality constraints
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80. Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control
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81. Coordinated behavior of cooperative agents using deep reinforcement learning
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83. Engineering Multi-Agent Systems
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84. Shaping fitness functions for coevolving cooperative multiagent systems
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85. Multi-agent systems by incremental gradient reinforcement learning
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86. Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems
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