scholarly journals An Efficient Approach to Do Multi-agent Planning

2015 ◽  
Author(s):  
Lin Zeng ◽  
Lei Li
Keyword(s):  
Efficient Approach ◽  
Multi Agent ◽  
Multi Agent Planning

Realization of multi-agent planning system for autonomous spacecraft

2005 ◽  
Vol 36 (4) ◽  
pp. 266-272 ◽  
Author(s):  
Xu Rui ◽  
Cui Pingyuan ◽  
Xu Xiaofei
Keyword(s):  
Planning System ◽  
Multi Agent ◽  
Multi Agent Planning

scholarly journals Multi-Agent Planning in the Presence of Multiple Goals

2006 ◽  
pp. 301-325 ◽  
Author(s):  
Michael Bowling ◽  
Rune Jensen ◽  
Manuela Veloso
Keyword(s):  
Multiple Goals ◽  
Multi Agent ◽  
Multi Agent Planning

Action dependencies in privacy-preserving multi-agent planning

2018 ◽  
Vol 32 (6) ◽  
pp. 779-821
Author(s):  
Shlomi Maliah ◽  
Guy Shani ◽  
Roni Stern
Keyword(s):  
Privacy Preserving ◽  
Multi Agent ◽  
Multi Agent Planning

scholarly journals IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs

2019 ◽  
Vol 33 ◽  
pp. 6062-6069 ◽  
Author(s):  
Yanlin Han ◽  
Piotr Gmytrasiewicz
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Computing ◽  
Neural Network Architecture ◽  
Markov Decision ◽  
Planning Algorithm ◽  
Multi Agent ◽  
Partially Observable ◽  
Multi Agent Planning

This paper introduces the IPOMDP-net, a neural network architecture for multi-agent planning under partial observability. It embeds an interactive partially observable Markov decision process (I-POMDP) model and a QMDP planning algorithm that solves the model in a neural network architecture. The IPOMDP-net is fully differentiable and allows for end-to-end training. In the learning phase, we train an IPOMDP-net on various fixed and randomly generated environments in a reinforcement learning setting, assuming observable reinforcements and unknown (randomly initialized) model functions. In the planning phase, we test the trained network on new, unseen variants of the environments under the planning setting, using the trained model to plan without reinforcements. Empirical results show that our model-based IPOMDP-net outperforms the other state-of-the-art modelfree network and generalizes better to larger, unseen environments. Our approach provides a general neural computing architecture for multi-agent planning using I-POMDPs. It suggests that, in a multi-agent setting, having a model of other agents benefits our decision-making, resulting in a policy of higher quality and better generalizability.

Multi-Agent Planning Under Complex Constraints for Deep-Space Probes Group

2020 ◽  
Author(s):  
Yuting Zhao ◽  
Zhaoyu Li ◽  
Shenying Zhu ◽  
Zixuan Liang ◽  
Rui Xu
Keyword(s):  
Deep Space ◽  
Multi Agent ◽  
Complex Constraints ◽  
Multi Agent Planning
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