Formal Modeling of a Tele-surgery Domain as a Multi-agent Planning Problem

2013 ◽  
Author(s):  
A. K. Lal ◽  
R. Niyogi
Keyword(s):  
Formal Modeling ◽  
Planning Problem ◽  
Multi Agent ◽  
Multi Agent Planning

scholarly journals An Abstract Framework for Non-Cooperative Multi-Agent Planning

2019 ◽  
Vol 9 (23) ◽  
pp. 5180 ◽  
Author(s):  
Jaume Jordán ◽  
Javier Bajo ◽  
Vicent Botti ◽  
Vicente Julian
Keyword(s):  
Strategic Planning ◽  
Strategic Behavior ◽  
Planning Problem ◽  
Plan Execution ◽  
Complete Problem ◽  
Multi Agent ◽  
Exhaustive Study ◽  
Abstract Framework ◽  
Multi Agent Planning

In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents’ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.

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.

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