Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours-Reference-Cited by-同舟云学术

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

Published:2021-07-05 Issue:11 Volume:36 Page:6273-6311
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J Intell Syst

Author:

Samarasinghe Dilini¹^ORCID,Barlow Michael¹^ORCID,Lakshika Erandi¹^ORCID,Kasmarik Kathryn¹^ORCID

Affiliation:

1. School of Engineering and IT University of New South Wales Canberra Australian Capital Territory Australia

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22550

Reference52 articles.

1. MalialisK DevlinS KudenkoD. Resource abstraction for reinforcement learning in multiagent congestion problems. In:AAMAS'16 International Foundation for Autonomous Agents and Multiagent Systems Singapore. Richland SC: International Foundation for Autonomous Agents and Multiagent Systems; 2016:503‐511.

2. On the cognitive process of human problem solving

3. Cognition and the Creation of Ideas

4. Human problem solving: The state of the theory in 1970.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Grammar‐based autonomous discovery of abstractions for evolution of complex multi‐agent behaviours;Swarm and Evolutionary Computation;2022-08

2. Grammar-based cooperative learning for evolving collective behaviours in multi-agent systems;Swarm and Evolutionary Computation;2022-03

3. Flow-Based Reinforcement Learning;IEEE Access;2022

4. Task Allocation in Multi-Agent Systems with Grammar-Based Evolution;Proceedings of the 21th ACM International Conference on Intelligent Virtual Agents;2021-09-14