Distributed Adaptive Control: An ideal Cognitive Architecture candidate for managing a robotic recycling plant
December 23, 2020 Β· Declared Dead Β· π Living Machines
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Oscar Guerrero-Rosado, Paul Verschure
arXiv ID
2012.12586
Category
cs.MA: Multiagent Systems
Cross-listed
cs.AI,
cs.NE,
cs.RO,
eess.SY
Citations
3
Venue
Living Machines
Last Checked
3 months ago
Abstract
In the past decade, society has experienced notable growth in a variety of technological areas. However, the Fourth Industrial Revolution has not been embraced yet. Industry 4.0 imposes several challenges which include the necessity of new architectural models to tackle the uncertainty that open environments represent to cyber-physical systems (CPS). Waste Electrical and Electronic Equipment (WEEE) recycling plants stand for one of such open environments. Here, CPSs must work harmoniously in a changing environment, interacting with similar and not so similar CPSs, and adaptively collaborating with human workers. In this paper, we support the Distributed Adaptive Control (DAC) theory as a suitable Cognitive Architecture for managing a recycling plant. Specifically, a recursive implementation of DAC (between both single-agent and large-scale levels) is proposed to meet the expected demands of the European Project HR-Recycler. Additionally, with the aim of having a realistic benchmark for future implementations of the recursive DAC, a micro-recycling plant prototype is presented.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multiagent Systems
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Mean Field Multi-Agent Reinforcement Learning
π
π
The Cartographer
A Survey and Critique of Multiagent Deep Reinforcement Learning
π
π
The Cartographer
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
π
π
The Cartographer
Collaborative vehicle routing: a survey
R.I.P.
π»
Ghosted
Deep Reinforcement Learning for Swarm Systems
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted