Authors:
Valeria Javalera-Rincon
1
;
Vicenc Puig Cayuela
2
;
Bernardo Morcego Seix
2
and
Fernando Orduña-Cabrera
1
Affiliations:
1
Advanced Systems Analysis and Ecosystem Services and Management Programs, International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg and Austria
;
2
Advanced Control Systems Group, Universitat Politècnica de Catalunya (UPC), Rambla Sant Nebridi, 10, 08222, Terrassa and Spain.
Keyword(s):
Multi-Agent Systems, Large Scale Systems, Linkage of Models, Reinforcement Learning, Distributed Control, Water Networks, Large Scale Systems.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
Distributed Problem Solving
;
Enterprise Information Systems
;
Evolutionary Computing
;
Industrial Applications of AI
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Multi-Agent Systems
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
This work shows how a Linker agent coordinates a cooperative MAS environment to seek a global optimum. The approach is applied to the Barcelona Drinking Water Network (DWN) administrated by AGBAR where the main problem was to coordinate the control of three different sectors of the network. Each part has a local controller (local agent) to solve the local water demands, but it also has to cooperate with the other agents to satisfy the water demands of the whole network. The cooperative Linker agent implemented, learns by using a Reinforcement Learning algorithm, called PlanningByExploration Behaviour with penalization (Javalera et al., 2019), to converge towards an optimal (or suboptimal) value of each of the variables that connect the local agents. For the training and simulation of the Linker agents real historical data of the Barcelona DWN provided by AGBAR were used, as well as the data to model the distributed topology of the DWN. Moreover, some results of the simulations of thi
s approach in contrast with the results of a centralized Model Predictive Controller are depicted.
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