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Authors: José Cascalho 1 ; Paulo Trigo 2 ; Maria Cruz 3 ; Armando Mendes 4 ; Eva Giacomello 5 ; Adriana Ressurreição 6 ; Tomás Dentinho 7 and Telmo Morato 5

Affiliations: 1 BioISI - Biosystems and Integrative Sciences Institute, FCUL -Universidade de Lisboa, Lisboa, Portugal, NIDeS - Núcleo de Desenvolvimento em e-Saúde, Universidade dos Açores, Ponta Delgada, Portugal, FCT - Universidade dos Açores, Ponta Delgada and Portugal ; 2 BioISI - Biosystems and Integrative Sciences Institute, FCUL -Universidade de Lisboa, Lisboa, Portugal, ISEL - Instituto Superior de Engenharia de Lisboa, Lisboa and Portugal ; 3 FCT - Universidade dos Açores, Ponta Delgada and Portugal ; 4 NIDeS - Núcleo de Desenvolvimento em e-Saúde, Universidade dos Açores, Ponta Delgada, Portugal, FCT - Universidade dos Açores, Ponta Delgada, Portugal, Algoritmi, Universidade do Minho and Portugal ; 5 MARE Marine and Environmental Sciences Centre, Horta, Portugal, OKEANOS Centre, Universidade dos Açores, Horta and Portugal ; 6 MARE Marine and Environmental Sciences Centre, Horta, Portugal, OKEANOS Centre, Universidade dos Açores, Horta, Portugal, CCMAR Centre of Marine Sciences, Faro and Portugal ; 7 FCAA - Universidade dos Açores, Angra do Heroísmo and Portugal

ISBN: 978-989-758-350-6

Keyword(s): Multiagents, Finite-state Machines, Muti-criteria Decision-making.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Technologies ; Multi-Agent Systems ; Operational Research ; Simulation ; Software Engineering ; Symbolic Systems

Abstract: Understanding fishermen decision-making proccess, plays a key role in predicting the impacts of the fishing activity in the marine ecosystems. Simulating fishing activity using multiagent based approaches provides tools that assist decision-makers in order to pursuit sustainable fishing activity. In this paper we present a multiagent architecture for the fishing activity where geo-referenced resources and fishing agents with different profiles are used to model and simulate the complexity of human fishing activity. A first implementation of the model (via NetLogo), along with gathered results, provides insights into the capability to build a research tool for fisheries management.

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Paper citation in several formats:
Cascalho, J.; Trigo, P.; Cruz, M.; Mendes, A.; Giacomello, E.; Ressurreição, A.; Dentinho, T. and Morato, T. (2019). SIMSEA: A Multiagent Architecture for Fishing Activity in a Simulated Environment.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-350-6, pages 202-209. DOI: 10.5220/0007393502020209

@conference{icaart19,
author={José Cascalho. and Paulo Trigo. and Maria João Cruz. and Armando Mendes. and Eva Giacomello. and Adriana Ressurrei\c{C}ão. and Tomás Dentinho. and Telmo Morato.},
title={SIMSEA: A Multiagent Architecture for Fishing Activity in a Simulated Environment},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2019},
pages={202-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007393502020209},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - SIMSEA: A Multiagent Architecture for Fishing Activity in a Simulated Environment
SN - 978-989-758-350-6
AU - Cascalho, J.
AU - Trigo, P.
AU - Cruz, M.
AU - Mendes, A.
AU - Giacomello, E.
AU - Ressurreição, A.
AU - Dentinho, T.
AU - Morato, T.
PY - 2019
SP - 202
EP - 209
DO - 10.5220/0007393502020209

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