Authors:
José Cascalho
1
;
Paulo Trigo
2
;
Maria João 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
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.