AgriBio Causal Multicriteria Decision Making in Food Production Networks

Developing causal multicriteria AI models for decision making under uncertainty in food production networks.

Leaders: Antonio Saraiva and Alexandre Delbem

The agribusiness productive cycles, environmental sustainability, and food security are current demands that defy worldwide authorities. In these settings, proper modeling of heterogeneous large-scale information, resilient learning systems that work with the dynamicity of real environments, and methods that find a balance among many concerns on costs and benefits are significant challenges. Representation learning, resilience enhancement, and multicriteria decision making are important tools to deal with those challenges.

The construction of reliable causal models is an open problem. Advanced methods for generating Dynamic Bayesian Networks (DBNs) based on the capture of tacit knowledge can enable causal models that combine continuous and discrete variables (a level of heterogeneity) and that are also adaptive.

Hybridization through ensembles of conventional knowledge-based models and learning methods is a possible way to produce useful solutions for real-world complex problems. Such processes can contribute to resilience through dataset evaluation and improvement, and selection of learner parameters (as meta- features) in a scenario of ensemble setup, dynamic ensemble selection and meta-learning. The integration of resilient-enhanced models with the DBN-based approaches may generate a higher level of predictive resilience.

The construction of new approaches for multicriteria decision making that combine the solutions found by the conventional knowledge-based techniques and by the proposed learning methods seems a promising strategy to generate short- and long-term innovations.

An important aspect of food security is climate change, mainly involving water supply. Hydrological models are investigated aiming at developing preliminary methods that can combine knowledge-based and data-driven approaches. Models for critical hydrological conditions, as droughts and floods, are also investigated in order to benefit predictions of crop water stress or perishability.

Goals

Representation Learning: new strategies for Heterogeneous Information can emerge by extending multiple representation techniques to construct a new unified feature space. In this way, an embedding is generated to incorporate the main patterns and correlations existing in multiple types of information. Its integration with modeling methods that capture the tacit knowledge can contribute to the Dynamic Representation Learning. The first challenge is the automatic acquisition of those structures and the integration of them with DBNs.

Resilience Enhancement: the investigation of adaptive (evolutionary) ensembles according to large margin distribution for the resilience enhancement of learning is promising. The multiobjective combination of separability measures can enable to find patterns from the marginal sample distribution that, in turn, can produce resilient learning. The investigation of DBN-based approaches can enable the integration of predictive resilience and representation learning, such as dynamicity (concept drift) and heterogeneity. Moreover, large-scale DBNs construction is a challenge that multiobjective evolutionary algorithms (MOEAs) with proper representation can succeed.

Decision making: conventional knowledge-based AgriBio techniques for multicriteria decision making are relevant for dealing with the conflicting demands in AgriBio. The robustness or the stability of approximate-Pareto fronts are the basics for creating new approaches dedicated to the AgriBio challenges facing uncertainty. Resilient criteria should be chosen or formulated to address climate and market changes. They also should enable the construction of procedures for decision making from the solutions found by the techniques developed in the C4AI-AgriBio.

Contact

c4ai-agribio@usp.br

Team

  • Name

    Relevant Information

  • Alexandre Cláudio Botazzo Delbem
    ICMC-USP
  • Ana Carolina Lorena
    IEC-ITA
  • Ana Carolina Sarmento Buarque
    EESC-USP
  • Ângela Silviane Moura Cunha
    ICMC-USP
  • Antonio Mauro Saraiva
    EP-USP
  • Bruno de Carvalho Albertini
    EP-USP
  • Carlos Dias Maciel
    EESC-USP
  • Danilo Hernane Spatti
    ICMC-USP
  • Ednaldo José Ferreira
    EMBRAPA
  • Eduardo Mario Mendiondo
    EESC-USP
  • Ennio Politi Lopes
    ICMC-USP
  • Evandro Marcos Saidel Ribeiro
    FEARP
  • Fabricio Alonso Richmond Navarro
    EESC-USP
  • Fernando Elias Corrêa
    Cepea-USP
  • Fernando Santos Osorio
    ICMC-USP
  • Filipi Miranda Soares
    EP-USP
  • Filippo Ghiglieno
    UFSCar
  • Gabriela Chiquito Gesualdo
    EESC-USP
  • Humberto Ribeiro da Rocha
    IAG-USP
  • Jailson Nunes Leocadio
    EP-USP
  • José Artur Teixeira Brasil
    EESC-USP
  • José Paulo Molin
    ESALQ-USP
  • Julio Cezar Estrella
    ICMC-USP
  • Luis Miguel Castillo Rápalo
    EESC-USP
  • Marcos Roberto Benso
    EESC-USP
  • Maria Clara Fava
    ICMC-USP
  • Marina Batalini de Macedo
    FSP-USP
  • Patricia Angélica Alves Marques
    ESALQ-USP
  • Rafael Geraldeli Rossi
    UFMS
  • Ricardo Marcondes Marcacini
    ICMC-USP
  • Roberto Fray da Silva
    EP-USP
  • Rodrigo Yoiti Tsukahara
    Fundação ABC
  • Silvia Helena Galvao de Miranda
    ESALQ-USP
  • Solange Oliveira Rezende
    ICMC-USP
  • Thiago Guilherme Péra
    ESALQ-USP
  • Thomas C. M. Martin
    IAG-USP
  • Uiara Bandineli Montedo
    EP-USP
  • Wilian França Costa
    Mackenzie