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Discrete-Choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities

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Autonomous Agents and Multiagent Systems. Best and Visionary Papers (AAMAS 2023)

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Abstract

Making Smart Cities more sustainable, resilient and democratic is emerging as an endeavor of satisfying hard constraints, for instance meeting net-zero targets. Decentralized multi-agent methods for socio-technical optimization of large-scale complex infrastructures such as energy and transport networks are scalable and more privacy-preserving by design. However, they mainly focus on satisfying soft constraints to remain cost-effective. This paper introduces a new model for decentralized hard constraint satisfaction in discrete-choice combinatorial optimization problems. The model solves the cold start problem of partial information for coordination during initialization that can violate hard constraints. It also preserves a low-cost satisfaction of hard constraints in subsequent coordinated choices during which soft constraints optimization is performed. Strikingly, experimental results in real-world Smart City application scenarios demonstrate the required behavioral shift to preserve optimality when hard constraints are satisfied. These findings are significant for policymakers, system operators, designers and architects to create the missing social capital of running cities in more viable trajectories.

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Notes

  1. 1.

    Available at: https://github.com/epournaras/EPOS/tree/hard_constraints.

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Acknowledgements

This work is supported by a UKRI Future Leaders Fellowship (MR/W009560/1): ‘Digitally Assisted Collective Governance of Smart City Commons–ARTIO’, the Alan Turing Fellowship project ‘New Edge-Cloud Infrastructure for Distributed Intelligent Computing’ and the SNF NRP77 ‘Digital Transformation’ project “Digital Democracy: Innovations in Decision-making Processes”, #407740_187249.

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Authors and Affiliations

  1. School of Computing, University of Leeds, Leeds, UK

    Srijoni Majumdar, Chuhao Qin & Evangelos Pournaras

Authors
  1. Srijoni Majumdar
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  2. Chuhao Qin
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  3. Evangelos Pournaras
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Correspondence to Srijoni Majumdar .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Francesco Amigoni

  2. Rutgers University, Piscataway, NJ, USA

    Arunesh Sinha

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Majumdar, S., Qin, C., Pournaras, E. (2024). Discrete-Choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities. In: Amigoni, F., Sinha, A. (eds) Autonomous Agents and Multiagent Systems. Best and Visionary Papers. AAMAS 2023. Lecture Notes in Computer Science(), vol 14456. Springer, Cham. https://doi.org/10.1007/978-3-031-56255-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-56255-6_4

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