{
  "abstract": "When autonomous systems take operational authority over urban commerce, accountability and human oversight matter as much as efficiency. We present AAIRM, a governance-aware autonomous retail coordination framework addressing three requirements for trustworthy procurement: tamper-evident decision provenance, data sovereignty, and adaptive demand coordination. AAIRM couples a Proximal Policy Optimization (PPO) ordering policy, a permissioned Blockchain Trust Ledger (BTL) for multi-party audit trails, and a Federated Demand Learning (FDL) layer. Evaluation is simulation-based, using a multi-category synthetic environment and the public M5 dataset; no live retail deployment is claimed. At its cost-optimal operating point (92.3% fill), AAIRM lowers normalized inventory cost by 13.2% (95% CI 12.2-14.2) against a conventionally parameterized reorder-point/economic-order-quantity (ROP-EOQ) baseline and by 10.2% (95% CI 8.0-12.4) on M5. Service parity is obtained by moving AAIRM along its own cost-service frontier rather than by retuning baselines: At 96.1% and 97.4% fill, it retains 8.2-point and 4.9-point advantages. A layered ablation attributes 8.8 points of the advantage to the reinforcement learning (RL) policy and 3.8 points to the combined coordination-and-governance block, which we further decompose into feasibility projection, escalation logic, cross-category policy rules, supplier ranking, and negotiation. The language-model orchestrator contributes 0.6 points (95% CI -0.9 to 2.1), statistically indistinguishable from zero and practically equivalent within a ±2-point margin. Against a feasibility-matched multi-agent RL comparator, the residual cost gap does not survive multiple-comparison correction, so the defensible advantages are hard feasibility and auditability, not cost. The BTL adds 6.6% decision-cycle overhead; its 500-case mutation replay is an integration check, and we give an explicit adversary model showing that crash-fault-tolerant ordering does not resist colluding majorities. FedProx converges within 0.9 WAPE points of centralized training at a 1.3-point cost penalty, without secure aggregation or differential privacy; we analyze rather than evaluate when those mechanisms become necessary. A 500-SKU study exposes a reward-hacking failure in a high-spoilage category, corrected by category-specific governance. We close with an architectural, non-empirical reading of labor exposure and three governance instruments regulators can act on.",
  "abstract_shown": true,
  "affiliations": [
    "AI Center, Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia.",
    "AI V&V Lab, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.",
    "Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing & Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia.",
    "Faculty of Computer Studies, Arab Open University-Bahrain, A'Ali, Bahrain."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Toqeer Ali Syed"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ali Akarma"
    },
    {
      "affs": [
        2
      ],
      "name": "Shahid Kamal"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Salman Jan"
    },
    {
      "affs": [
        0
      ],
      "name": "Ahmad B Alkhodre"
    },
    {
      "affs": [
        2
      ],
      "name": "Arshad Jamal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/afbajpa9v52pj.bib",
  "cited_by": 0,
  "doi": "10.3389/frai.2026.1942376",
  "fulltext_url": "https://europepmc.org/articles/PMC13683724",
  "id": "afbajpa9v52pj",
  "issns": [
    "2624-8212"
  ],
  "landing_url": "https://europepmc.org/article/MED/42909062",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1404|Supply Chain and Inventory Management"
    ]
  ],
  "openalex": "W7220846161",
  "pdf_url": "https://europepmc.org/articles/PMC13683724?pdf=render",
  "pmcid": "PMC13683724",
  "pmid": "42909062",
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/afbajpa9v52pj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "MED:42909062",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42909062"
    }
  ],
  "subjects": [],
  "title": "Governance-aware autonomous retail coordination in artificial intelligence cities using multi-agent reinforcement learning, blockchain accountability, and federated learning.",
  "title_text": "Governance-aware autonomous retail coordination in artificial intelligence cities using multi-agent reinforcement learning, blockchain accountability, and federated learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/afbajpa9v52pj",
  "venue": "Frontiers in artificial intelligence",
  "versions": []
}