{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:29:04Z","timestamp":1775197744853,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Multi-agent perception systems must operate under fundamental asymmetries: some agents provide fast but unreliable observations, while others deliver higher-quality evidence with delay and uncertain correspondence. Traditional deterministic orchestration and rule-based fusion struggle to manage these trade-offs, often producing brittle or unstable behavior. We introduce a probabilistic orchestration framework that treats coordination as an epistemic generation problem\u2014constructing and updating belief states under uncertainty\u2014rather than a selection problem. Instead of committing to a single agent\u2019s output, the orchestrator constructs a belief state that explicitly represents uncertainty, evidential provenance, and temporal relevance. Decisions are produced through latency-aware, association-weighted fusion, and uncertainty itself becomes a first-class signal governing action, deferral, and learning. Crucially, the orchestrator enables controlled teacher\u2013student adaptation: high-confidence, well-associated stationary observations are gated into a feedback loop that improves ego perception over time while mitigating error amplification. We demonstrate the approach on an infrastructure-assisted dual-camera obstacle-recognition task. Experimental results show improved robustness to distance, occlusion, and delayed evidence compared to ego-only and deterministic orchestration baselines. By operationalizing orchestration as epistemic generation, this work provides a unifying framework for robust decision-making and safe adaptation in multi-agent systems, with implications that extend beyond perception to agentic and generative AI architectures.<\/jats:p>","DOI":"10.3390\/a19040261","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T08:22:36Z","timestamp":1774858956000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Probabilistic Orchestrator for Indeterministic Multi-Agent Systems in Real-Time Environments"],"prefix":"10.3390","volume":"19","author":[{"given":"Arkady","family":"Bovshover","sequence":"first","affiliation":[{"name":"School of Computer Science, Holon Institute of Technology, Holon 5810201, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei","family":"Kojukhov","sequence":"additional","affiliation":[{"name":"School of Computer Science, Holon Institute of Technology, Holon 5810201, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0298-4547","authenticated-orcid":false,"given":"Ilya","family":"Levin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Holon Institute of Technology, Holon 5810201, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,29]]},"reference":[{"key":"ref_1","unstructured":"Wooldridge, M. 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