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Decentralized Multi-Agent Coordination Algorithms

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Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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The contract focuses on the development of decentralized coordination algorithms for AI agents operating in peer-to-peer networks. Key areas include distributed task planning, multi-agent reinforcement learning, and the creation of fault-tolerant consensus methods designed to function effectively in contested or adversarial environments. The goal is to enable robust and efficient collaboration among autonomous agents without relying on centralized control, enhancing their operational capabilities in dynamic and potentially hostile settings. Issued by the Department of Defense's DARPA Information Processing Technologies Office, this subcontract opportunity falls under NAICS code 541715. The solicitation was posted on June 10, 2026, with a response deadline on August 25, 2026. Although specific details such as location and point of contact are not provided, the contract emphasizes advanced research and development efforts that support the defense sector's interest in resilient, distributed AI systems.

General Info

Develop decentralized algorithms for AI agents enabling fault-tolerant, adversarial-resistant collaboration.

Agency

Department Of Defense → Darpa - Information Processing Technologies OfficeView Agency

NAICS

541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)View NAICS

Place of Performance

Not specified

Set-Aside

NONE

Documents

(0)

No documents available

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Organization & Contact Information

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AgencyDepartment Of Defense → Darpa - Information Processing Technologies Office
ContactsNo contacts available
OfficeN/A
Organization / Agency
Department Of Defense → Darpa - Information Processing Technologies Office
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Office AddressN/A
ContactsNo contact information available

Full Description

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Develop peer-to-peer coordination algorithms for AI agents, including distributed task planning, multi-agent reinforcement learning, and fault-tolerant consensus methods in contested environments.

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