System and method for strategic airspace deconfliction using cooperative multi-agent reinforcement learning

A Cooperative Multi-Agent Platform (CMAP) system for safety-adaptive strategic deconfliction, configured for deployment within an Automated Data Service Provider (ADSP) infrastructure, the system comprising:

• a. a Regulatory Compliance Monitor (RCM) module configured to:
• i. perform real-time conformance monitoring for a fleet of Unmanned Aircraft Systems (UAS), and
• ii. calculate a real-time Conflict Risk Score based on said conformance monitoring;
• b. a processor;
• and
• c. a non-transitory memory storing a trained Multi-Agent Reinforcement Learning (MARL) policy network and executable instructions that, when executed by the processor, configure the processor to function as a MARL Engine, said MARL Engine configured to:
• i. receive said real-time Conflict Risk Score from the RCM;
• ii. generate a Local Observation Vector comprising said real-time Conflict Risk Score and a historical negotiation context;
• iii. select a strategic negotiation primitive from a predefined action space by inputting said Local Observation Vector into said trained MARL policy network, wherein the selected primitive is dynamically determined based at least in part on said real-time Conflict Risk Score;
• and
• d. an Intent Negotiation Module (INM) configured to transmit the selected strategic negotiation primitive to a peer ADSP agent.

A non-transitory computer-readable medium storing executable instructions that, when executed by a processor of an Automated Data Service Provider (ADSP) operating in a Unmanned Aircraft Systems Traffic Management (UTM) network, cause the processor to perform the steps of:

• a. receiving, from an associated Regulatory Compliance Monitor (RCM), a real-time Conflict Risk Score quantifying a risk of non-conformance;
• b. generating a Local Observation Vector, wherein said Local Observation Vector comprises said real-time Conflict Risk Score and a historical negotiation context;
• c. inputting said Local Observation Vector into a trained Multi-Agent Reinforcement Learning (MARL) policy network;
• d. selecting, by the MARL policy network, a strategic negotiation primitive from a predefined action space, wherein said selection is dynamically determined based at least in part on said real-time Conflict Risk Score;
• and
• e. transmitting the selected strategic negotiation primitive to a peer ADSP agent via an Intent Negotiation Module (INM).