Automated Planning Logic Synthesis for Autonomous Unmanned Vehicles in Competitive Environments with Deceptive Adversaries

We developed a new approach for automated synthesis of a planning logic for autonomous unmanned vehicles. This new approach can be viewed as an automated iterative process during which an initial version of a logic is synthesized and then gradually improved by detecting and fixing its shortcomings. This is achieved by combining data mining for extraction of vehicle’s states of failure and Genetic Programming (GP) technique for synthesis of corresponding navigation code. We verified the feasibility of the approach using unmanned surface vehicles (USVs) simulation. Our focus was specifically on the generation of a planning logic used for blocking the advancement of an intruder boat towards a valuable target. Developing autonomy logic for this behavior is challenging as the intruder’s attacking logic is human-competitive with deceptive behavior so the USV is required to learn specific maneuvers for specific situations to do successful blocking. We compared the performance of the generated blocking logic to the performance of logic that was manually implemented. Our results show that the new approach was able to synthesize a blocking logic with performance closely approaching the performance of the logic coded by hand.