Leveling up on war gaming
NewsApril 04, 2019
MALIBU, Calif. HRL Laboratories -- an R & D lab owned by The Boeing Company and General Motors -- has embarked on a new program with the Defense Advanced Research Projects Agency (DARPA) that aims to develop an artificial intelligence (AI) decision-making engine for multiagent military problems such as multidomain war gaming and strategic battle management.
The goal of the project -- known as the Adversarial Game-based Iterative Learning Engine (AGILE) -- is to learn effective tactics and behaviors by playing adversarial games, such as 2v2 air-to-air engagement, at a human performance level. It is hoped that AGILE will enable the development of advanced reinforcement learning (RL) algorithms, a type of machine learning that does not require any human-labeled data to train an AI agent.
The AGILE goal is a fully autonomous system that will make high-level decisions that humans usually make: “Recent advances in RL have used these types of algorithms to train AI agents to play against humans and win board games, such as Go or chess, that have very well-defined moves to specific places on a board,” said senior researcher Deepak Khosla, HRL’s principal investigator on AGILE. “Our AI strategies are similar to the ones developed for those games, but we are aiming to expand to multi-agent games with imperfect information and numerous variations that far exceed those in a deterministic game with perfect information such as Go or chess.”
The key innovation the HRL team hopes to introduce is reinforcement learning that exhibits robustness, scalability, and adaptability; in the meantime, The Boeing Company will focus on developing a variety of multi-agent scenarios and simulations plus validation of AGILE's performance.
