Bridging the Gap Between High-Level Reasoning in Strategic Agent Coordination and Low-Level Agent Development

Recent advances in fields such as computer vision and natural language processing have paved the way for developing agents capable of automatically interpreting their surrounding environment. Concurrently, advances in artificial intelligence have made the coordination of many such agents possible. However, there is little work considering both the low-level reasoning that allows agents to interpret their environment, such as deep learning techniques, and the high-level reasoning that coordinates such agents. By considering both together, we can better handle real-world scenarios, for example by planning at a high level with low-level uncertainty in mind, or even by improving low-level processing by using high-level reasoning to place the agent in the best scenario for success.