Agents of exploration and discovery

Abstract

Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the problem, identify component functions, describe current abilities, and note remaining limitations. Finally, I discuss obstacles that the community must overcome before it can develop integrated agents of exploration and discovery.

Integrated Systems for Computational Scientific Discovery

“…Our challenge has much in common with Kitano's (2016) proposal to develop an AI system that wins a Nobel Prize in science. The vision also shares features with another recent call for joining discovery methods with robotic agents that explore unknown environments (Langley, 2021). Nevertheless, the development of integrated discovery systems raises enough challenges on its own to keep the AI research community occupied for many years to come.…”