Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision-making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that the performance measure can be exactly and correctly specified in the form of a utility or reward function” S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (Pearson, 2020). Once the reward function is known, any problem can be formulated as an optimization or search problem—the areas well explored within the AI community. However, while the long-term objectives of a specific activity are often well defined, constructing short-term rewards that remain aligned with those goals and consistent with real-world constraints remains a major unresolved challenge. Such alignment has been achieved in domains such as chess, Go, and supervised machine learning problems, where objectives are well defined and easily simulated. However, no universal solution exists for defining intermediate rewards for complex, evolving scientific goals, which remains an open challenge. Correspondingly, the key to operationalizing automated instruments, integrating multi-instrument self-driving laboratories, and building geographically distributed research facilities is to generate experiment-aligned, probabilistic, domain-specific reward functions. These rewards must be consistent with long-term experimental objectives while remaining actionable on the timescales of decision-making on laboratory tools in microscopy and materials synthesis labs—proper reward definition operationalizes scientific intent. Here, we review the extant reward structures in the physical sciences and summarize opportunities for reward design informed by physical principles, human heuristics, and LLM-based reasoning.
Kalinin et al. (Thu,) studied this question.