Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection

Imagine you're developing a system to automate scientific discovery, where you need to decide which experiments to run and which hypotheses to test. Currently, many systems focus on maximizing immediate information gain, which can lead to poor decisions when the best path involves building capabilities that don't yield immediate results. This is what's called myopic decision-making, where the planner overlooks the value of actions that set up future opportunities, leading to suboptimal choices and potentially missing out on significant discoveries.

To address this, the authors propose a new framework that treats the discovery process as a stochastic shortest-path problem in belief space. This means they consider not just the immediate outcomes of actions but also how those actions can change the landscape of future possibilities. They introduce CG-Plan, a replanner that incorporates a capability-aware heuristic, allowing it to better evaluate the long-term benefits of acquiring new capabilities. This approach shows that when planning for scientific discovery, it's crucial to account for the potential future actions that can arise from current experiments, which traditional methods often ignore. For builders, this means a shift in how to design systems for scientific exploration, emphasizing the importance of capability development over short-term gains.

The approach introduces a new way to value experiments based on future capabilities rather than immediate information gain.

The method is tested in a controlled environment, showing consistent performance under specific conditions.