Actionranging

Actionranging is a decision-making paradigm in which an agent reasons over ranges of possible actions rather than selecting a single action from a discrete set. It represents the action space as intervals or regions with associated outcome estimates and uncertainties. The goal is to identify an action range that satisfies safety and performance criteria, or to select a representative action within that range when execution occurs.

Formalization often treats the action space A as a continuous domain. For each action a, the environment yields outcomes with uncertainty, represented by bounds [L(a), U(a)] or a probabilistic model μ(a). An action range R ⊆ A is chosen to ensure constraint satisfaction (for example, safety or energy limits) while optimizing an objective over R, or to enable robust execution by selecting actions within R.

Approaches include robust optimization, interval analysis, and chance-constrained planning, as well as integrations with model-based control and reinforcement learning. In practice, actionranging can be used to plan with safety envelopes, reducing sensitivity to model error.

Applications span robotics (manipulation, locomotion), autonomous vehicles (motion planning with safety margins), drones, and human-robot collaboration. It is also relevant to resource-constrained scheduling and any domain where action-effect uncertainty is significant.

Challenges include computational overhead, reliable uncertainty estimation, and avoiding over-conservatism when action ranges are wide.