Initial Condition Retrieving for Hybrid and Numeric Planning Problems

Real-world applications of planning techniques often deal with dynamic and noisy environments, where sensor readings are by no means perfect and the world's states can evolve in unexpected ways. This is particularly challenging for hybrid discrete-continuous planning approaches, where processes and events can be strongly affected by even slightly different initial conditions of the world, and planning problems are notoriously difficult to cope with. In this paper, we introduce the Initial Condition Retrieving (ICR) problem to foster hybrid planning in real-world applications. Given a knowledge model of a planning problem and a trace, solving the ICR problem allows identifying the space of all the initial conditions from which the provided plan is guaranteed to reach a goal state. We define thre tasks: (i) retrieving any initial condition, (ii) fixing only some desired initial values and retrieving the fully assigned initial condition which fills the unassigned values, or (iii) specifying a full initial condition from which we are unable to reach the goal and retrieve its closest counterpart. Experiments on well-known hybrid planning domains demonstrate the efficacy of our approach in solving such tasks. Moreover, given that our approach can be applied to numeric planning without any change, we also include numeric planning domains in our analysis, also obtaining positive results.