Initial Condition Retrieving for Hybrid and Numeric Planning Problems

Abstract

Real-world applications of planning techniques often deal with dynamic and noisy environments, where sensor readings are often inaccurate, 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 tasks 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 task 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 three tasks: (i) retrieving any valid initial condition, (ii) fixing only some desired initial values and retrieving a complete initial condition that fills in the unassigned values, or (iii) retrieving the closest achievable initial condition to a fully specified one from which the goal cannot be reached. 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 extend our analysis to numeric domains, where we obtain positive results.