Interval-Based Relaxation for General Numeric Planning
Enrico Scala, Patrik Haslum, Sylvie Thiébaux, Ramirez Miquel · Frontiers in artificial intelligence and applications · 2016
We generalise the interval-based relaxation to sequential numeric planning problems with non-linear conditions and effects, and cyclic dependencies. This effectively removes all the limitations on the problem placed in previous work on numeric planning heuristics, and even allows us to extend the planning language with a wider set of mathematical functions. Heuristics obtained from the generalised relaxation are pruning-safe. We derive one such heuristic and use it to solve discrete-time control-like planning problems with autonomous processes. Few planners can solve such problems, and search with our new heuristic compares favourably with them.