International Joint Conferences on Artificial Intelligence (IJCAI)
Abstract
Non-linear continuous change is common in realworld
problems, especially those that model physical
systems. We present an algorithm which builds
upon existent temporal planning techniques based
on linear programming to approximate non-linear
continuous monotonic functions. These are integrated
through a semantic attachment mechanism,
allowing external libraries or functions that are difficult
to model in native PDDL to be evaluated during
the planning process. A new planning system
implementing this algorithm was developed and
evaluated. Results show that the addition of this
algorithm to the planning process can enable it to
solve a broader set of planning problems.peer-reviewe
