In this publication, a framework for parallel planning agents is developed and applied to planning problems ranging from domain independent planning to planning for autonomous vehicle systems. This general-purpose framework combines both logic-based and cost-based planning approaches in order to allow for the creation of logic-constrained, cost optimal plans with respect to possibly dynamic environments, user objectives, and constraints while providing the ability to construct “any-time” plans in partitioned problem spaces. Experiments are performed in rule-based and cost-based planning domains with examples from the literature such as the towers of Hanoi, and domain independent planning as well as current state-of-the-art real-time autonomous vehicle applications such as on- and off-road driving. These experimental results show that this incremental technique produces smaller planning graphs with higher quality planning results than standard graph-based planning techniques. These improvements are the result of the incremental graph construction approach and the application of hard and soft constraints that are also shown to elicit a variety of different driving behaviors in the autonomous vehicle domain and different problem solving approaches in the towers of Hanoi domain.
