Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation

Efficient Plan Adaptation through Replanning Windows and Heuristic Goals

Fast plan adaptation is important in many AI-applications. From a theoretical point of view, in the worst case adapting an existing plan to solve a new problem is no more efficient than a complete regeneration of the plan. However, in practice plan adaptation can be much more efficient than plan generation, especially when the adapted plan can be obtained by performing a limited amount of changes to the original plan. In this paper, we investigate a domain-independent method for plan adaptation that modifies the original plan by replanning within limited temporal windows containing portions of the plan that need to be revised. Each window is associated with a particular replanning subproblem that contains some “heuristic goals ” facilitating the plan adaptation, and that can be solved using different planning methods. An experimental analysis shows that, in practice, adapting a given plan for solving a new problem using our techniques can be much more efficient than replanning from scratch.

On Compiling Away PDDL3 Soft Trajectory Constraints without Using Automata

We address the problem of propositional planning extended with the class of soft temporally extended goals supported in PDDL3, also called qualitative preferences since IPC-5. Such preferences are useful to characterise plan quality by allowing the user to express certain soft constraints on the state trajectory of the desired solution plans. We propose and evaluate a compilation approach that extends previous work on compiling soft reachability goals and always goals to the full set of PDDL3 qualitative preferences. This approach directly compiles qualitative preferences into propositional planning without using automata to represent the trajectory constraints. Moreover, since no numeric fluent is used, it allows many existing STRIPS planners to immediately address planning with preferences without changing their algorithms or code. An experimental analysis presented in the paper evaluates the performance of state-of-the-art propositional planners using our compilation of qualitative preferences. The results indicate that the proposed approach is highly competitive with respect to current planners that natively support the considered class of preferences, as well as with a recent automata-based compilation approach

An approach to efficient planning with numerical fluents and multi-criteria plan quality

Dealing with numerical information is practically important in many real-world planning domains where the executability of an action can depend on certain numerical conditions, and the action effects can consume or renew some critical continuous resources, which in PDDL can be represented by numerical fluents. When a planning problem involves numerical fluents, the quality of the solutions can be expressed by an objective function that can take different plan quality criteria into account. We propose an incremental approach to automated planning with numerical fluents and multi-criteria objective functions for PDDL numerical planning problems. The techniques in this paper significantly extend the framework of planning with action graphs and local search implemented in the LPG planner. We define the numerical action graph (NA-graph) representation for numerical plans and we propose some new local search techniques using this representation, including a heuristic search neighborhood for NA-graphs, a heuristic evaluation function based on relaxed numerical plans, and an incremental method for plan quality optimization based on particular search restarts. Moreover, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach, and at analyzing its effectiveness in terms of fast computation of a valid plan and quality of the best plan that can be generated within a given CPU-time limit. Overall, the results show that our planner performs quite well compared to other state-of-the-art planners handling numerical fluents