Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning

Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances. When the objective of the optimization is some scalar quality of the solution given by the target algorithm, this quality is also used as the basis for the quality of parameter sets. But in the case of multi-objective optimization by aggregation, the set of solutions is given by several single-objective runs with different weights on the objectives, and it turns out that the hypervolume of the final population of each single-objective run might be a better indicator of the global performance of the aggregation method than the best fitness in its population. This paper discusses this issue on a case study in multi-objective temporal planning using the evolutionary planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how ParamILS makes a difference between both approaches, and demonstrate that indeed, in this context, using the hypervolume indicator as ParamILS target is the best choice. Other issues pertaining to parameter tuning in the proposed context are also discussed.Comment: arXiv admin note: substantial text overlap with arXiv:1305.116

Partial Order Temporal Plan Merging for Mobile Robot Tasks

For many mobile service robot applications, planning problems are based on deciding how and when to navigate to certain locations and execute certain tasks. Typically, many of these tasks are independent from one another, and the main objective is to obtain plans that efficiently take into account where these tasks can be executed and when execution is allowed. In this paper, we present an approach, based on merging of partial order plans with durative actions, that can quickly and effectively generate a plan for a set of independent goals. This plan exploits some of the synergies of the plans for each single task, such as common locations where certain actions should be executed. We evaluate our approach in benchmarking domains, comparing it with state-of-the-art planners and showing how it provides a good trade-off between the approach of sequencing the plans for each task (which is fast but produces poor results), and the approach of planning for a conjunction of all the goals (which is slow but produces good results)

An Evolutionary Metaheuristic Based on State Decomposition for Domain-Independent Satisficing Planning

DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by embedding the domain-independent satisficing YAHSP planner and using the critical path h1 heuristic. Experiments with the resulting algorithm are performed on a selection of IPC benchmarks from classical, cost-based and temporal domains. Under the experimental conditions of the IPC, and in particular with a universal parameter setting common to all domains, DAEYAHSP is compared to the best planner for each type of domain. Results show that DAEYAHSP performs very well both on coverage and quality metrics. It is particularly noticeable that DAEX improves a lot on plan quality when compared to YAHSP, which is known to provide largely suboptimal solutions; making it competitive with state-of-the-art planners. This article gives a full account of the algorithm, reports on the experiments and provides some insights on the algorithm behavior

Décomposition des problèmes de planification de tâches basée sur les landmarks

The algorithms allowing on-the-fly computation of efficient strategies solving a heterogeneous set of problems has always been one of the greatest challenges faced by research in Artificial Intelligence. To this end, classical planning provides to a system reasoning capacities, in order to help it to interact with its environment autonomously. Given a description of the world current state, the actions the system is able to perform, and the goal it is supposed to reach, a planner can compute an action sequence yielding a state satisfying the predefined goal. The planning problem is usually intractable (PSPACE-hard), however some properties of the problems can be automatically extracted allowing the design of efficient solvers.Firstly, we have developed the Landmark-based Meta Best-First Search (LMBFS) algorithm. Unlike state-of-the-art planners, usually based on state-space heuristic search, LMBFS reenacts landmark-based planning problem decomposition. A landmark is a fluent appearing in each and every solution plan. The LMBFS algorithm splits the global problem in a set of subproblems and tries to find a global solution using the solutions found for these subproblems. Secondly, we have adapted classical planning techniques to enhance the performance of our base algorithm, making LMBFS a competitive planner. Finally, we have tested and compared these methods.Les algorithmes permettant la création de stratégies efficaces pour la résolution d’ensemble de problèmes hétéroclites ont toujours été un des piliers de la recherche en Intelligence Artificielle. Dans cette optique, la planification de tâches a pour objectif de fournir à un système la capacité de raisonner pour interagir avec son environnement de façon autonome afin d’atteindre les buts qui lui ont été assignés. À partir d’une description de l’état initial du monde, des actions que le système peut exécuter, et des buts qu’il doit atteindre, un planificateur calcule une séquence d’actions dont l’exécution permet de faire passer l’état du monde dans lequel évolue le système vers un état qui satisfait les buts qu’on lui a fixés. Le problème de planification est en général difficile à résoudre (PSPACE-difficile), cependant certaines propriétés des problèmes peuvent être automatiquement extraites permettant ainsi une résolution efficace.Dans un premier temps, nous avons développé l’algorithme LMBFS (Landmarkbased Meta Best-First Search). À contre-courant des planificateurs state-of-the-art, basés sur la recherche heuristique dans l’espace d’états, LMBFS est un algorithme qui réactualise la technique de décomposition des problèmes de planification basés sur les landmarks. Un landmark est un fluent qui doit être vrai à un certain moment durant l’exécution de n’importe quel plan solution. L’algorithme LMBFS découpe le problème principal en un ensemble de sous-problèmes et essaie de trouver une solution globale grâce aux solutions trouvées pour ces sous-problèmes. Dans un second temps, nous avons adapté un ensemble de techniques pour améliorer les performances de l’algorithme. Enfin, nous avons testé et comparé chacune de ces méthodes permettant ainsi la création d’un planificateur efficace