Planning through Automatic Portfolio Configuration: The PbP Approach

In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbP�s behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions

Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning

Case-Based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is extremely effective when similar reuse candidates can be efficiently chosen. In this paper, we study an innovative technique based on planning problem features for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. A problem feature is a characteristic of the instance that can be automatically derived from the problem specification, domain and search space analyses, and different problem encodings. Since the use of existing planning features are not always able to effectively distinguish between problems within the same planning domain, we introduce a new class of features. An experimental analysis in this paper shows that our features-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system

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 (domain- independent) 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 experimental 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

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

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques

PbP2: Automatic Configuration of a Portfolio-based Multi-Planner

We present PbP2, an automated system that generates efficient domain-specific multi planners from a portfolio of domain-independent planning techniques by (i) computing some sets of macro-actions for every planner in the portfolio, (ii) optimizing the parameter setting of the parameterized planners in the portfolio, (iii) selecting a promising combination of planners in the portfolio and relative useful macro-actions, and (iv) defining some running time slots for their round-robin scheduling during planning. The configuration of the portfolio yielding the multi planner relies on some knowledge about the performance of the planners and relative macro actions, which is automatically generated from a training problem set. PbP2 is a revision and extension of a preliminary version of this system (PbP) that was awarded at the learning track of IPC-2008

Generating Domain-Specific Planners through Automatic Parameter Configuration in LPG

The ParLPG planning system is based on the idea of using a generic algorithm configuration procedure – here, the well-known ParamILS framework – to optimise the performance of a highly parametric planner on a set of problem instances representative of a specific planning domain. This idea is applied to LPG, a versatile and efficient planner based on stochastic local-search with 62 parameters and over 6.5 × 10^17 possible configurations. A recent, large-scale empirical investigation showed that the approach behind ParLPG yields substantial performance improvements across a broad range of planning domains