ASAP: An Automatic Algorithm Selection Approach for Planning

Abstract

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements.In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings-planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.

Inner entanglements: Narrowing the search in classical planning by problem reformulation

“…In planning, some objects might be symmetric, which can be exploited for avoiding alternatives concerning one object that has been already tried with the object's symmetric "twin." 38 A recent work, which is, to some extent, similar to inner entanglements, proposes a method to learn "bad" causal links in order to generate plans of better quality. This approach has been recently extended for cost-optimal planning.…”

“…Pruning techniques are often an inseparable part of advanced planning engines. 38 Exploiting a natural property of inner entanglements, ie, exclusivity of predicate achievement or requirement, has been also used for generating macros. We demonstrated that inner entanglements can often significantly improve their performance.…”

“…Outer and inner entanglements are complementary as has already been demonstrated in the literature. 38 A recent work, which is, to some extent, similar to inner entanglements, proposes a method to learn "bad" causal links in order to generate plans of better quality. 39 In contrast to this work, inner entanglements aim to capture possibly "good" causal links that are enforced in the planning process.…”

“…A recent work introducing ASAP, which is a planner based on the algorithm selection approach that selects the best couple (planner,encoding) for a given domain, has shown that inner entanglements and the combination of outer and inner entanglements often outperformed macros. 38 Exploiting a natural property of inner entanglements, ie, exclusivity of predicate achievement or requirement, has been also used for generating macros. 28 Such macros can be, in some cases, beneficial; however, such an approach cannot be used in cases where operators in an inner-entanglement relation cannot be applied consecutively.…”

Portfolio Methods for Optimal Planning: An Empirical Analysis

“…There are planner portfolio configuration systems mainly designed to generate domain-specific planners, such as PbP and ASAP [5,6], as well as a range of domain-independent portfolio planners. Among the latter, we can identify two main classes: static portfolios, which run the same schedule of planners on every given problem instance, and portfolios based on per-instance planner schedules.…”