MEvo: a framework for effective macro sets evolution

Abstract

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide "general improvement", the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros-taken from a given pool-while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.

Reformulation Techniques for Automated Planning: A Systematic Review

“…PbP [Gerevini et al, 2014] uses statistical tests to identify the most promising portfolio of planning engines and macro actions to be used for solving challenging planning instance. Finally, MeVo [Vallati et al, 2020], given a large pool of macros, can evolve over time the best set of macros to be used by a planning engine for solving a continuous stream of problems from a considered domain. This features allows MeVo to overcome the issue of having training instances that are not representative of the testing ones.…”

Reformulation techniques for automated planning: a systematic review

“…Finally, MeVo (Vallati et al. , 2020), given a large pool of macros, can evolve over time the best set of macros to be used by a planning engine for solving a continuous stream of problems from a considered domain. This features allows MeVo to overcome the issue of having training instances that are not representative of the testing ones.…”

TempoRL: Learning When to Act

“…For various problem domains of AI planning, varieties of useful macro actions are known and selecting which macro actions to consider is not trivial. Vallati et al (2019) propose a macro action selection mechanism that selects which macro actions should be considered for new problems. Further, Nasiriany et al (2019) show that goal-conditioned policies learned with RL can be incorporated into planning.…”