Meta-level Techniques for Planning, Search, and Scheduling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Metareasoning is a core idea in AI at that captures the essence of being both human and intelligent. This idea is that much can be gained by thinking (reasoning) about one’s own thinking. In the context of search and planning, metareasoning concerns with making explicit decisions about computation steps, by comparing their ‘cost’ in computational resources, against the gain they can be expected to make towards advancing the search for solution (or plan) and thus making better decisions. To apply metareasoning, a meta-level problem needs to be defined and solved with respect to a specific framework or algorithm. In some cases, these meta-level problems can be very hard to solve. Yet, even a fast-to-compute approximation of meta-level problems can yield good results and improve the algorithms to which they are applied. This paper provides an overview of different settings in which we applied metareasoning to improve search, planning and scheduling.

Original languageAmerican English
Title of host publication14th International Symposium on Combinatorial Search, SoCS 2021
EditorsHang Ma, Ivan Serina
Pages236-238
Number of pages3
ISBN (Electronic)9781713834557
StatePublished - 1 Jan 2021
Event14th International Symposium on Combinatorial Search, SoCS 2021 - Guangzhou, Virtual, China
Duration: 26 Jul 202130 Jul 2021

Publication series

Name14th International Symposium on Combinatorial Search, SoCS 2021

Conference

Conference14th International Symposium on Combinatorial Search, SoCS 2021
Country/TerritoryChina
CityGuangzhou, Virtual
Period26/07/2130/07/21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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