Abstract
This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema.
Original language | English |
---|---|
Title of host publication | Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling |
Subtitle of host publication | (ICAPS 2020) |
Editors | J. Christopher Beck, Olivier Buffet, Jörg Hoffmann, Erez Karpas, Shirin Sohrabi |
Publisher | AAAI press |
Pages | 569-573 |
Number of pages | 5 |
Volume | 30 |
ISBN (Print) | 9781577358244 |
DOIs | |
Publication status | Published - 29 May 2020 |
Event | 30th International Conference on Automated Planning and Scheduling - Online, France Duration: 19 Oct 2020 → 30 Oct 2020 Conference number: 30 https://icaps20.icaps-conference.org/ |
Publication series
Name | Proceedings International Conference on Automated Planning and Scheduling, ICAPS |
---|---|
Publisher | AAAI Press |
Volume | 30 |
ISSN (Print) | 2334-0835 |
ISSN (Electronic) | 2334-0843 |
Conference
Conference | 30th International Conference on Automated Planning and Scheduling |
---|---|
Abbreviated title | ICAPS 2020 |
Country/Territory | France |
Period | 19/10/20 → 30/10/20 |
Internet address |