On-the-Fly Knowledge Acquisition for Automated Planning Applications: Challenges and Lessons Learnt

Saumya Bhatnagar, Sumit Mund, Enrico Scala, Keith McCabe, Lee McCluskey, Mauro Vallati

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

5 Citations (Scopus)

Abstract

Automated planning is a prominent AI challenge, and it is now exploited in a range of real-world applications. There are three crucial aspects of automated planning: the planning engine, the domain model, and the problem instance. While the planning engine and the domain model can be engineered and optimised offline, in many applications there is the need to generate problem instances on the fly. In this paper we focus on the challenges of on-the-fly knowledge acquisition for complex and variegated problem instances. We consider as a case study the application of planning to urban traffic control and we describe the designed and developed knowledge acquisition process. This allows us to discuss a range of lessons learned from the experience, and to point to important lines of research for support the knowledge acquisition process for automated planning applications.
Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Agents and Artificial Intelligence, ICAART 2022
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages387-397
Number of pages11
Volume2 of 3
ISBN (Print)9789897585470
DOIs
Publication statusPublished - 3 Feb 2022
Event14th International Conference on Agents and Artificial Intelligence - Online Streaming, Virtual
Duration: 3 Feb 20225 Feb 2022
Conference number: 14
https://icaart.scitevents.org/

Publication series

NameInternational Conference on Agents and Artificial Intelligence
PublisherSciTePress
ISSN (Print)2184-433X

Conference

Conference14th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2022
CityVirtual
Period3/02/225/02/22
Internet address

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