Omniscient Debugging for Cognitive Agent Programs

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)

Abstract

For real-time programs reproducing a bug by rerunning the system is likely to fail, making fault localization a time-consuming process. Omniscient debugging is a technique that stores each run in such a way that it supports going backwards in time. However, the overhead of existing omniscient debugging implementations for languages like Java is so large that it cannot be effectively used in practice. In this paper, we show that for agent-oriented programming practical omniscient debugging is possible. We design a tracing mechanism for efficiently storing and exploring agent program runs. We are the first to demonstrate that this mechanism does not affect program runs by empirically establishing that the same tests succeed or fail. Usability is supported by a trace visualization method aimed at more effectively locating faults in agent programs.

Original languageEnglish
Title of host publicationProceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
Pages265-272
Number of pages8
ISBN (Electronic)978-0-9992411-0-3
DOIs
Publication statusPublished - 2017
EventIJCAI 2017: 26th International Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
Conference number: 26
http://ijcai-17.org/

Conference

ConferenceIJCAI 2017
Abbreviated titleIJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17
Internet address

Keywords

  • Agent-based and Multi-agent Systems
  • Multidisciplinary Topics and Applications

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