A Learning Algorithm for Episodes

Tom Obry

doi:10.29007/j9d9

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A Learning Algorithm for Episodes

11 pages•Published: January 6, 2018

Tom Obry

Abstract

Sequences of events describing the behavior and actions of agents or systems can be collected in several domains. An episode is a collection of events that occur in a given partial order. By performing a recognition of recurrent episodes in several sequences and comparing them, it is pos- sible to determine a pattern common to all the se- quences. In this paper, we propose an approach to recognize episodes that are common in a set of event sequences. The method described is applied to the automotive domain for learning diagnostic procedures.

Keyphrases: algorithm, Data Mining, Episodes

In: Marina Zanella, Ingo Pill and Alessandro Cimatti (editors). 28th International Workshop on Principles of Diagnosis (DX'17), vol 4, pages 1--11

Links:	https://easychair.org/publications/paper/1vkG
	https://doi.org/10.29007/j9d9

BibTeX entry

@inproceedings{DX'17:Learning_Algorithm_for_Episodes,
  author    = {Tom Obry},
  title     = {A Learning Algorithm for Episodes},
  booktitle = {28th International Workshop on Principles of Diagnosis (DX'17)},
  editor    = {Marina Zanella and Ingo Pill and Alessandro Cimatti},
  series    = {Kalpa Publications in Computing},
  volume    = {4},
  pages     = {1--11},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {https://easychair.org/publications/paper/1vkG},
  doi       = {10.29007/j9d9}}

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