Matching State-Based Sequences with Rich Temporal Aspects

Authors

  • Aihua Zheng Anhui University
  • Jixin Ma University of Greenwich
  • Jin Tang Anhui University
  • Bin Luo Anhui University

DOI:

https://doi.org/10.1609/aaai.v26i1.8413

Abstract

A General Similarity Measurement (GSM), which takes into account of both non-temporal and rich temporal aspects including temporal order, temporal duration and temporal gap, is proposed for state-sequence matching. It is believed to be versatile enough to subsume representative existing measurements as its special cases.

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Published

2021-09-20

How to Cite

Zheng, A., Ma, J., Tang, J., & Luo, B. (2021). Matching State-Based Sequences with Rich Temporal Aspects. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 2463-2464. https://doi.org/10.1609/aaai.v26i1.8413

Issue

Vol. 26 No. 1 (2012): Twenty-Sixth AAAI Conference on Artificial Intelligence

Section

Student Abstract Track