Signal-to-Symbol Transformation: HASP/SIAP Case Study

Authors

  • H. Penny Nii
  • Edward A. Feigenbaum
  • John J. Anton

DOI:

https://doi.org/10.1609/aimag.v3i2.368

Abstract

Artificial intelligence is that part of computer science that concerns itself with the concepts and methods of symbolic inference and symbolic representation of knowledge. Its point of departure -- it's most fundamental concept -- is what Newell and Simon called (in their Turing Award Lecture) "the physical symbol system." But within the last fifteen years, it has concerned itself also with signals -- with the interpretation or understanding of signal data. AI researchers have discussed "signal-to symbol transformations," and their programs have shown how appropriate use of symbolic manipulations can be of great use in making signal processing more effective and efficient. Indeed, the programs for signal understanding have been fruitful, powerful, and among the most widely recognized of AI's achievements.

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Published

1982-06-15

How to Cite

Nii, H. P., Feigenbaum, E. A., & Anton, J. J. (1982). Signal-to-Symbol Transformation: HASP/SIAP Case Study. AI Magazine, 3(2), 23. https://doi.org/10.1609/aimag.v3i2.368

Issue

Section

Articles