ML Anthology

Learning and Abstraction in Simulation

Sarah E. Goldin, Philip Klahr
IJCAI 1981 pp. 212-214
/ijcai/1981/goldin1981ijcai-learning/

Abstract

Complex s imula t ion programs t y p i c a l l y require large amounts of computation to produce h igh ly de ta i l ed output d i f f i c u l t fo r users to understand. Bu i ld ing abstracted s imula t ion systems tha t s imp l i f y both computation and output can make s imula t ion both more economical and more i n t e l l i g i-b l e. Ve describe an approach to abstracted Simula* t i o n tha t uses a scenario network to represent t yp-i c a l sequences of events in the s imula t ion domain. Abstract s imula t ion output is generated by proba-

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Text

Goldin and Klahr. "Learning and Abstraction in Simulation." International Joint Conference on Artificial Intelligence, 1981.

Markdown

[Goldin and Klahr. "Learning and Abstraction in Simulation." International Joint Conference on Artificial Intelligence, 1981.](https://mlanthology.org/ijcai/1981/goldin1981ijcai-learning/)

BibTeX

@inproceedings{goldin1981ijcai-learning,
  title     = {{Learning and Abstraction in Simulation}},
  author    = {Goldin, Sarah E. and Klahr, Philip},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1981},
  pages     = {212-214},
  url       = {https://mlanthology.org/ijcai/1981/goldin1981ijcai-learning/}
}