Scaling up Self-Explanatory Simulators: Polynomial-time Compilation
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Qualitative Reasoning and Diagnosis. Pages 1798-1804.
Abstract
Self-explanatory simulators have many potential applications, including supporting engineering activities, intelligent tutoring systems, and computer-based training systems. To fully realize this potential requires improving the technology to efficiently generate highly optimized simulators. This paper describes an algorithm for compiling selfexplanatory simulators that operates in polynomial time. It is capable of constructing self-explanatory simulators with thousands of parameters, which is an order of magnitude more complex than any previous technique. The algorithm is fully implemented, and we show evidence that suggests its performance is quadratic in the size of the system being simulated. We also analyze the tradeoffs between compilers and interpreters for self-explanatory simulation in terms of application-imposed constraints, and discuss plans for applications.
@inproceedings{ijcai1995p231,
title = {Scaling up Self-Explanatory Simulators: Polynomial-time Compilation},
author = {Forbus, Kenneth D. and Falkenhainer, Brian},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1798--1804},
year = {1995},
note = {Qualitative Reasoning and Diagnosis},
url = {https://ijcai.org/proceedings/1995/231},
}
TY - CPAPER TI - Scaling up Self-Explanatory Simulators: Polynomial-time Compilation AU - Forbus, Kenneth D. AU - Falkenhainer, Brian T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1798 EP - 1804 UR - https://ijcai.org/proceedings/1995/231 ER -Copied
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