Abstract In scientific computing and in realistic
graphic animation, simulation – that is, step-by-step
calculation of the complete trajectory of a physical
system – is one of the most common and important
modes of calculation. In this article, we address the
scope and limits of the use of simulation, with respect
to \AI\ tasks that involve high-level physical
reasoning. We argue that, in many cases, simulation can
play at most a limited role. Simulation is most
effective when the task is prediction, when complete
information is available, when a reasonably high
quality theory is available, and when the range of
scales involved, both temporal and spatial, is not
extreme. When these conditions do not hold, simulation
is less effective or entirely inappropriate. We discuss
twelve features of physical reasoning problems that
pose challenges for simulation-based reasoning. We
briefly survey alternative techniques for physical
reasoning that do not rely on simulation.
%0 Journal Article
%1 davis-scope-simulation-reasoning-2016
%A Davis, Ernest
%A Marcus, Gary
%D 2016
%J Artificial Intelligence
%K ai reasoning
%P 60--72
%R http://dx.doi.org/10.1016/j.artint.2015.12.003
%T The scope and limits of simulation in automated
reasoning
%U http://www.sciencedirect.com/science/article/pii/S0004370215001794
%V 233
%X Abstract In scientific computing and in realistic
graphic animation, simulation – that is, step-by-step
calculation of the complete trajectory of a physical
system – is one of the most common and important
modes of calculation. In this article, we address the
scope and limits of the use of simulation, with respect
to \AI\ tasks that involve high-level physical
reasoning. We argue that, in many cases, simulation can
play at most a limited role. Simulation is most
effective when the task is prediction, when complete
information is available, when a reasonably high
quality theory is available, and when the range of
scales involved, both temporal and spatial, is not
extreme. When these conditions do not hold, simulation
is less effective or entirely inappropriate. We discuss
twelve features of physical reasoning problems that
pose challenges for simulation-based reasoning. We
briefly survey alternative techniques for physical
reasoning that do not rely on simulation.
@article{davis-scope-simulation-reasoning-2016,
abstract = {Abstract In scientific computing and in realistic
graphic animation, simulation – that is, step-by-step
calculation of the complete trajectory of a physical
system – is one of the most common and important
modes of calculation. In this article, we address the
scope and limits of the use of simulation, with respect
to \{AI\} tasks that involve high-level physical
reasoning. We argue that, in many cases, simulation can
play at most a limited role. Simulation is most
effective when the task is prediction, when complete
information is available, when a reasonably high
quality theory is available, and when the range of
scales involved, both temporal and spatial, is not
extreme. When these conditions do not hold, simulation
is less effective or entirely inappropriate. We discuss
twelve features of physical reasoning problems that
pose challenges for simulation-based reasoning. We
briefly survey alternative techniques for physical
reasoning that do not rely on simulation.},
added-at = {2016-07-12T19:24:18.000+0200},
author = {Davis, Ernest and Marcus, Gary},
biburl = {https://www.bibsonomy.org/bibtex/24df2eb486652dc6ec860827aece64dc5/mhwombat},
doi = {http://dx.doi.org/10.1016/j.artint.2015.12.003},
interhash = {cea444a577b60dbc68d194a9c560f43a},
intrahash = {4df2eb486652dc6ec860827aece64dc5},
issn = {0004-3702},
journal = {Artificial Intelligence},
keywords = {ai reasoning},
pages = {60--72},
timestamp = {2016-07-12T19:25:30.000+0200},
title = {The scope and limits of simulation in automated
reasoning},
url = {http://www.sciencedirect.com/science/article/pii/S0004370215001794},
volume = 233,
year = 2016
}