Combining agoric and genetic methods in stochastic design (1997)
| Venue: | Proceedings of the Fifth Foresight Conference on Molecular Nanotechnology |
| Citations: | 5 - 1 self |
BibTeX
@INPROCEEDINGS{Hall97combiningagoric,
author = {J Storrs Hall and Louis Steinberg and Brian D Davison},
title = {Combining agoric and genetic methods in stochastic design},
booktitle = {Proceedings of the Fifth Foresight Conference on Molecular Nanotechnology},
year = {1997},
pages = {page}
}
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Abstract
Abstract. Molecular nanotechnology will be physically capable of producing objects whose complexity exceeds that of any currently designed artifact by several orders of magnitude. Designs which make more than non-trivial use of this capability are beyond the capabilities of human designers, even using current CAD methods. Thus automatic design, or at least an improvement in automation over existing practice, will be a crucial component of molecular manufacturing. Central problems in automatic design include the allocation of scarce resources in the design (e.g. power and materials budgets), managing tradeoffs between conflicting design goals, and control of the overall design process itself and the simulations that it entails. The present effort is an investigation of a mixed-paradigm control model, drawing from evolution (the ‘genetic algorithm’) and economics (‘agoric algorithms’). We show that this model is a promising formulation for the general control and integration task. We present experimental results in which it performs certain desirable control tasks, including rational allocation of effort in stochastic methods, coordinating local expertise into an overall structure
Keyphrases
stochastic design genetic method general control central problem material budget present effort agoric algorithm certain desirable control task design goal automatic design scarce resource overall structure crucial component present experimental result local expertise thus automatic design several order molecular nanotechnology current cad method genetic algorithm promising formulation molecular manufacturing stochastic method rational allocation human designer integration task non-trivial use overall design process mixed-paradigm control model
