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Automated assembly as situated development: using artificial ontogenies to evolve buildable 3-D objects
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Proceedings of the 2005 conference on Genetic and evolutionary computation table of contents
Washington DC, USA
SESSION: Artificial life, evolutionary robotics, and adaptive behavior table of contents
Pages: 99 - 106  
Year of Publication: 2005
ISBN:1-59593-010-8
Authors
John Rieffel  Brandeis University, Waltham, MA
Jordan Pollack  Brandeis University, Waltham, MA
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Artificial Ontogenies, which are inspired by biological development, have been used to automatically generate a wide array of novel objects, some of which have recently been manufactured in the real world. The majority of these evolved designs have been evaluated in simulation as completed objects, with no attention paid to how, or even if, they can be realistically built. As a consequence, significant human effort is required to transfer the designs to the real world. One way to reduce human involvement in this regard is to evolve how to build rather than what to build, by using prescriptive rather than descriptive representations. In the context of Artificial Ontogenies, this requires what we call Situated Development, in which an object's development occurs in the same environment as its final evaluation. Not only does this produce sufficient information on how to build evolved designs, but it also ensures that only buildable designs are evolved. In this paper we explore the consequences of Situated Development, and demonstrate how it can be incorporated into Artificial Ontogenies in order to generate buildable objects, which can be sequentially assembled in a realistic 3-D physics environment.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
John Rieffel: colleagues
Jordan Pollack: colleagues