Biologically-Inspired Robust Spatial Programming
Inspired by the robustness and flexibility of biological systems, we are developing linguistic and programming tools to allow us to program spatial systems populated by vast numbers of unreliable components interconnected in unknown, irregular, and time-varying ways. We organize our computations aro...
| Main Authors: | , |
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| Language: | en_US |
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2005
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/30516 |
| _version_ | 1826194451150667776 |
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| author | Beal, Jacob Sussman, Gerald |
| author_facet | Beal, Jacob Sussman, Gerald |
| author_sort | Beal, Jacob |
| collection | MIT |
| description | Inspired by the robustness and flexibility of biological systems, we are developing linguistic and programming tools to allow us to program spatial systems populated by vast numbers of unreliable components interconnected in unknown, irregular, and time-varying ways. We organize our computations around geometry, making the fact that our system is made up of discrete individuals implicit. Geometry allows us to specify requirements in terms of the behavior of the space occupied by the aggregate rather than the behavior of individuals, thereby decreasing complexity. So we describe the behavior of space explicitly, abstracting away the discrete nature of the components. As an example, we present the Amorphous Medium Language, which describes behavior in terms of homeostatic maintenance of constraints on nested regions of space. |
| first_indexed | 2024-09-23T09:56:18Z |
| id | mit-1721.1/30516 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:56:18Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | mit-1721.1/305162019-04-12T08:37:39Z Biologically-Inspired Robust Spatial Programming Beal, Jacob Sussman, Gerald AI amorphous robust biological spatial sensor networks language programming medium Inspired by the robustness and flexibility of biological systems, we are developing linguistic and programming tools to allow us to program spatial systems populated by vast numbers of unreliable components interconnected in unknown, irregular, and time-varying ways. We organize our computations around geometry, making the fact that our system is made up of discrete individuals implicit. Geometry allows us to specify requirements in terms of the behavior of the space occupied by the aggregate rather than the behavior of individuals, thereby decreasing complexity. So we describe the behavior of space explicitly, abstracting away the discrete nature of the components. As an example, we present the Amorphous Medium Language, which describes behavior in terms of homeostatic maintenance of constraints on nested regions of space. 2005-12-22T02:20:32Z 2005-12-22T02:20:32Z 2005-01-18 MIT-CSAIL-TR-2005-003 AIM-2005-001 http://hdl.handle.net/1721.1/30516 en_US Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 17 p. 42144634 bytes 6024067 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI amorphous robust biological spatial sensor networks language programming medium Beal, Jacob Sussman, Gerald Biologically-Inspired Robust Spatial Programming |
| title | Biologically-Inspired Robust Spatial Programming |
| title_full | Biologically-Inspired Robust Spatial Programming |
| title_fullStr | Biologically-Inspired Robust Spatial Programming |
| title_full_unstemmed | Biologically-Inspired Robust Spatial Programming |
| title_short | Biologically-Inspired Robust Spatial Programming |
| title_sort | biologically inspired robust spatial programming |
| topic | AI amorphous robust biological spatial sensor networks language programming medium |
| url | http://hdl.handle.net/1721.1/30516 |
| work_keys_str_mv | AT bealjacob biologicallyinspiredrobustspatialprogramming AT sussmangerald biologicallyinspiredrobustspatialprogramming |