| 项目编号: | 1344227
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| 项目名称: | INSPIRE Track 1: Is Evolvability Driven By Emergent Modularity? Biomimetic robots, gene inspired information structures, and the evolvability of intelligent agents |
| 作者: | Kenneth Livingston
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| 承担单位: | Vassar College
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| 批准年: | 2013
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| 开始日期: | 2014-01-01
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| 结束日期: | 2018-12-31
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| 资助金额: | USD999314
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| 资助来源: | US-NSF
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| 项目类别: | Continuing grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Biological Sciences - Environmental Biology
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| 英文关键词: | robot
; evolvability
; physical robot
; robotic control
; modern genetics
; simple robot
; useful robot
; division
; robotic engineering
; information integration
; evolutionary robotic
; directorate
; information science
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| 英文摘要: | This INSPIRE award is partially funded by the Evolutionary Processes program in the Division of Environmental Biology in the Directorate for Biological Sciences, the Behavioral Systems program in the Division of Integrative Organismal Systems in the Directorate for Biological Sciences, and the Information Integration and Informatics program in the Division of Information & Intelligent Systems in the Directorate for Computer & Information Science & Engineering.
For millennia, humans have bred organisms to produce better food, clothes, and companionship. Recently, scientists have learned how to breed robots, evolving simulated creatures in virtual worlds, or physical robots in the real world. By combining the evolutionary process with robotic engineering, more complex and novel designs should be possible compared to traditional methods. In spite of the promise, so far evolved robots only do simple things like walk, navigate, or pick up objects. What limits progress is a lack of understanding of "evolvability," the capacity of organisms (or robots) to change and become more complex. Understanding evolvability is the main goal of this project: researchers will borrow ideas from modern genetics so their robots mutate and develop in ways that are similar to how biological creatures do. In theory, this could produce simple robots that evolve into ever more complex, capable and useful robots.
Understanding how complexity evolves is central to the study of life, and may enable even non-specialists to automatically and continuously produce diverse kinds of machines. By linking complexity, genetics, and evolution, this project seeks to discover new principles that can be applied in science and industry. To help convert scientific principles into innovation drivers, online software will be created to show how to evolve virtual or physical robots; this will help students learn about engineering, biology, and how to apply both to technology. Finally, evolutionary robotics can be used to solve complex problems in robotic control that defy logical programming solutions, so this research can help companies that manufacture robots. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/97526
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Kenneth Livingston. INSPIRE Track 1: Is Evolvability Driven By Emergent Modularity? Biomimetic robots, gene inspired information structures, and the evolvability of intelligent agents. 2013-01-01.
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