4.4 Article

Investigating Biological Assumptions through Radical Reimplementation

期刊

ARTIFICIAL LIFE
卷 21, 期 1, 页码 21-46

出版社

MIT PRESS
DOI: 10.1162/ARTL_a_00150

关键词

Artificial life; biological relevance; evolution; development; artificial intelligence

资金

  1. DARPA
  2. ARO through DARPA [N11AP20003]
  3. US Army Research Office [W911NF-11-1-0489]

向作者/读者索取更多资源

An important goal in both artificial life and biology is uncovering the most general principles underlying life, which might catalyze both our understanding of life and engineering lifelike machines. While many such general principles have been hypothesized, conclusively testing them is difficult because life on Earth provides only a singular example from which to infer. To circumvent this limitation, this article formalizes an approach called radical reimplementation. The idea is to investigate an abstract biological hypothesis by intentionally reimplementing its main principles to diverge maximally from existing natural examples. If the reimplementation successfully exhibits properties resembling biology, it may support the underlying hypothesis better than an alternative example inspired more directly by nature. The approach thereby provides a principled alternative to a common tradition of defending and minimizing deviations from nature in artificial life. This work reviews examples that can be interpreted through the lens of radical reimplementation to yield potential insights into biology despite having purposely unnatural experimental setups. In this way, radical reimplementation can help renew the relevance of computational systems for investigating biological theory and can act as a practical philosophical tool to help separate the fundamental features of terrestrial biology from the epiphenomenal.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Computer Science, Artificial Intelligence

Petalz: Search-Based Procedural Content Generation for the Casual Gamer

Sebastian Risi, Joel Lehman, David B. D'Ambrosio, Ryan Hall, Kenneth O. Stanley

IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES (2016)

Article Multidisciplinary Sciences

Extinction Events Can Accelerate Evolution

Joel Lehman, Risto Miikkulainen

PLOS ONE (2015)

Article Computer Science, Artificial Intelligence

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Nick Cheney, Patryk Chrabaszcz, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frenoy, Christian Gagn, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, Francois Taddei, Danesh Tarapore, Simon Thibault, Richard Watson, Westley Weimer, Jason Yosinski

ARTIFICIAL LIFE (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Reinforcement Learning Under Moral Uncertainty

Adrien Ecoffet, Joel Lehman

Summary: The ambitious goal of machine learning is to create agents that behave ethically, expanding the context in which autonomous agents can be practically deployed. However, there is widespread disagreement about the nature of morality, leading to the proposal that ethical behavior requires acting under moral uncertainty. This paper translates insights from moral philosophy to reinforcement learning, proposing training methods that address competing desiderata and highlighting potential technical complications in grounding moral philosophy in RL.

INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Learning Belief Representations for Imitation Learning in POMDPs

Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng

35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

POET: Open-Ended Coevolution of Environments and their Optimized Solutions

Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley

PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'19) (2019)

Article Computer Science, Interdisciplinary Applications

Tradeoffs in Neuroevolutionary Learning-Based Real-Time Robotic Task Design in the Imprecise Computation Framework

Pd-Chi Huang, Luis Sentis, Joel Lehman, Chien-Liang Fok, Aloysius K. Mok, Risto Miikkulainen

ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS (2019)

Proceedings Paper Computer Science, Artificial Intelligence

The Surprising Creativity of Digital Evolution

Joel Lehman, Jeff Clune, Dusan Misevic

2018 CONFERENCE ON ARTIFICIAL LIFE (ALIFE 2018) (2018)

Proceedings Paper Computer Science, Artificial Intelligence

On the Potential Benefits of Knowing Everything

Joel Lehman, Kenneth O. Stanley

2018 CONFERENCE ON ARTIFICIAL LIFE (ALIFE 2018) (2018)

Proceedings Paper Computer Science, Theory & Methods

Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It

Henok Mengistu, Joel Lehman, Jeff Clune

GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2016)

Article Robotics

On the Critical Role of Divergent Selection in Evolvability

Joel Lehman, Bryan Wilder, Kenneth O. Stanley

FRONTIERS IN ROBOTICS AND AI (2016)

Proceedings Paper Computer Science, Theory & Methods

Automatic Evolution of Multimodal Behavior with Multi-Brain HyperNEAT

Jacob Schrum, Joel Lehman, Sebastian Risi

PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION) (2016)

Proceedings Paper Computer Science, Theory & Methods

Tradeoffs in Real-Time Robotic Task Design with Neuroevolution Learning for Imprecise Computation

Pei-Chi Huang, Luis Sentis, Joel Lehman, Chien-Liang Fok, Aloysius K. Mok, Risto Miikkulainen

2015 IEEE 36TH REAL-TIME SYSTEMS SYMPOSIUM (RTSS 2015) (2015)

Proceedings Paper Computer Science, Artificial Intelligence

Enhancing Divergent Search through Extinction Events

Joel Lehman, Risto Miikkulainen

GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (2015)

暂无数据