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Related Experiment Video

Updated: May 29, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Automata in random environments with application to machine intelligence.

E J Wegman1, J Gould

  • 1SENIOR MEMBER, IEEE, Statistics and Probability Program, Office of Naval Research, Arlington, VA 22217.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study models computers and brains using automata, finding that probabilistic automata are more powerful. Environmental interactions reveal mathematical limits for machine intelligence, particularly in random environments.

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Last Updated: May 29, 2026

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Area of Science:

  • Theoretical computer science
  • Computational neuroscience
  • Artificial intelligence

Background:

  • Computers are modeled by finite automata, while brains are modeled by probabilistic automata.
  • Probabilistic automata possess greater computational power than finite automata.
  • Environmental influences impact the behavior of both computational systems and biological brains.

Purpose of the Study:

  • To model automata within an environmental context.
  • To investigate the role of environmental models in computational systems.
  • To explore the mathematical limitations of machine intelligence.

Main Methods:

  • Formalizing automata within an environmental framework.
  • Proving that infinite sets constitute effective environmental models (Theorem 1).
  • Analyzing automaton behavior in random environments using probabilistic structures (Theorem 2).

Main Results:

  • Theorem 1 establishes infinite sets as necessary for useful environmental models.
  • Theorem 2 compares finite and probabilistic automata in random environments.
  • The study provides insights into the mathematical boundaries of machine intelligence.

Conclusions:

  • Environmental modeling is crucial for understanding automata behavior.
  • Probabilistic automata offer a more powerful framework for modeling complex systems.
  • The research highlights inherent mathematical limitations in achieving artificial general intelligence.