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Automata with hierarchical control and evolutionary learning.

S Waner1, Y H Wu

  • 1Department of Mathematics, Hofstra University, Hempstead, NY 11550.

Bio Systems
|January 1, 1988
PubMed
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We introduce a framework for hierarchical control using automata theory, enabling a universal automaton to emulate any other automaton on a graph. This framework defines automaton depth and aids in learning natural encodings for graph-based tasks.

Area of Science:

  • Theoretical Computer Science
  • Automata Theory
  • Control Theory

Background:

  • Hierarchical control systems are crucial for complex tasks.
  • Automata theory provides formal models for computation and control.
  • Graph traversal algorithms are fundamental in computer science.

Purpose of the Study:

  • To develop an automata-theoretical framework for structured hierarchical control.
  • To introduce a universal automaton capable of emulating other automata on graphs.
  • To define and explore the concept of automaton depth based on hierarchical structure.

Main Methods:

  • Proposing a framework based on rules and meta-rules for hierarchical control.
  • Defining a universal hierarchically structured automaton (μ).

Related Experiment Videos

  • Developing input mapping for automaton emulation and encoding.
  • Analyzing efficient encodings of graph-search algorithms.

    • Main Results:

    • A universal automaton (μ) can emulate any automaton on a given graph via input encoding.
    • Efficient encodings correlate with the natural hierarchical structure of algorithms.
    • A precise notion of automaton "depth" is established.
    • Application to stochastic neural networks for learning structural encodings.

    Conclusions:

    • The proposed framework offers a formal approach to hierarchical control and automaton emulation.
    • Learned encodings reflect the inherent hierarchical structure of graph-based algorithms.
    • This work has implications for structural pattern recognition and learning systems.