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The STAR automaton: expediency and optimality properties.

A A Economides¹, A Kehagias

¹Dept. of Econ., Univ. of Macedonia, Thessaloniki, Greece.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This summary is machine-generated.

We introduce the STack ARchitecture (STAR) automaton, a novel learning system. STAR demonstrates strong performance in stationary and non-stationary environments, outperforming existing automata.

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Area of Science:

Artificial Intelligence
Machine Learning
Automata Theory

Background:

Learning automata are adaptive systems used in decision-making and control.
Existing automata models often struggle with non-stationary environments.

Purpose of the Study:

Introduce the STack ARchitecture (STAR) automaton.
Evaluate STAR's performance in stationary and non-stationary environments.
Compare STAR against classical variable structure automata.

Main Methods:

Developed a novel automaton architecture with a star-shaped state transition diagram.
Utilized a stack-like operation within automaton branches for learning.
Conducted mathematical analysis for optimality in stationary environments.
Performed numerical simulations for performance evaluation in non-stationary environments.

Main Results:

STAR can achieve ε-optimality in stationary environments with deterministic reward/probabilistic penalty and sufficient states (D).
STAR generally outperforms classical automata (L/sub R-P/, L/sub R-I/, L/sub R-/spl epsi/P/) in non-stationary, switching environments.
The learning parameter D influences STAR's performance and adaptability.

Conclusions:

The STAR automaton offers a robust and adaptable learning framework.
STAR presents a significant advancement over traditional automata, particularly in dynamic environments.
Further research can explore variations and applications of the STAR architecture.