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XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Fumito Uwano1, Will N Browne2

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Summary
This summary is machine-generated.

Robots face challenges in distinguishing states due to sequential perceptual aliasing. The new hierarchical Frames-of-References-based XCS (Hi-FoRsXCS) system improves policy learning accuracy by chaining aliased states.

Keywords:
XCSlearning classifier systemmulti-step decision makingperceptual aliasingreinforcement learning

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Cognitive Science

Background:

  • Sequential perceptual aliasing poses a significant challenge for learning agents, hindering their ability to differentiate states and make optimal decisions.
  • Current systems often struggle with effective abstraction and discrimination of observations, limiting policy learning.
  • This necessitates novel approaches to handle complex state representations in robotic systems.

Purpose of the Study:

  • To introduce new types of sequential aliasing and propose an enhanced XCS classifier system.
  • To enable learning agents to effectively manage and learn from aliased states in sequential decision-making tasks.
  • To improve the accuracy and efficiency of policy learning in the presence of perceptual aliasing.

Main Methods:

  • Introduction of new aliasing types within the sequential aliasing context.
  • Development of a hierarchical Frames-of-References-based XCS (Hi-FoRsXCS) classifier.
  • Implementation of a complete state-action map for learning.
  • Concatenation of sequences of aliased states with identical observations into a chain.

Main Results:

  • The proposed Hi-FoRsXCS system successfully chains sequences of aliased states.
  • Hi-FoRsXCS predicts associations between observations and aliased states using the ends of the state chain.
  • Experimental results show that Hi-FoRsXCS significantly outperforms existing systems in terms of accuracy.
  • The system enables optimal policy learning with a complete action map.

Conclusions:

  • Hi-FoRsXCS offers a robust solution for sequential perceptual aliasing in learning agents.
  • The chaining mechanism effectively addresses the challenge of differentiating states with similar observations.
  • The enhanced system demonstrates superior performance in policy learning accuracy compared to prior methods.
  • Further discussion on the limitations of Hi-FoRsXCS is provided.