Complex Logical Action-State Prediction

• Author / Creator

  Schlauwitz, Justin M.

• This thesis proposes three novel improvements to the Actor-Critic State-Action-Reward-State-Action algorithm while considering potential biologically equivalent mechanisms.
  The algorithms are optimized via a Particle Swarm Algorithm, tested on a unigram character prediction problem, and evaluated on bit-wise accuracy and character exactness.
  Some non-unique changes include, kerneling for flexibility in state encoding options, and mixing historical and predictive information into the algorithm's logical input to supplement non-observable elements. The first contribution is a more flexible delta calculation method which better emulates how neurotransmitters are released, recovered, and lost. The second contribution is w.r.t. the implementation of complex weights and states using a trigonometric interpretation, allowing the algorithm to more clearly distinguish between non-observability and non-existence. The last contribution, bounded error, restricts the maximum output magnitude of the logical predictions in a way that improves weight stability and filtration of influence from states with weak relations to the output.

• Subjects / Keywords

  • Temporal Difference Reinforcement Learning
  • SARSA
  • CLASP
  • Conventional Logic Faults When Expectation Deviates From Application
  • Complex Logic and Signed Probability
  • Ions in the Neuron Cell
  • The Generalized Sigmoid Function
  • Slacked Error
  • Quality Error

• Graduation date

  Fall 2021

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-njfw-vj27

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.