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Project Summary

A framework for learning finite automatons that perform goal-directed interaction with entities which exhibit deterministic or stochastic behavior. The learning process can be carried out in real time together with the interaction process. A basic building block for supporting state models of finite automatons is adaptive probabilistic mapping, which for an argument from its domain returns more often results that maximize or minimize values of one or more objective functions. Finite automatons can be represented by assembler programs with user-defined instructions that perform effective work. To assist in the learning of a finite automaton, a template for its state model can be provided as an assembler program with probabilistic jump instructions.

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boltzmann c framework hmm markov viterbi

In a Nutshell, QSMM...

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Languages

C
87%
Automake
7%
7 Other
6%

30 Day Summary

Feb 25 2024 — Mar 26 2024

12 Month Summary

Mar 26 2023 — Mar 26 2024
  • 22 Commits
    Down -9 (29%) from previous 12 months
  • 1 Contributors
    Down 0 (0%) from previous 12 months

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