Natural games

@inproceedings{Anttila2011NaturalG,
  title={Natural games},
  author={Jani V Anttila and Arto Annila},
  year={2011}
}
The Fundamental Nature of Motives
  • A. Annila
  • Computer Science
    Frontiers in Neuroscience
  • 2022
TLDR
The least-time maximation of entropy, equivalent to the minimization of free energy, parallels the optimization of subjective expected utility as the system attains a state of balance, and all driving forces vanish.
Physical portrayal of computational complexity
TLDR
Since the computational time needed to contract a given set is proportional to dissipation, the computational complexity class P is a proper (strict) subset of NP.
Threads of Time
The concept of time’s arrow is examined using the principle of least action as given in its original non-Abelian form. When every entity of nature is considered to be composed of quantized actions,
Natural networks as thermodynamic systems
TLDR
The analysis of evolutionary equation of motion, derived from statistical physics of open systems, reveals that evolution of natural networks is a path-dependent and nondeterministic process.
Thoughts about Thinking: Cognition According to the Second Law of Thermodynamics
TLDR
Perception, sensation and learning as well as the processes of memory, emotions and consciousness can be regarded as natural expressions of the neural network under the suzerainty of the Second Law.
On the Character of Consciousness
TLDR
This work applies statistical mechanics of open systems to describe the brain as a hierarchical system in consuming free energy in least time and ascribed to a natural process that integrates various neural networks for coherent consumption of free energy, i.e., for meaningful deeds.
Action, an Extensive Property of Self - Organizing Systems
The Principle of Least Action has evolved and established itself as the most basic law of physics. This allows us to see how this fundamental law of nature determines the development of the system
Back to optimality: a formal framework to express the dynamics of learning optimal behavior
TLDR
This paper proposes a reinforcement learning algorithm, called Value-Gradient Learning (VGL), as a computational model of behavior optimality and proves that, unlike standard models of Reinforcement Learning, Temporal Difference in particular, VGL is guaranteed to converge to optimality under certain conditions.
Principle of least action and convergence of systems towards state of closure
TLDR
It is being proposed, that the development of a system towards states of greater organization is cyclic in nature.
Epidemic as a natural process
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