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Sidney Pontes-Filho1,2, Kristoffer Olsen3, Anis Yazidi1,4,5

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Summary

We propose evolving low-level artificial general intelligence (AGI) by having agents learn from environmental feedback. This biologically-inspired approach successfully tackles diverse tasks, suggesting potential for more complex AI challenges.

Keywords:
Hebbian learningartificial general intelligencemeta-learningneuroevolutionspike-timing-dependent plasticityspiking neural networkweight agnostic neural network

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Evolutionary Computation

Background:

  • Intelligent behavior in nature arises from organism-environment interaction and adaptation.
  • Learning is hypothesized to occur via sensory feedback interpretation during agent action.
  • A embodied agent and a reactive environment are essential for this learning process.

Purpose of the Study:

  • To propose and evaluate a framework for low-level artificial general intelligence (AGI).
  • To investigate the evolution of biologically-inspired neural networks that learn from environmental interactions.
  • To benchmark the adaptivity and generality of evolved controllers in mutable environments.

Main Methods:

  • Neuroevolution of Artificial General Intelligence (NAGI): evolving spiking neural networks with adaptive synapses.
  • Agents with evolved controllers are instantiated in mutable environments.
  • Tasks include food foraging, logic gate emulation, and cart-pole balancing.

Main Results:

  • Successful task completion using small, evolved neural network topologies.
  • Demonstrated adaptivity and generality of the neuroevolutionary approach.
  • Validation of the hypothesis that learning occurs through interpreting sensory feedback.

Conclusions:

  • Evolving low-level AGI from basic principles is feasible.
  • The NAGI framework provides a viable method for developing adaptive and general AI controllers.
  • This approach opens avenues for tackling more complex AI tasks and curriculum learning scenarios.