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Found 11 papers, 2 papers with code
Detecting Information Relays in Deep Neural Networks
Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts.
Incentivising cooperation by rewarding the weakest member
Autonomous agents that act with each other on behalf of humans are becoming more common in many social domains, such as customer service, transportation, and health care.
The structure of evolved representations across different substrates for artificial intelligence
Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection.
The Role of Conditional Independence in the Evolution of Intelligent Systems
When these components have multiple outputs, we intuitively assume that the outputs are causally dependent on the inputs but are themselves independent of each other given the state of their shared input.
Markov Brains: A Technical Introduction
Markov Brains are a class of evolvable artificial neural networks (ANN).
Machine Learned Learning Machines
There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning.
Exploring the coevolution of predator and prey morphology and behavior
In this model, we observe a coevolutionary cycle between prey swarming behavior and the predator's visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known "predator confusion effect."
Computational evolution of decision-making strategies
Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds.
Risk aversion as an evolutionary adaptation
We hypothesize that risk aversion in the equivalent mean payoff gamble is beneficial as an adaptation to living in small groups, and find that a preference for risk averse strategies only evolves in small populations of less than 1, 000 individuals, while agents exhibit no such strategy preference in larger populations.
Predator confusion is sufficient to evolve swarming behavior
Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
The evolution of representation in simple cognitive networks
Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information.
