The effect of noise on the performance of cultural evolution in multi-agent systems

Curran, Dara; O’Riordan, Colm

doi:10.1109/cec.2004.1331109

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…An experiment conducted in our previous work [21] confirmed that small levels of noise introduced into the communication process improved both the formation of a shared lexicon and agent performance.…”

Section: Cultural Learningsupporting

confidence: 70%

The Effects of Cultural Learning in Populations of Neural Networks

Curran

O’Riordan

2007

Artificial Life

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Population learning can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness and is often simulated using genetic algorithms. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher-pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. By examining the innate fitness of a population (i.e., the fitness of the population measured before any cultural learning takes place), it is possible to examine the effects of cultural learning on the population's genetic makeup. Our model explores the effect of cultural learning on a population and employs three benchmark sequential decision tasks as the evolutionary task for the population: connect-four, tic-tac-toe, and blackjack. Experiments are conducted with populations employing population learning alone and populations combining population and cultural learning. The article presents results showing the gradual transfer of knowledge from genes to the cultural process, illustrated by the simultaneous decrease in the population's innate fitness and the increase of its acquired fitness measured after learning takes place.

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Section: Cultural Learningsupporting

confidence: 70%

The Effects of Cultural Learning in Populations of Neural Networks

Curran

O’Riordan

2007

Artificial Life

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“…In previous work by Denaro and Parisi (1996), it was suggested that the addition of noise to a teacher's verbal output could enhance a population's ability to retain culturally acquired information. Experiments conducted in our previous work (Curran & O'Riordan, 2004) confirmed that small levels of noise introduced to the communication process improved both the formation of a shared lexicon and agent performance.…”

Section: Cultural Learningsupporting

confidence: 54%

Increasing Population Diversity Through Cultural Learning

Curran

O’Riordan

2006

Adaptive Behavior

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A number of learning models are commonly employed in the simulation of social behavior. These include population learning, lifetime learning and cultural learning. Population learning allows populations as a whole to evolve over time, typically through a Darwinian model of natural selection. Lifetime learning allows individuals to acquire knowledge during their lifetimes and cultural learning allows individuals to pass this knowledge to their peers or subsequent generations. This work examines the effects of cultural learning on both the fitness and the diversity of a population of neural network agents. A population employing population learning alone and one employing both population and cultural learning are assigned three benchmark tasks: the 5-bit parity problem, the game of tic-tac-toe and the game of connect-four. Each agent contains a genome which encodes a neural network controller used by the agent to perceive and react to environmental stimuli. Results show that the addition of cultural learning promotes improved fitness and significantly increases both genotypic (the genetic make up of individuals) and phenotypic (the behavior of individuals) diversity in the population.

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“…En [113], se considera la teoría de la cognición distribuida para establecer un modelo conexionista sobre el desarrollo cultural de símbolos lingüísticos. En [114] se emplea dicho modelo, en un algoritmo genético híbrido, con aprendizaje individual y cultural. Los mejores individuos en cada generación funcionan como tutores, al comunicar sus propias "verbalizaciones" sobre el medio ambiente a pupilos con la capacidad de imitarlas.…”

Section: Evolución Cultural Con Neuro-agentesunclassified

GeNeSys - sistema de co-evolución genética y neuro-memética para la auto-organización senso-motriz y conductual en una sociedad de robots

Buitrago¹

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Bio-inspired computing can be used to model natural and social systems, including societies with cultural development. Currently, two positions on cultural evolution stand out: with and without replicators. The existence of memes, as cultural replicators, is still hypothetical, and it seems better to look for them in the brain, because they can only be: neuro-memes. In literature there are only two models inspired by the neuro-memetics, and culture evolves side by side with genetics, so it’s necessary to model a gene-culture co-evolution, with neuro-memes. Such a model would be used to help validate the neuro-memetics, on the one hand, and on the other hand, it would help to understand and heal serious problems in human societies. Here, a genetic and neuro-memetic co-evolutionary system was achieved, and a robotic society used it for survive by developing behavioural patterns as a cultural tradition.

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The effect of noise on the performance of cultural evolution in multi-agent systems

Cited by 3 publications

References 19 publications

The Effects of Cultural Learning in Populations of Neural Networks

The Effects of Cultural Learning in Populations of Neural Networks

Increasing Population Diversity Through Cultural Learning

GeNeSys - sistema de co-evolución genética y neuro-memética para la auto-organización senso-motriz y conductual en una sociedad de robots

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