MABE (Modular Agent Based Evolver): A framework for digital evolution research

Bohm, Clifford; Nitash, C. G.; Hintze, Arend

doi:10.7551/ecal_a_016

Cited by 25 publications

(19 citation statements)

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“…For this work we implemented our evolutionary simulation using the MABE software (Bohm et al, 2017). In order to understand the relationship between noise, tournament size and, population size, with rates of adaptation, we ran an array of nearly 4,500 scenarios.…”

Section: Methodsmentioning

confidence: 99%

Connections between Noisy Fitness and Selection Strength

Ragusa

Bohm

2021

The 2021 Conference on Artificial Life

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Selection can be described as a filtering process which changes a population over time with regard to the result of some evaluation (i.e. a fitness function). We are interested understanding the relationship between different parameters for altering selection strength and rates of adaptation. In this work we perform a detailed assay exploring the relationship between population size, noisy phenotype evaluation, and tournament size, and their effects on rates of genomic change. We run our model on nearly 4,500 different scenarios.We observe evolution on a smooth fitness landscape as well as nine deceptive landscapes using our model. We show that for the smooth landscape it is always best to have strong selection with noise-free fitness and a large population. For deceptive landscapes, there is an optimum configuration of tournament size and noise that balances exploration and exploitation. Population size, on the other hand, always increases genomic change when larger, because it not only increases selection strength but also maximizes mutational inflow and standing variation. We see that while these parameters for selection strength have similar effects, they each behave in unique ways. Finally, we suggest that evaluation noise is a better proxy for selection strength than population size.

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Section: Methodsmentioning

confidence: 99%

Connections between Noisy Fitness and Selection Strength

Ragusa

Bohm

2021

The 2021 Conference on Artificial Life

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“…All experiments were carried out using evolvable Markov Brains [6] in the MABE framework [3]. For these brains R (representations) and the smearedness (structure) of these representations can be easily quantified [7,8,10].…”

Section: Methodsmentioning

confidence: 99%

Augmenting neuro-evolutionary adaptation with representations does not incur a speed accuracy trade-off

Kirkpatrick

Hintze

2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Representations, or sensor-independent internal models of the environment, are important for any type of intelligent agent to process and act in an environment. Imbuing an artificially intelligent system with such a model of the world it functions in remains a difficult problem. However, using neuro-evolution as the means to optimize such a system allows the artificial intelligence to evolve proper models of the environment. Previous work has found an informationtheoretic measure, R, which measures how much information a neural computational architecture (henceforth loosely referred to as a brain) has about its environment, and can additionally be used speed up the neuro-evolutionary process. However, it is possible that this improved evolutionary adaptation comes at a cost to the brain's ability to generalize or the brain's robustness to noise. In this paper, we show that this is not the case; to the contrary, we find an improved ability of the to evolve in noisy environments when the neuro-correlate R is used to augment evolutionary adaptation.

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“…This section explains the animat designs, the environment, the evolutionary simulations, and the experiment setup. We used MABE (Modular Agent-Based Evolver) [41] as a computational evolution framework with the same parameters as in previous work [17] (see Table S7 in Supporting Information).…”

Section: Methodsmentioning

confidence: 99%

How cognitive and environmental constraints influence the reliability of simulated animats in groups

Fischer

Mostaghim

Albantakis

2019

Preprint

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16Evolving in groups can either enhance or reduce an individual's task performance. Still, 17 we know little about the factors underlying group performance, which may be reduced to 18 three major dimensions: (a) the individual's ability to perform a task, (b) the dependency on 19 environmental conditions, and (c) the perception of, and the reaction to, other group 20 members. In our research, we investigated how these dimensions interrelate in simulated 21 evolution experiments using adaptive agents equipped with Markov brains ("animats"). We 22 evolved the animats to perform a spatial-navigation task under various evolutionary setups.23 The last generation of each evolution simulation was tested across modified conditions to 24 evaluate and compare the animats' reliability when faced with change. Moreover, the 25 complexity of the evolved Markov brains was assessed based on measures of information 26 integration. We found that, under the right conditions, specialized animats were as reliable 27 as animats already evolved for the modified tasks, that interaction between animats was 28 dependent on the environment and on the design of the animats, and that the task difficulty 29 influenced the correlation between the performance of the animat and its brain complexity.30 Generally, our results suggest that the interrelation between the aforementioned dimensions 31 is complex and their contribution to the group's task performance, reliability, and brain 32 complexity varies, which points to further dependencies. Still, our study reveals that 33 balancing the group size and individual cognitive abilities prevents over-specialization and 34 can help to evolve better reliability under unknown environmental situations. 35 Keywords: Collective behavior, evolutionary algorithms, cognitive science, Markov brains. PLOS Computational Biology --FOR REVIEW 3 of 35 36Author Summary 37The ability to adapt to environmental changes is an essential attribute of organisms which 38 have had evolutionary success. We designed a simulated evolution experiment to better 39 understand the relevant features of such organisms and the conditions under which they 40 evolve: First, we created diverse groups of cognitive systems by evolving simulated 41 organisms ("animats") acting in groups on a spatial-navigation task. Second, we post-42 evolutionary tested the final evolved animats in new environments-not encountered before-43 in order to test their reliability when faced with change. Our results imply that the ability to 44 generalize to environments with changing task demands can have complex dependencies on 45 the cognitive design and sensor configuration of the organism itself, as well as its social or 46 environmental conditions. 47Introduction 48Intelligence is the ability to adapt to changes. According to this prevalent perspective, 49 possessing general intelligence [1,2] not only enables one to perform a task correctly under 50 already known conditions, but also to perform well under unexpected conditions. Further, in 51 natural ...

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MABE (Modular Agent Based Evolver): A framework for digital evolution research

Cited by 25 publications

References 0 publications

Connections between Noisy Fitness and Selection Strength

Connections between Noisy Fitness and Selection Strength

Augmenting neuro-evolutionary adaptation with representations does not incur a speed accuracy trade-off

How cognitive and environmental constraints influence the reliability of simulated animats in groups

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