Emergence of specialized Collective Behaviors in Evolving Heterogeneous Swarms

• URL: http://arxiv.org/abs/2402.04763v1
• Date: Wed, 7 Feb 2024 11:26:53 GMT
• Title: Emergence of specialized Collective Behaviors in Evolving Heterogeneous Swarms
• Authors: Fuda van Diggelen, Matteo De Carlo, Nicolas Cambier, Eliseo Ferrante, A.E. Eiben
• Abstract summary: Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization. We evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior.
• Score: 3.918604886944516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful to address complex tasks and to survive. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with one different neural network controller associated to each component. The whole genotype, expressing the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyse the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show three main findings: 1) The sub-groups evolve distinct emergent behaviors. 2) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 3) The online regulatory mechanism enhances overall performance and scalability.

Related papers

• Emergent Collective Reproduction via Evolving Neuronal Flocks [0.0]
  This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova. VitaNova intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups.
  arXiv  Detail & Related papers  (2024-09-20T06:22:24Z)
• Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems [59.117526206317116]
  We show that CELL can adaptively evolve into different models for different tasks and data. Experiments on four real-world datasets demonstrate that CELL significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-29T02:35:23Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• An evolutionary model of personality traits related to cooperative behavior using a large language model [0.4972818675034513]
  We study the evolution of personality traits in the context of a game-theoretic relationship as a situation in which inter-individual interests exert strong selection pressures. We construct an agent model in which linguistic descriptions of personality traits related to cooperative behavior are used as genes. The deterministic strategies extracted from Large Language Model (LLM) that make behavioral decisions based on these personality traits are used as behavioral traits.
  arXiv  Detail & Related papers  (2023-10-03T14:35:27Z)
• Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction [58.67761673662716]
  Humans are highly adaptable, swiftly switching between different modes to handle different tasks, situations and contexts. In Human-object interaction (HOI) activities, these modes can be attributed to two mechanisms: (1) the large-scale consistent plan for the whole activity and (2) the small-scale children interactive actions that start and end along the timeline. This work proposes to model two concurrent mechanisms that jointly control human motion.
  arXiv  Detail & Related papers  (2023-07-24T12:21:33Z)
• On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search [0.0]
  Evolvability refers to the ability of an individual genotype to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent search methods promote evolvability by implicitly creating selective pressure for it. This paper provides a novel perspective on the relationship between neuroevolutionary divergent search and evolvability.
  arXiv  Detail & Related papers  (2023-06-16T13:46:55Z)
• Learning signatures of decision making from many individuals playing the same game [54.33783158658077]
  We design a predictive framework that learns representations to encode an individual's 'behavioral style' We apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task.
  arXiv  Detail & Related papers  (2023-02-21T21:41:53Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Heritability in Morphological Robot Evolution [2.7402733069181]
  We introduce the biological notion of heritability, which captures the amount of phenotypic variation caused by genotypic variation. In our analysis we measure the heritability on the first generation of robots evolved from two different encodings. We show how heritability can be a useful tool to better understand the relationship between genotypes and phenotypes.
  arXiv  Detail & Related papers  (2021-10-21T14:58:17Z)
• Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types [75.65676405302105]
  We propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT. We pre-train our model on the ATAC-seq dataset with 17 million genome sequences.
  arXiv  Detail & Related papers  (2021-10-11T12:48:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.