4 research outputs found

    A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems

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    Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains

    Structured Memetic Automation for Online Human-like Social Behavior Learning

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    Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior

    Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

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    This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top-K models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners' mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches

    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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    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.La computación bio-inspirada puede ser empleada para modelar sistemas naturales y sociales, entre los cuales están las sociedades con desarrollo cultural. En la actualidad, sobresalen dos posturas sobre la evolución cultural: con y sin replicadores. La existencia de memes, como replicadores culturales, es aún hipotética, y parece mejor buscarlos en el cerebro, porque solo pueden ser: neuro-memes. En la literatura hay apenas dos modelos inspirados en la concepción neuro-memética, y como la evolución cultural va de la mano con la genética, se requiere entonces modelar una co-evolución gene-cultura, basada en neuro-memes. Un modelo así, se usaría para ayudar a validar la hipótesis neuro-memética, por un lado, y por el otro, ayudaría a comprender y atender serias problemáticas en las sociedades humanas. Con este proyecto se logró un sistema de co-evolución genética y neuro-memética, que fue usado por una sociedad de robots para sobrevivir, desarrollando un comportamiento cultural.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí
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