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Learning Gradual Argumentation Frameworks using Genetic Algorithms [article]

Jonathan Spieler, Nico Potyka, Steffen Staab
2021 arXiv   pre-print
Gradual argumentation frameworks represent arguments and their relationships in a weighted graph.  ...  As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models.  ...  We describe a genetic algorithm for learning gradual argumentation frameworks from data in Section 3 and present some experimental results in Section 4.  ... 
arXiv:2106.13585v1 fatcat:5c4boewwh5ddzgniwuyguz4ffq

Page 813 of Psychological Abstracts Vol. 89, Issue 2 [page]

2002 Psychological Abstracts  
Malleability and gradual transformability play an important role in facilitating evolutionary learning.  ...  A connectionist modeling framework, a network representation formalism for argument structures, connectionist network mechanisms, and their models of computations to extract the behavior of argument structures  ... 

The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

Dazhi Jiang, Zhun Fan
2015 Mathematical Problems in Engineering  
In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions.  ...  The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for their very detailed and helpful comments that help us to increase the quality of this work.  ... 
doi:10.1155/2015/474805 fatcat:uzgqpw5bejhifndyrtoxsao3e4

Cognitive evolutionary psychology without representational nativism

Denise Cummins, Robert Cummins, Pierre Poirier
2003 Journal of experimental and theoretical artificial intelligence (Print)  
However, those requirements would also be satisfied by heritable learning biases, perhaps in the form of architectural or chronotopic constraints, that operated to increase the canalization of specific  ...  As an organism develops, cognitive capacities that are highly canalized as the result of heritable learning biases might result in an organism that is behaviourally quite similar to an organism whose innate  ...  , the genetic algorithm does not evolve a capacity to navigate but an ability to quickly learn navigation.  ... 
doi:10.1080/0952813021000055162 fatcat:c54q4migejfexoqqif2ckmc6gq

Cognitive Evolutionary Psychology Without Representational Nativism [chapter]

Denise Dellarosa Cummins, Robert Cummins, Pierre Poirier
2010 The World in the Head  
However, those requirements would also be satisfied by heritable learning biases, perhaps in the form of architectural or chronotopic constraints, that operated to increase the canalization of specific  ...  As an organism develops, cognitive capacities that are highly canalized as the result of heritable learning biases might result in an organism that is behaviourally quite similar to an organism whose innate  ...  , the genetic algorithm does not evolve a capacity to navigate but an ability to quickly learn navigation.  ... 
doi:10.1093/acprof:osobl/9780199548033.003.0014 fatcat:yeadeiudmrglfgw5ctbwc4a6ie

Artificial Neural Network Architecture Optimization for Heart Disease Classification using Genetic Algorithm

Dicky Liegar
2020 International Journal of Advanced Trends in Computer Science and Engineering  
In this work, we implemented genetic algorithm to optimize the architecture of artificial neural network from previous work and used same dataset from Cleveland Heart Disease Data.  ...  For this study, we decided to use scikit-learn library and Multi Layer Perceptron (MLP) classifier for our neural network framework.  ...  Genetic Algorithm A better and feasible way to optimize hyperparameter is to use Evolutionary Algorithm [12] .  ... 
doi:10.30534/ijatcse/2020/146922020 fatcat:cpge4cz6dzhozmpyztdho23z5u

RL-EA: A Reinforcement Learning-Based Evolutionary Algorithm Framework for Electromagnetic Detection Satellite Scheduling Problem [article]

Yanjie Song, Luona Wei, Qing Yang, Jian Wu, Lining Xing, Yingwu Chen
2022 arXiv   pre-print
Based on the proposed framework, a Q-learning-based genetic algorithm(QGA) is designed. Q-learning is used to guide the population search process by choosing variation operators.  ...  The evolutionary algorithm framework based on reinforcement learning uses the Q-learning framework, and each individual in the population is regarded as an agent.  ...  Based on the framework, we propose a Q-learning based genetic algorithm.  ... 
arXiv:2206.05694v1 fatcat:dczmfmywkfhojn3f4spsgjaqbu

Towards Argumentative Decision Graphs: Learning Argumentation Graphs from Data

Pierpaolo Dondio
2021 International Conference of the Italian Association for Artificial Intelligence  
We evaluate a preliminary greedy algorithm to learn an 𝐴𝐷𝐺 from data using public datasets and we compare our results with Decision Tree in terms of balanced accuracy and size of the model.  ...  An 𝐴𝐷𝐺 is a special argumentation framework where arguments have a rule-based structure and an attack relation is defined among arguments.  ...  In [15] the authors proposed to use genetic algorithms to learn a gradual argumentation graph, considered as an instance of a sparse multi-layer neural network.  ... 
dblp:conf/aiia/Dondio21 fatcat:ghkni5hrvrfhhecllrbpmygzo4

Grammatical Assimilation [chapter]

Ted Briscoe
2003 Language Evolution  
Genetic assimilation is a neo-Darwinian (and not Lamarckian) mechanism supporting apparent 'inheritance of acquired characteristics ' (e.g. Waddington, 1942,  ...  , on 1 , or off 0 , was evolved using a genetic algorithm.  ...  Algorithm.  ... 
doi:10.1093/acprof:oso/9780199244843.003.0016 fatcat:npvxotskwnbprkla5zlo2xumvi

Evolutionary learning of graph layout constraints from examples

Toshiyuki Masui
1994 Proceedings of the 7th annual ACM symposium on User interface software and technology - UIST '94  
Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is.  ...  In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique.  ...  If a system can not only learn, but evolve to a more powerful learning system, it can be a very powerful framework for adaptive interfaces. Is Genetic Programming Really Effective?  ... 
doi:10.1145/192426.192468 dblp:conf/uist/Masui94 fatcat:hsdkehoox5bsfgft6s57nril6y

Artificial life and Piaget

Ulrich Mueller, K.H. Grobman
2003 Trends in Cognitive Sciences  
This new method uses artificial neural networks to simulate living phenomena in a computer.  ...  A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework.  ...  Artificial Life uses genetic algorithms as such tools.  ... 
doi:10.1016/s1364-6613(03)00034-2 pmid:12691760 fatcat:oeejfygdjbg7paz2emanjgdyrm

ADAPTIVE LEARNING SEARCH, A NEW TOOL TO HELP COMPREHENDING METAHEURISTICS

JOHANN DRÉO, JEAN-PHILIPPE AUMASSON, WALID TFAILI, PATRICK SIARRY
2007 International journal on artificial intelligence tools  
The majority of the algorithms used to solve hard optimization problems today are population metaheuristics.  ...  We discuss how to design metaheuristics following this approach, and propose an implementation with our Open Metaheuristics framework, along with concrete examples.  ...  Thus, sampling must concentrate on the areas of interest, while converging gradually towards the optimum by means of algorithms.  ... 
doi:10.1142/s0218213007003370 fatcat:zmhfzs6sjvgpbmvxcail6nu24i

Automorphic Equivalence-aware Graph Neural Network [article]

Fengli Xu, Quanming Yao, Pan Hui, Yong Li
2021 arXiv   pre-print
While the design of subgraph templates can be hard, we further propose a genetic algorithm to automatically search them from graph data.  ...  ., GRAPE, that uses learnable AE-aware aggregators to explicitly differentiate the Ego-AE of each node's neighbors with the aids of various subgraph templates.  ...  This inspires us to design a genetic optimization framework, which can navigate through the discrete search space via the gradual mutations between generations.  ... 
arXiv:2011.04218v2 fatcat:55xw635nufchbfhnrl6tov5ove

Eco-evo-devo and iterated learning: towards an integrated approach in the light of niche construction

José Segovia-Martín, Sergio Balari
2020 Biology & Philosophy  
We use the concept of niche construction to connect evolutionary developmental accounts for sensory guided motor capacities and cultural evolution guided by iterated learning models.  ...  We argue that eco-evo-devo provides the appropriate conceptual background to ground an account for the many interconnected genetic, environmental and developmental factors that facilitated the emergence  ...  For a detailed analysis of iterated learning using learning algorithms based on Bayesian inference, see Griffiths and Kalish (2007) .  ... 
doi:10.1007/s10539-020-09761-3 fatcat:tnk7bsrmrvgh3omqqzizvykwyq

An Alternative to Domain-general or Domain-specific Frameworks for Theorizing about Human Evolution and Ontogenesis

Annette Karmiloff-Smith
2015 AIMS Neuroscience  
Within this framework, learning was initially relegated to a very secondary role [28] .  ...  further domain-specific learning takes place?  ...  algorithms [1] [2] [3] [4] [5] , while others invoke more domain-general learning mechanisms without the need for representational content [6] [7] [8] .  ... 
doi:10.3934/neuroscience.2015.2.91 pmid:26682283 pmcid:PMC4678597 fatcat:hgtvtgo3fbf7dohvwpr7fa7fsu
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