Multi-level metacognition for adaptive behavior

Q2 Psychology
Marvin Conn , Kenneth M'Bale , Darsana Josyula
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引用次数: 4

Abstract

Behavior adaptation is an integral aspect for autonomous agents to survive in a world where change is normal. Animals change their foraging routines and socializing habits based on predator risks in their environment. Humans adapt their behavior based on current interests, social norms, stress level, health conditions, upcoming deadlines and various other factors. Artificial agents need to effectively adapt to changes in their environment such that they can quickly adjust their behavior to maintain performance in the changed environment. In this paper, we present a multi-level metacognitive model that allows agents to adapt their behavior in various ways based on the resources available for metacognitive processing. As the agent operates at higher levels of this model, the agent is better equipped to adapt to a wider range of changes. The model has been tested on 2 different applications: (i) a reinforcement learner-based agent trying to navigate and collect rewards in a seasonal grid-world environment and (ii) a convolutional neural network-based agent trying to classify the signals in a radio frequency spectrum world and separate them into known modulations and unknown modulations.

适应性行为的多层次元认知
行为适应是自主主体在变化是常态的世界中生存的一个不可或缺的方面。动物会根据环境中捕食者的风险来改变它们的觅食习惯和社交习惯。人们根据当前的兴趣、社会规范、压力水平、健康状况、即将到来的截止日期和各种其他因素来调整自己的行为。人工智能体需要有效地适应环境的变化,以便在变化的环境中快速调整自己的行为以保持性能。在本文中,我们提出了一个多层次的元认知模型,该模型允许代理根据元认知加工可用的资源以各种方式调整其行为。由于代理在该模型的更高层次上运行,因此代理能够更好地适应更大范围的变化。该模型已经在两种不同的应用中进行了测试:(i)一个基于强化学习的智能体,试图在季节性网格世界环境中导航和收集奖励;(ii)一个基于卷积神经网络的智能体,试图在无线电频谱世界中对信号进行分类,并将它们分为已知调制和未知调制。
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来源期刊
Biologically Inspired Cognitive Architectures
Biologically Inspired Cognitive Architectures COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEN-NEUROSCIENCES
CiteScore
3.60
自引率
0.00%
发文量
0
期刊介绍: Announcing the merge of Biologically Inspired Cognitive Architectures with Cognitive Systems Research. Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.
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