Constructivist procedural learning for grounded cognitive agents

IF 2.1 3区 心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sean Kugele
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引用次数: 0

Abstract

Constructivism is a learning theory based on the idea that individuals actively build their understanding of the world through their interactions with their environment. Learning is a dynamic process where new knowledge builds on prior knowledge, and a learner’s mental models are continually refined by their experiences. Building on this theoretical framework and Drescher’s seminal contributions to constructivist AI, this paper explores constructivism within the context of LIDA (Learning Intelligent Decision Agent), a biologically inspired cognitive architecture. Specifically, I develop a modified version of Drescher’s schema mechanism, which I use to implement LIDA’s Procedural Memory and Action Selection modules. I demonstrate that an agent based on this implementation can construct an accurate internal model of its environmental interactions and use that model to select goal-directed behaviors. This work significantly advances LIDA’s computational capabilities by implementing grounded instructionist procedural learning, hierarchical action plans, and the selection of exploratory behaviors. These computational enhancements will enable the creation of more sophisticated LIDA-based agents that can operate in more complex environments where the hand-coding of procedural knowledge is infeasible. An alternate way to view this work is as an enhancement to Drescher’s schema mechanism, which is a purely symbolic and ungrounded cognitive system. LIDA’s sensory and perceptual systems provide a means by which the schema mechanism’s representations can be grounded. This, in itself, is an important contribution of this paper.
基于认知主体的建构主义程序学习
建构主义是一种学习理论,其基础是个体通过与环境的互动积极地建立对世界的理解。学习是一个动态的过程,新知识建立在已有知识的基础上,学习者的思维模式会随着他们的经历而不断完善。基于这一理论框架和Drescher对建构主义人工智能的开创性贡献,本文探讨了LIDA(学习智能决策代理)背景下的建构主义,LIDA是一种受生物学启发的认知架构。具体来说,我开发了一个修改版本的Drescher的模式机制,我用它来实现LIDA的程序记忆和动作选择模块。我证明了基于这种实现的智能体可以构建其环境相互作用的精确内部模型,并使用该模型选择目标导向的行为。这项工作通过实施基础教学程序学习、分层行动计划和探索行为的选择,显著提高了LIDA的计算能力。这些计算能力的增强将能够创建更复杂的基于lida的代理,这些代理可以在更复杂的环境中运行,在这些环境中,程序知识的手工编码是不可行的。另一种看待这项工作的方式是作为对Drescher的图式机制的增强,这是一个纯粹的象征性和无根据的认知系统。LIDA的感觉和知觉系统提供了一种方法,通过这种方法,图式机制的表征可以建立起来。这本身就是本文的一个重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cognitive Systems Research
Cognitive Systems Research 工程技术-计算机:人工智能
CiteScore
9.40
自引率
5.10%
发文量
40
审稿时长
>12 weeks
期刊介绍: 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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