{"title":"Constructivist procedural learning for grounded cognitive agents","authors":"Sean Kugele","doi":"10.1016/j.cogsys.2025.101321","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"90 ","pages":"Article 101321"},"PeriodicalIF":2.1000,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Systems Research","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389041725000014","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.