{"title":"Schema formalism for the common model of cognition","authors":"Alexei V. Samsonovich","doi":"10.1016/j.bica.2018.10.008","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>Common Model of Cognition (CMC) is a collective attempt to develop a consensus on cognitive architectures. The model needs to be extended to include components and functions that are vital to achieving the goals of Humanlike AI, supporting humanlike learnability, </span>social acceptability and humanlike creativity. Being biologically grounded, together these components will enable social-emotional character reasoning in artifacts and support emotionally-driven </span>behavior<span> generation. Historically, cognitive architectures originated from rule-based systems. Their main building block then evolved to a variety of structures, collectively called here schemas. While a schema is an overloaded term, in the field of biologically inspired cognitive architectures (BICA) it can be given a precise and useful meaning, allowing comparison of different models. Here one particular model is used as the main example: emotional BICA, or eBICA (Samsonovich, BICA, 2013) that extends GMU BICA (Samsonovich & De Jong, 2005) and supports human-like socially-emotional intelligence. This becomes possible with the help of so-called moral schemas. Their operation relies on semantic maps and contributes to the functioning of narrative networks. The present work documents the general formalism of schemas of eBICA, defines moral schemas, and explains their usage on examples. This framework is expected to enable a human-level believability and social compatibility in virtual actors and cobots across a variety of practically important domains and paradigms, thereby contributing to the expected breakthrough in humane artificial intelligence. Expected applications include virtual cobots-assistants and actors-partners in a broad spectrum of tasks. Forming a consensus on goals, paradigms, metrics and target applications for the new framework is equally important in understanding the overarching mission of solving the BICA Challenge.</span></p></div>","PeriodicalId":48756,"journal":{"name":"Biologically Inspired Cognitive Architectures","volume":"26 ","pages":"Pages 1-19"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bica.2018.10.008","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biologically Inspired Cognitive Architectures","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212683X18301440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Psychology","Score":null,"Total":0}
引用次数: 14
Abstract
Common Model of Cognition (CMC) is a collective attempt to develop a consensus on cognitive architectures. The model needs to be extended to include components and functions that are vital to achieving the goals of Humanlike AI, supporting humanlike learnability, social acceptability and humanlike creativity. Being biologically grounded, together these components will enable social-emotional character reasoning in artifacts and support emotionally-driven behavior generation. Historically, cognitive architectures originated from rule-based systems. Their main building block then evolved to a variety of structures, collectively called here schemas. While a schema is an overloaded term, in the field of biologically inspired cognitive architectures (BICA) it can be given a precise and useful meaning, allowing comparison of different models. Here one particular model is used as the main example: emotional BICA, or eBICA (Samsonovich, BICA, 2013) that extends GMU BICA (Samsonovich & De Jong, 2005) and supports human-like socially-emotional intelligence. This becomes possible with the help of so-called moral schemas. Their operation relies on semantic maps and contributes to the functioning of narrative networks. The present work documents the general formalism of schemas of eBICA, defines moral schemas, and explains their usage on examples. This framework is expected to enable a human-level believability and social compatibility in virtual actors and cobots across a variety of practically important domains and paradigms, thereby contributing to the expected breakthrough in humane artificial intelligence. Expected applications include virtual cobots-assistants and actors-partners in a broad spectrum of tasks. Forming a consensus on goals, paradigms, metrics and target applications for the new framework is equally important in understanding the overarching mission of solving the BICA Challenge.
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.