Schema formalism for the common model of cognition

Alexei V. Samsonovich
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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.

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图式形式主义为认知的共同模式
通用认知模型(CMC)是一种集体尝试,旨在发展认知架构的共识。该模型需要扩展,以包括对实现类人人工智能目标至关重要的组件和功能,支持类人的可学习性、社会可接受性和类人的创造力。在生物学基础上,这些组成部分将使人工制品中的社会情感特征推理成为可能,并支持情感驱动行为的产生。历史上,认知架构起源于基于规则的系统。它们的主要构建块随后演变成各种各样的结构,在这里统称为模式。虽然模式是一个过载的术语,但在生物学启发的认知架构(BICA)领域,它可以被赋予精确而有用的含义,允许对不同的模型进行比较。这里使用一个特定的模型作为主要示例:情感BICA,或eBICA (Samsonovich, BICA, 2013),它扩展了GMU BICA (Samsonovich &De Jong, 2005),并支持类似人类的社交情商。这在所谓的道德图式的帮助下成为可能。它们的运作依赖于语义地图,并有助于叙事网络的功能。本文记录了道德图式的一般形式,定义了道德图式,并举例说明了道德图式的用法。该框架有望在各种实际重要领域和范式的虚拟演员和协作机器人中实现人类水平的可信度和社会兼容性,从而为人类人工智能的预期突破做出贡献。预期的应用包括虚拟协作机器人——助手和演员——在广泛的任务中的合作伙伴。就新框架的目标、范例、指标和目标应用达成共识,对于理解解决BICA挑战的总体使命同样重要。
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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%
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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.
期刊最新文献
Biologically Inspired Cognitive Architectures 2019 - Proceedings of the 10th Annual Meeting of the BICA Society, BICA 2019, Seattle, WA, USA, August 15-18, 2019 How Language Processing can Shape a Common Model of Cognition Application of Registration of Human Vegetative Reactions in the Process of Functional Magnetic Resonance Imaging Reconfigurable Locomotion of Hexapod Robot Based on Inverse Kinematics Methods of Determining Errors in Open-Ended Text Questions
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