Symbol Emergence in Cognitive Developmental Systems: a Survey
arXiv:1801.08829 · doi:10.1109/TCDS.2018.2867772
Abstract
Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to {\it symbols}. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems.
23 pages, 6 figures. Submitted to IEEE Transactions on Cognitive and Developmental Systems
References in corpus (15)
- Auto-Encoding Variational Bayes
- The Symbol Grounding Problem
- Edward: A library for probabilistic modeling, inference, and criticism
- Composing graphical models with neural networks for structured representations and fast inference
- Learning Symbolic Models of Stochastic Domains
- Symbol Emergence in Robotics: A Survey
- Grounded Language Learning in a Simulated 3D World
- Discovering objects and their relations from entangled scene representations
- Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
- Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences
- Planning with Noisy Probabilistic Relational Rules
- Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals
- Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture
- Grounding Spatio-Semantic Referring Expressions for Human-Robot Interaction
- Detection of social signals for recognizing engagement in human-robot interaction
Cited by in corpus (21)
- Philosophical Specification of Empathetic Ethical Artificial Intelligence
- Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models
- Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping
- A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots
- From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
- Compression, The Fermi Paradox and Artificial Super-Intelligence
- Symbol Emergence and The Solutions to Any Task
- DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
- Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI
- The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence
- Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping
- Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis
- From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges
- Discovering Predictive Relational Object Symbols with Symbolic Attentive Layers
- Learning Type-Generalized Actions for Symbolic Planning
- High-level Features for Resource Economy and Fast Learning in Skill Transfer
- On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems based on Probabilistic Generative Models
- Beyond Individuals: Collective Predictive Coding for Memory, Attention, and the Emergence of Language
- Symbol Emergence as an Interpersonal Multimodal Categorization
- Bootstrapping Concept Formation in Small Neural Networks
- Learning Multi-Object Symbols for Manipulation with Attentive Deep Effect Predictors