Semiotic Dynamics Solves the Symbol Grounding Problem

Language requires the capacity to link symbols (words, sentences) through the intermediary of internal representations to the physical world, a process known as symbol grounding. One of the biggest debates in the cognitive sciences concerns the question how human brains are able to do this. Do we need a material explanation or a system explanation? John Searle's well known Chinese Room thought experiment, which continues to generate a vast polemic literature of arguments and counter-arguments, has argued that autonomously establishing internal representations of the world (called 'intentionality' in philosophical parlance) is based on special properties of human neural tissue and that consequently an artificial system, such as an autonomous physical robot, can never achieve this. Here we study the Grounded Naming Game as a particular example of symbolic interaction and investigate a dynamical system that autonomously builds up and uses the semiotic networks necessary for performance in the game. We demonstrate in real experiments with physical robots that such a dynamical system indeed leads to a successful emergent communication system and hence that symbol grounding and intentionality can be explained in terms of a particular kind of system dynamics. The human brain has obviously the right mechanisms to participate in this kind of dynamics but the same dynamics can also be embodied in other types of physical systems

Psycholinguistic Correlates of Symbol Grounding in Dictionaries

A dictionary can be represented as a directed graph with links from defining to defined words. The minimal feedback vertex sets (MinSets, Ms) of a dictionary graph are the smallest sets of words from which all the rest can be defined. We computed Ms for four English dictionaries. The words in the dictionary components revealed by our graph-theoretic analysis differ in their psycholinguistic correlates. Every MinSet has a C-part that is younger and more frequent and an S-part, that is more concrete. To understand the functional role of these components will require a close study of the words themselves, and how they are combined into definitions. We can already conclude that the closer a word is to the MinSets that can define all other words, the more concrete and frequent the word is likely to be, and the earlier it is likely to have been learned. This is what one would expect if the words in the MinSets were the ones that had been acquired through direct sensorimotor grounding

Symbol grounding and its implications for artificial intelligence

In response to Searle's well-known Chinese room argument against Strong AI (and more generally, computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, then the robot could be said to literally understand. In this article, I expand on the notion of symbol groundedness in three ways. Firstly, I show how a robot might select the best set of categories describing the world, given that fundamentally continuous sensory data can be categorised in an almost infinite number of ways. Secondly, I discuss the notion of grounded abstract (as opposed to concrete) concepts. Thirdly, I give an objective criterion for deciding when a robot's symbols become sufficiently grounded for "understanding" to be attributed to it. This deeper analysis of what symbol groundedness actually is weakens Searle's position in significant ways; in particular, whilst Searle may be able to refute Strong AI in the specific context of present-day digital computers, he cannot refute computationalism in general

A Praxical Solution of the Symbol Grounding Problem

Peer reviewe

Solutions and Open Challenges for the Symbol Grounding Problem

This article discusses the current progress and solutions to the symbol grounding problem and specifically identifies which aspects of the problem have been addressed and issues and scientific challenges that still require investigation. In particular, the paper suggests that of the various aspects of the symbol grounding problem, the transition from indexical representations to symbol-symbol relationships requires the most research. This analysis initiated a debate and solicited commentaries from experts in the field to gather consensus on progress and achievements and identify the challenges still open in the symbol grounding problem