Paper Info
Title
Symbol grounding through cumulative learning
Abstract
We suggest that the primary motivation for an agent to construct a symbol-meaning mapping is to solve a task. The meaning space of an agent should be derived from the tasks that it faces during the course of its lifetime. We outline a process in which agents learn to solve multiple tasks and extract a store of “cumulative knowledge” that helps them to solve each new task more quickly and accurately. This cumulative knowledge then forms the ontology or meaning space of the agent. We suggest that by grounding symbols to this extracted cumulative knowledge agents can gain a further performance benefit because they can guide each others' learning process. In this version of the symbol grounding problem meanings cannot be directly communicated because they are internal to the agents, and they will be different for each agent. Also, the meanings may not correspond directly to objects in the environment. The communication process can also allow a symbol meaning mapping that is dynamic. We posit that these properties make this version of the symbol grounding problem realistic and natural. Finally, we discuss how symbols could be grounded to cumulative knowledge via a situation where a teacher selects tasks for a student to perform.
Year
DOI
Venue
2006
10.1007/11880172_14
EELC
Keywords
Field
DocType
communication process,cumulative learning,performance benefit,new task,meaning space,problem meaning,cumulative knowledge,symbol-meaning mapping,multiple task,symbol grounding,symbol meaning mapping,cumulative knowledge agent,cumulant
Ontology,Multi-task learning,Turing test,Language-game,Symbol,Computer science,Symbol grounding,Recurrent neural network,Artificial intelligence
Conference
Volume
ISSN
ISBN
4211
0302-9743
3-540-45769-0
Citations 
PageRank 
References 
4
0.58
14
Authors
4
Name
Order
Citations
PageRank
Samarth Swarup121328.37
Kiran Lakkaraju244536.90
Sylvian R. Ray37040.41
Les Gasser41601261.00