Coherence, Symbol Grounding and Interactive Task Learning
Year: 2019
Type: conference-paper
Abstract: To teach agents through natural language interaction, we need methods for updating the agent’s knowledge, given a teacher’s feedback. But natural language is ambiguous at many levels and so a major challenge is for the agent to disambiguate the intended message, given the signal and the context in which it’s uttered. In this paper we look at how coherence relations can be used to help disambiguate the teachers’ feedback and so contribute to the agent’s reasoning about how to solve their domain-level task. We conduct experiments where the agent must learn to build towers that comply with a set of rules, which the agent starts out ignorant of. It is also unaware of the concepts used to express the rules. We extend a model for learning these tasks which is based on coherence and show experimentally that our extensions can improve how fast the agent learns.
Authors: Mattias Appelgren, Alex Lascarides
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W3081101600