70 research outputs found
Construction Grammar and Artificial Intelligence
In this chapter, we argue that it is highly beneficial for the contemporary
construction grammarian to have a thorough understanding of the strong
relationship between the research fields of construction grammar and artificial
intelligence. We start by unravelling the historical links between the two
fields, showing that their relationship is rooted in a common attitude towards
human communication and language. We then discuss the first direction of
influence, focussing in particular on how insights and techniques from the
field of artificial intelligence play an important role in operationalising,
validating and scaling constructionist approaches to language. We then proceed
to the second direction of influence, highlighting the relevance of
construction grammar insights and analyses to the artificial intelligence
endeavour of building truly intelligent agents. We support our case with a
variety of illustrative examples and conclude that the further elaboration of
this relationship will play a key role in shaping the future of the field of
construction grammar.Comment: Peer-reviewed author's draft of a chapter to appear in the Cambridge
Handbook of Construction Grammar (2024 - edited by Mirjam Fried and Kiki
Nikiforidou
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning
In this paper, we formulate the challenge of re-conceptualising the language
game experimental paradigm in the framework of multi-agent reinforcement
learning (MARL). If successful, future language game experiments will benefit
from the rapid and promising methodological advances in the MARL community,
while future MARL experiments on learning emergent communication will benefit
from the insights and results gained from language game experiments. We
strongly believe that this cross-pollination has the potential to lead to major
breakthroughs in the modelling of how human-like languages can emerge and
evolve in multi-agent systems.Comment: This paper was accepted for presentation at the 2020 AAAI Spring
Symposium `Challenges and Opportunities for Multi-Agent Reinforcement
Learning' after a double-blind reviewing proces
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning
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