Human natural languages use quantifiers as ways to designate the number of objects
of a set. They include numerals, such as ``three'', or circumscriptions, such as ``a few''.
The latter are not only underdetermined but also context dependent.
We provide a cultural-evolution explanation for the emergence of
such quantifiers, focusing in particular on the role of environmental constraints on
strategy choices. Through a series of situated interaction experiments, we show how a
community of robotic agents can self-organize a quantification system.
Different perceptions of the scene make underdetermined quantifiers useful and
environments in which the distribution of objects exhibits some degree of predictability creates
favorable conditions for context-dependent quantifiers.
Carcassi, Fausto, Shane Steinert‐Threlkeld & Jakub Szymanik
2021. Monotone Quantifiers Emerge via Iterated Learning. Cognitive Science 45:8
Gierasimczuk, Nina, Dariusz Kalociński, Franciszek Rakowski & Jakub Uszyński
2023. Convexity and Monotonicity in Language Coordination: Simulating the Emergence of Semantic Universals in Populations of Cognitive Agents. Journal of Logic, Language and Information 32:4 ► pp. 569 ff.
Kalociński, Dariusz, Marcin Mostowski & Nina Gierasimczuk
2018. Interactive Semantic Alignment Model: Social Influence and Local Transmission Bottleneck. Journal of Logic, Language and Information 27:3 ► pp. 225 ff.
Nevens, Jens, Paul Van Eecke & Katrien Beuls
2020. From Continuous Observations to Symbolic Concepts: A Discrimination-Based Strategy for Grounded Concept Learning. Frontiers in Robotics and AI 7
2012. Embodied Quantifiers. In New Directions in Logic, Language and Computation [Lecture Notes in Computer Science, 7415], ► pp. 52 ff.
Smith, Andrew D.M.
2014. Models of language evolution and change. Wiley Interdisciplinary Reviews: Cognitive Science 5:3 ► pp. 281 ff.
Spranger, Michael, Simon Pauw, Martin Loetzsch & Luc Steels
2012. Open-ended Procedural Semantics. In Language Grounding in Robots, ► pp. 153 ff.
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