# Formalising Natural Language Quantifiers for Human-Robot Interactions

arXiv: https://arxiv.org/abs/2308.13192 · HTML: https://mlsanity.com/paper/2308.13192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [A Hierarchical Architecture for Human-Robot Cooperation Processes](https://mlsanity.com/paper/2009.02807/) (2020)
- [Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://mlsanity.com/paper/2204.10615/) (2022)
- [A Compositional Approach to Verifying Modular Robotic Systems](https://mlsanity.com/paper/2208.05507/) (2022)

## Abstract (arXiv)

We present a method for formalising quantifiers in natural language in the context of human-robot interactions. The solution is based on first-order logic extended with capabilities to represent the cardinality of variables, operating similarly to generalised quantifiers. To demonstrate the method, we designed an end-to-end system able to receive input as natural language, convert it into a formal logical representation, evaluate it, and return a result or send a command to a simulated robot.