Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures

TL;DR

This paper explores how different models of working memory forgetting influence the generation of referring expressions in robots, aiming to improve human-like dialogue by aligning robot descriptions with human cognitive processes.

Contribution

It introduces and computationalizes two models of working memory forgetting within a robot architecture to analyze their impact on referring expression generation.

Findings

Different forgetting models lead to distinct referring expressions.

Cognitive availability influences the properties used in descriptions.

Models can produce more human-like referring expressions.

Abstract

To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as "Referring Expression Generation." As speakers repeatedly refer to similar objects, they tend to re-use properties from previous descriptions, in part to help the listener, and in part due to cognitive availability of those properties in working memory (WM). Because different theories of working memory "forgetting" necessarily lead to differences in cognitive availability, we hypothesize that they will similarly result in generation of different referring expressions. To design effective intelligent agents, it is thus necessary to determine how different models of forgetting may be differentially effective at producing natural human-like referring expressions. In this work, we computationalize two candidate models of working memory forgetting within a robot cognitive architecture, and demonstrate how they lead to cognitive availability-based differences in generated referring expressions.