Toward Forgetting-Sensitive Referring Expression Generationfor Integrated Robot Architectures
This addresses the challenge of natural dialogue in robotics, but it is incremental as it builds on existing theories of working memory.
The paper tackled the problem of generating human-like referring expressions for robots by modeling working memory forgetting, demonstrating that different forgetting models lead to differences in the generated expressions.
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.