Abstract
Robots designed to interact with humans in realistic environments must be able to handle uncertainty with respect to the identities and properties of the people, places, and things found in their environments. When humans refer to these entities using under-specified language, robots must often generate clarification requests to determine which entities were meant. In this paper, we first present recommendations for designers of robots needing to generate such requests. We then show how a Dempster-Shafer theoretic pragmatic reasoning component capable of generating requests to clarify pragmatic uncertainty can also generate requests to resolve referential uncertainty when integrated with probabilistic reference resolution and referring expression generation components. Our system is then demonstrated in a simulated alpine search and rescue context enabled by a novel hybrid architecture.
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