Dempster-Shafer Theoretic Learning of Indirect Speech Act Comprehension Norms

Ruchen Wen, Mohammed Aun Siddiqui, Tom Williams
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
For robots to successfully operate as members of human-robot teams, it is crucial for robots to correctly understand the intentions of their human teammates. This task is particularly difficult due to human sociocultural norms: for reasons of social courtesy (e.g., politeness), people rarely express their intentions directly, instead typically employing polite utterance forms such as Indirect Speech Acts (ISAs). It is thus critical for robots to be capable of inferring the intentions behind
more » ... r teammates' utterances based on both their interaction context (including, e.g., social roles) and their knowledge of the sociocultural norms that are applicable within that context. This work builds off of previous research on understanding and generation of ISAs using Dempster-Shafer Theoretic Uncertain Logic, by showing how other recent work in Dempster-Shafer Theoretic rule learning can be used to learn appropriate uncertainty intervals for robots' representations of sociocultural politeness norms.
doi:10.1609/aaai.v34i06.6610 fatcat:2cigs6nkz5dq7kamc2vhajb6e4