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Paper TuB210.5

Lim, Gi Hyun (Hanyang University), Suh, Il Hong (Hanyang University)

Improvisational Goal-Oriented Action Recommendation under Incomplete Knowledge Base

Scheduled for presentation during the Interactive Session "Interactive Session TuB-2" (TuB210), Tuesday, May 15, 2012, 11:00−11:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords AI Reasoning Methods, Reactive and Sensor-Based Planning, Service Robots

Abstract

Robots need to have knowledge of their environment to be able to successfully complete service tasks. Most knowledge inference mechanisms assume complete and correct knowledge about the environment. Real world environments are often uncertain and only partially observable. Thus, intelligent service robots may have an incomplete knowledge base which includes true positives as well as false negatives and false positives. False negatives and false positives can prevent service robots from completing their service tasks. In the field of logical inference, false positives are a more significant problem compared to false negatives. A weighted ontology and association mechanism was proposed in previous research which recommended improvisational goal-oriented actions that could be applied in the case of false negatives. However, false positives are not usually matched in the existing ontological semantic network. Consequently, the association mechanism does not work. To deal with false positives, the weighted ontology and association mechanism were extended by adding additional nodes which are associated with epistemic actions. The proposed method was successfully evaluated and verified through experiments; results show that almost all problems associated with false positives and false negatives were resolved.

 

 

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