Contextual multi-armed bandit strategies for diagnosing post-harvest diseases of apple

Abstract

This paper describes a decision support system to fulfill the diagnosis of post-harvest diseases in apple fruit. The diagnostic system builds on user feedback elicitation via multiple conversational rounds. The interaction with the user is conducted by selecting and displaying images depicting symptoms of diseased apples. The system is able to adapt the image selection mechanism by exploiting previous user feedback through a contextual multi-armed bandit approach. We performed a large scale user experiment, where different strategies for image selection have been compared in order to identify which reloading strategy makes users more effective in the diagnosis task. Concretely, we compared contextual multi-armed bandit methods with two baseline strategies and identified that the exploration-exploitation principle significantly paid off in comparison to a greedy and to a random stratified selection strategy. Although the application context is very domain specific, we believe that the general methodology of conversational item selection for eliciting user preferences applies to other scenarios in decision support and recommendation systems.