Case-Based Sample Generation Using Multi-Armed Bandits

A central problem in knowledge-based tasks is to provide a collection of reusable knowledge samples extracted from a textual corpus. Often, such corpora are structured into different documents or topics, respectively. The samples need to be proven for usability and adapted by a domain expert requiring a certain processing time for each sample taken. The goal is to achieve an optimal retrieval and adaptation success meeting the time budget of the domain expert. In this work, we formulate this task as a constrained multi-armed bandit model. We combine it with the model of a configurable data-driven case-based learning agent. A case study evaluates the theoretical considerations in a scenario of regulatory knowledge acquisition. Therefore, a data set is constructed out of a corpus of nuclear safety documents. We use the model to optimize the evaluation process of sample generation of adaptational knowledge. The corresponding knowledge graph has been created in an information extraction step by automatically identifying semantic concepts and their relations.