Abstract: Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.

Abstract: Systems and methods for call handling optimization of a call center are disclosed. A system is configured to: obtain a plurality of agent demand variables; obtain an indication of available agents that are associated with a plurality of skills; generate a multi-skill routing matrix depicting a routing problem from the agent demand variables and the available agents; deconstruct the multi-skill matrix into one or more band routing matrices; and identify, for each band routing matrix, a number of agents from the available agents to be used in solving the band routing matrix. Identifying the number of agents includes recursively simulating a plurality of possible routing solutions, measuring a quality metric for each simulation, and reducing the range of available agents based on the quality metric. The system may indicate the number of agents to be used for each routing sub-problem (which may be at the staff group level).

Abstract: Embodiments of the disclosure provide systems and methods for domain adaptation between a plurality of source domains and a target domain. The artificial intelligence method includes receiving labeled data from the plurality of source domains and unlabeled data from the target domain. The method further includes separately training, by a processor, a plurality of source classifiers each corresponding to a source domain using the labeled data received from the respective source domains. The method also includes selecting a subset of the labeled data received from each source domain based on a similarity between the selected labeled data and the unlabeled data of the target domain. The method additionally includes refining, by the processor, each source classifier using the selected subset of the labeled data, and predicting labels of the unlabeled data using the refined source classifiers.