International Business Machines Corporation

Doordash, Inc.

Qingzi Vera Liao

Yunfeng Zhang

Pavan Kapanipathi Bangalore

Biplav Srivastava

John Thomas Richards

Rachel Katherine Emma Bellamy

Robert G Farrell

Stephanie Houde

David John Piorkowski

Bhavya Ghai

Luis Alfonso Lastras-Montano

Michael Desmond

Song Feng

Klaus Mueller

Tian Gao

Ana Smith

David John Pirokowski

Rachel Katherine Emma Ballamy

An embodiment includes identifying, from a training dataset for training a model, a first unlabeled datapoint to present for labelling according to a first query strategy. The embodiment also includes issuing a query requesting a label for the first unlabeled datapoint. The embodiment also includes receiving a labeled datapoint in response to the query, the labeled datapoint comprising the first unlabeled datapoint as labeled by an oracle. The embodiment also includes generating a causal network based on labeled datapoints from the training dataset. The embodiment also includes receiving an instruction to modify the causal network. The embodiment also includes replacing the first query strategy with a second query strategy based on the instruction to modify the causal network. The embodiment also includes identifying, from the training dataset, a second unlabeled datapoint to present for labelling according to the second query strategy.

Active learning using causal network feedback

An autonomous agent operating method, system, and computer program product, including launching a first autonomous agent for a task with an initial update interval and adjusting the initial update interval for a second autonomous agent based on a second task for the second autonomous agent being similar to the task over time in relation to a trust level of a human user in a performance of the first autonomous agent.

Increasing trust formation and reduce oversight costs for autonomous agents

A method optimizes machine learning systems. A computing device accesses a committee of classifiers that have been trained using an initial labeled instance of data from an annotator. The initial labeled instance of data includes annotator-ranked attributes of the data, initial values of the attributes, and an initial prediction label that describes an initial predicted state based on the values. The computing system compares the attributes ranking from the annotator to attributes rankings that are generated by and used by each of the machine learning systems when evaluating one or more instances of unlabeled data that include the attributes, and weights the machine learning systems according to how closely each of the attributes rankings generated by and used by each of the machine learning systems match the attributes ranking from the annotator. The machine learning systems are then optimized based on this matching.

Optimizing a machine learning system

A dialogue complexity assessment method, system, and computer program product including calculating a complexity utilizing domain-dependent terms and domain-independent terms of a dialogue, where the dialogue includes dialogue data from contact centers of service providers.

Assessing complexity of dialogs to streamline handling of service requests

An approach is provided for training classifiers used in machine learning. A corpus of training data is received. One or more clusters of the training data is generated according to features of the training data. The one or more clusters are refined using user-specified rules. One or more classifiers are trained for use in machine learning based upon the refined one or more clusters.

Training classifiers in machine learning

An autonomous agent operating method, system, and computer program product, including configuring an autonomous agent for a task, launching the autonomous agent with an initial update interval, the update interval determining a frequency of the autonomous agent reporting results to a human user for review, measuring the trust level of human user in a performance of the autonomous agent, and dynamically adjusting the update interval based on this measured trust.

A computer-implemented method identifies one or more concepts from a document, wherein the document comprises information on completing a task. The method computes a relevance value of an utterance with respect to completing the task using the one or more identified concepts. The method removes the utterance from a dialog model to be used for completing the task when the relevance value of the utterance is below a given threshold value.

Adapting dialog models by relevance value for concepts to complete a task

A method, a computer program product, and a computer system determine when to transfer a communication session from an agent to a bot. The method includes monitoring the communication session between the agent and a user. The method includes determining a continuing utility value indicating a predicted continuing cost to maintaining the communication session with the agent. The continuing utility value is indicative of a predicted continuing benefit to maintaining the communication with the agent. The method includes determining a transferring utility value indicating a predicted transferring cost to transferring the communication session from the agent to the bot. The transferring utility value is indicative of a predicted transferring benefit to transferring the communication session from the agent to the bot. The method includes, as a result of the predicted transferring benefit being greater than the predicted continuing benefit, transferring the communication session from the agent to the bot.

Agent to bot transfer

A dialogue complexity assessment method, system, and computer program product for introducing the notion of dialogue complexity to understand and compare dialogues in a repository, calculating the dialogue complexity, use the dialogue complexity to understand customer interactions in a variety of domains using public and proprietary data, and demonstrate the dialogue complexity usage to improve a service management operation.

Growing community of inventors

Yorktown Heights, NY, United States of America

Qingzi Vera Liao