International Business Machines Corporation

Lydia Manikonda

Biplav Srivastava

Shirin Sohrabi Araghi

Kartik Talamadupula

Anton Viktorovich Riabov

Deepak Srinivas Turaga

Embodiments for learning personalized actionable domain models by a processor. A domain model may be generated according to a plurality of actions, extracted from one or more online data sources, of a plurality of cluster representatives. The plurality of actions achieve a goal. A hierarchical action model may be generated based on probabilities of the domain model and the plurality of actions. The hierarchical action model comprises a sequence of actions of the plurality of actions for achieving the goal. The hierarchical action model may be personalized by filtering to a selected set of actions according to weighted actions of the plurality of actions.

Learning personalized actionable domain models

Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.

Autonomous learning of actionable models from unstructured data

Autonomous learning of actionable models from unstrutured data

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Tempe, AZ, United States of America

Lydia Manikonda