International Business Machines Corporation

Surya Shravan Kumar Sajja

Amith Singhee

Ashok Pon Kumar Sree Prakash

Vijay Ekambaram

Roman Vaculin

Krishnasuri Narayanam

Vivek Shyamlal Sharma

Lam Nguyen

Harshit Kumar

Vikas Raykar

Satyam Dwivedi

Sumanta Mukherjee

Stephane Michel

Yuan Yuan Jia

Nupur Aggarwal

Kanthi Sarpatwar

A computer implemented method using an artificial intelligence (A.I.) module to explain large scale scheduling solutions includes receiving an original instance of a resource constrained scheduling problem. The instance includes a set of tasks and a variety of resource requirements and a variety of constraints. An optimizer process determines a schedule for the set of tasks while minimizing a makespan of the schedule. A minimal set of resource links is generated based on resource dependencies between tasks. The resource links are added to the original instance of scheduling problem, as precedence constraints. All the resource constraints are removed from the original instance of the resource constrained scheduling problem. A set of critical tasks is computed using a non-resource constrained critical path. Schedules are provided with an explanation of an optimized order of the set of tasks based on the use of the non-resource constrained critical path.

Optimizer agnostic explanation system for large scale schedules

Methods, systems, and computer program products for determining locations for offerings using artificial intelligence are provided herein. A computer-implemented method includes determining an initial sub-set of one or more enterprise locations, from a set of multiple enterprise locations, for providing at least one enterprise offering; determining at least one sequential ordering of one or more additional ones of the multiple enterprise locations for providing the at least one enterprise offering by processing data related to the initial sub-set of one or more enterprise locations using an artificial intelligence engine that utilizes one or more artificial intelligence techniques; and automatically training at least a portion of the artificial intelligence engine that utilizes the one or more artificial intelligence techniques based at least on feedback data pertaining to provision of the at least one enterprise offering to the at least one sequential ordering of one or more additional enterprise locations.

Determining locations for offerings using artificial intelligence

Methods, systems, and computer program products for data filtering with fuzzy attribute association are provided herein. A computer-implemented method includes obtaining one or more rules, specified by an expert, that define a partial ranking of a plurality of fuzzy pairings between (i) a plurality of item attributes for items in a data catalog and (ii) a plurality of user attributes related to said items; generating an interactive session with the expert to resolve one or more ambiguities in the one or more rules; and deriving a scoring function based at least in part on (i) the one or more rules and (ii) the resolved one or more ambiguities, wherein the scoring function generates a comparative score between any two items of said data catalog for a given one of the users associated with the plurality of attributes.

Data filtering with fuzzy attribute association

In an approach for an indoor survey data collection, a processor generates reference points based on a site map and an accuracy requirement. A processor collects data at each reference point through a data collecting agent. A processor detects an outlier at the reference points using a feedback from the data collecting agent during the data collection and a database. A processor eliminates the detected outlier and rectifies the data.

Indoor survey data collection

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Bengaluru, India

Surya Shravan Kumar Sajja