公开(公告)号：US20210326727A1

公开(公告)日：2021-10-21

申请号：US17249454

申请日：2021-03-02

Applicant: Tata Consultancy Services Limited

Inventor： Garima GUPTA ,  Ankit SHARMA ,  Ranjitha PRASAD ,  Arnab CHATTERJEE ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N5/04 ,  G06N3/04 ,  G06F16/2458

Abstract: Causality is a crucial paradigm in several domains where observational data is available. Primary goal of Causal Inference (CI) is to uncover cause-effect relationship between entities. Conventional methods face challenges in providing an accurate CI framework due to cofounding and selection bias in multiple treatment scenario. The present disclosure computes a Propensity Score (PS) from a received CI data for the plurality of subjects under test for a treatment. A Generalized Propensity Score (GPS) is computed for a plurality of treatments corresponding to the plurality of subjects by using the PS. Further, a plurality of task batches are created using the GPS and given as input to the DNN for training. Errors in factual data and in balancing representation of the DNN are rectified using a novel loss function. The trained DNN is further used for predicting the counter factual treatment response corresponding to the factual treatment data.

公开(公告)号：US20240211815A1

公开(公告)日：2024-06-27

申请号：US18527487

申请日：2023-12-04

Applicant: Tata Consultancy Services Limited

Inventor： Shashee KUMARI ,  Sakyajit BHATTACHARYA ,  Avik GHOSE ,  Arnab CHATTERJEE

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: Research work in the literature on imputation of mobility data for missing records of a subject's location trajectory has been specifically revolved around usage of historical data. Thus, performances drop when missing records or imputation mobility data for unknown subject with very little or no historical data has to be predicted. A method and system for training an ensemble classifier for imputation of mobility data of unknown subject based on cohort of the unknown subject is disclosed. The method and system disclosed herein exploits the knowledge that semantic trajectories of different individuals has considerable similarity when individuals belong to the same cohort. This concept is used by the method to predict the behavior of all the individuals in a cohort using ensemble classifier, also referred to as imputation model, trained on the semantic location data of a fraction of total individuals in the cohort with a certain accuracy.

公开(公告)号：US20200020061A1

公开(公告)日：2020-01-16

申请号：US16510748

申请日：2019-07-12

Applicant: Tata Consultancy Services Limited

Inventor： Vishal SUNDER ,  Lovekesh VIG ,  Arnab CHATTERJEE ,  Gautam SHROFF

IPC: G06Q50/18 ,  G06N5/04

Abstract: This disclosure relates generally to method and system for performing negotiation task using reinforcement learning agents. Performing negotiation on a task is a complex decision making process and to arrive at consensus on contents of a negotiation task is often expensive and time consuming due to the negotiation terms and the negotiation parties involved. The proposed technique trains reinforcement learning agents such as negotiating agent and an opposition agent. These agents are capable of performing the negotiation task on a plurality of clauses to agree on common terms between the agents involved. The system provides modelling of a selector agent on a plurality of behavioral models of a negotiating agent and the opposition agent to negotiate against each other and provides a reward signal based on the performance. This selector agent emulate human behavior provides scalability on selecting an optimal contract proposal during the performance of the negotiation task.