公开(公告)号：US20240160943A1

公开(公告)日：2024-05-16

申请号：US18054009

申请日：2022-11-09

Applicant: Salesforce.com, inc.

Inventor： Soham Phade ,  Stefano Ermon ,  Stephan Zheng

IPC: G06N3/092

CPC classification number: G06N3/092

Abstract: Embodiments described herein provide systems and methods for solving and applying a multi-agent decision process. A system performs a process, where at each iterative step, the system determines policies for a plurality of agents that optimize respective reward values based on the plurality of costs, and the characteristics of the plurality of agents. The system simulates the multi-agent decision process using the determined policies, thereby generating respective reward values and aggregated resource contribution values. The system increments or decrements the plurality of costs based on the constraints and the aggregated resource contribution values. The system updates a final reward value based on the respective reward values. The system updates a final plurality of costs based on the plurality of costs. After performing the iterative step for a predetermined number of iterations, the system outputs the final reward value and the final plurality of costs.

公开(公告)号：US11087092B2

公开(公告)日：2021-08-10

申请号：US16399871

申请日：2019-04-30

Applicant: salesforce.com, inc.

Inventor： Stephan Zheng ,  Wojciech Kryscinski ,  Michael Shum ,  Richard Socher ,  Caiming Xiong

IPC: G06F40/30 ,  G06N3/08 ,  G06F40/205

Abstract: Approaches for determining a response for an agent in an undirected dialogue are provided. The approaches include a dialogue generating framework comprising an encoder neural network, a decoder neural network, and a language model neural network. The dialogue generating framework generates a sketch sentence response with at least one slot. The sketch sentence response is generated word by word and takes into account the undirected dialogue and agent traits of the agent making the response. The dialogue generating framework generates sentence responses by filling the slot with words from the agent traits. The dialogue generating framework ranks the sentence responses according to perplexity by passing the sentence responses through a language model and selects a final response which is a sentence response that has a lowest perplexity.