公开(公告)号：US20250130567A1

公开(公告)日：2025-04-24

申请号：US18492313

申请日：2023-10-23

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Claudia GOLDMAN-SHENHAR ,  Yuval ZAK ,  Ronit BUSTIN ,  Ofer SARAF

IPC: G05D1/00 ,  G06F16/901

Abstract: A method includes generating a data structure including states and actions to be executed at those states as determined by an AI policy of behavior, determining, with a first computing system that is offline, state factors associated with the states and responsibility scores for the state factors, each responsibility score indicating a causal impact for each of the actions associated with one of the states, generating, with the first computing system, a causal ML model based on the state factors and the responsibility scores, determining, with a second computing system that is online based on the causal ML model, state factors associated with a current state, and identifying one or more of the state factors as a causal reason for an action resulting from the current state. Other example methods and systems for providing explanation of an AI policy of behavior with causal reasoning are also disclosed.

公开(公告)号：US20250103575A1

公开(公告)日：2025-03-27

申请号：US18475780

申请日：2023-09-27

Applicant: GM Global Technology Operations LLC

Inventor： Ronit BUSTIN ,  Claudia Goldman-Shenhar

IPC: G06F16/22

Abstract: A method for probability tree reduction includes receiving a probability tree structure with a plurality of nodes. At least one node structural value is associated with each of the plurality of nodes and quantifies an entropy of a subtree extending from a corresponding one of the plurality of nodes. The method further includes receiving at least one parameter for removing one or more nodes of the probability tree structure, removing at least one node of the plurality of nodes from the probability tree structure according to the parameter, calculating an updated entropy for each of the plurality of nodes upstream from the removed node, and outputting a reduced probability tree structure without the removed node and with the updated entropy for each of the plurality of nodes upstream from the removed node. Other example methods and systems for probability tree reduction are also disclosed.