公开(公告)号：US20240169129A1

公开(公告)日：2024-05-23

申请号：US18512812

申请日：2023-11-17

Applicant: SRI International

Inventor： Adam Derek Cobb ,  Daniel Elenius ,  Anirban Roy ,  Patrick Denis Lincoln ,  Susmit Jha

IPC: G06F30/27

CPC classification number: G06F30/27 ,  G06F2119/02

Abstract: In an example, an iterative method for generating designs includes receiving, by a computing system, a plurality of symbolic rules and a plurality of design objectives for a design of a system; generating, by the computing system, a first plurality of designs for the system based on the plurality of the symbolic rules; evaluating performance of the first plurality of designs; training a machine learning model using the first plurality of designs and performance metrics; generating a second plurality of designs; evaluating, by the computing system, using a machine learning model, performance of the second plurality of designs to filter one or more designs that meet one or more of the plurality of the design objectives; evaluating performance of the filtered designs; and updating, by the computing system, the plurality of the design objectives and/or the plurality of the symbolic rules based on the evaluated performance of the filtered designs.

公开(公告)号：US20210398000A1

公开(公告)日：2021-12-23

申请号：US17304448

申请日：2021-06-21

Applicant: SRI International

Inventor： Grit Denker ,  Daniel Elenius ,  Karsten Martiny

IPC: G06N5/04 ,  G06N5/02

Abstract: In general, the disclosure describes various aspects of techniques for explaining results provided by automated decision systems. A device comprising a memory and a computation engine executing one or more processor may be configured to perform the techniques. The memory may store an automated reasoning engine. The computation engine may execute the automated reasoning engine to obtain a query, obtain, from a knowledge base, and responsive to the query, a knowledge base entity representative of an explicit fact or a rule, and determine, based on the knowledge base entity, the query result that provides a decision to the query. The automated reasoning engine may also obtain provenance information that explains a history for the knowledge base entity, determine, based on the provenance information, an explanation that explains a difference between the query result and a previous query result provided with respect to the query, and output the explanation.

公开(公告)号：US11263339B2

公开(公告)日：2022-03-01

申请号：US16560930

申请日：2019-09-04

Applicant: SRI International

Inventor： Grit Denker ,  Karsten Martiny ,  Daniel Elenius

IPC: G06F21/62

Abstract: In general, techniques for data access control are described, in which a policy engine implements and applies a declarative policy framework that can represent and reason about complex privacy policies. By using a common data model together with a formal shareability theory, this declarative policy framework enables the specification of expressive policies in a concise way without burdening the user with technical details of the underlying formalism of a data querying application or other knowledge representation scheme. The policy engine may be deployed as the policy decision point in a data access control system that also includes a policy enforcement point. The policy engine includes user interfaces for the creation, validation, and management of privacy policies. The policy engine may interface with systems that manage data requests and replies by coordinating policy engine decisions and access to databases.

公开(公告)号：US20210142005A1

公开(公告)日：2021-05-13

申请号：US17094278

申请日：2020-11-10

Applicant: SRI International

Inventor： Natarajan Shankar ,  Stephane Graham-Lengrand ,  Daniel Elenius ,  Chih-hung Yeh

IPC: G06F40/211 ,  G06F40/253 ,  G06F40/279 ,  G06N7/00 ,  G06N20/20

Abstract: In general, the disclosure describes techniques for machine learning for translation to structured computer readable representation. An example method to generate a training set for a natural language translation model includes receiving, by a computing system, a grammar comprising rules, one or more of the rules being associated with random biases; generating, by the computing system, at least one of random trees or random graphs based on the random biases in the grammar; for each of the random trees or random graphs, by the computing system, generating a natural language sample; and generating, by the computing system, the training set with the random trees or random graphs and the corresponding natural language samples.

公开(公告)号：US20240312129A1

公开(公告)日：2024-09-19

申请号：US18509024

申请日：2023-11-14

Applicant: SRI International

Inventor： Anirban Roy ,  Adam Derek Cobb ,  Daniel Elenius ,  Patrick Denis Lincoln ,  Susmit Jha

IPC: G06T17/00 ,  G06V10/426

CPC classification number: G06T17/00 ,  G06V10/426

Abstract: In an example, a method for adapting a machine learning model includes receiving a digital representation of a three-dimensional (3D) object; learning, using a surrogate model, relationships between a plurality of points on a surface of the 3D object; and generating, using the surrogate model, one or more predictions about fluid properties along the surface of the 3D object.

公开(公告)号：US20240143689A1

公开(公告)日：2024-05-02

申请号：US18489777

申请日：2023-10-18

Applicant: SRI International

Inventor： Susmit Jha ,  Adam Derek Cobb ,  Anirban Roy ,  Daniel Elenius ,  Patrick Denis Lincoln

IPC: G06F17/11

CPC classification number: G06F17/11

Abstract: In an example, a method of designing a system or architecture includes, receiving a plurality of parameter values and a set of requirements for a plurality of objective functions related to a design problem; compressing the plurality of parameters to generate a latent representation; forward processing, with one or more Invertible Neural Networks (INNs), the latent representation to generate a plurality of objective values corresponding to the plurality of the objective functions; inverse processing the plurality of objective values; and generating, based on the latent representation, a plurality of solutions to the design problem that satisfy the set of requirements for the plurality of objective functions.