SRI International

SRI International - American Nonprofit Research Institute

Artificial intelligence

SRI develops methods for building intelligent systems that learn, perceive and interact with the world both autonomously and in collaboration with humans. Our researchers create leading-edge technologies and explore their deployment across a broad range of applications.

CONTACT US

Core AI technologies and applications

At SRI, our researchers work together across disciplines to deliver on the promise of AI and machine learning. We create leading-edge technologies and explore their deployment across a broad range of application areas.

SENSING AND ANALYTICS

Scene recognition and understanding

Person recognition and tracking

Mapping and localization

Text understanding and summarization

Predictive analytics

HUMAN-SYSTEM INTERACTION

Conversational assistants

Intent recognition

Explainable systems

Mixed-initiative planning

Supervised autonomy

REASONING AND PROBLEM SOLVING

Adaptive planning and resource allocation

Multi-agent coordination

Decision-making under uncertainty

Bioinformatics

MACHINE LEARNING

Deep learning for language, image processing

Reinforcement learning

Learning from demonstration

Hybrid logic/learning architectures

Learning causal models

Our work

more +

Bioinformatics databases

BioCyc database collection

A comprehensive website for sharing fundamental information about biochemical pathways and genomes with researchers around the world

EcoCyc: encyclopedia of E. coli genes and metabolism

A bioinformatics database that describes the genome, metabolic network and regulatory network of Escherichia coli

A powerful legacy; a visionary future

SRI’s Artificial Intelligence Center has driven seminal breakthroughs in AI since the earliest days of the field, including the first mobile-robot (Shakey) and the widely used A* and RANSAC algorithms. Our researchers build on this legacy as we continue to push the boundaries of AI in autonomy, bioinformatics, personal assistants and machine learning.

Learn more about our contributions.

Artificial intelligence leadership

Featured publications

more +

“SRI’s got that rare mix of people, creativity, technical diversity and excellence, and flexibility to pursue longer-range goals.  Of course there are no guarantees, but SRI provides the tools for a motivated person to make things happen.”

Eric Yeh

Senior Computer Scientist

sri_ericyeh_profile

Latest publications

more +

  • Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments

    We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments. We consider the problem of controlling a sensor that makes partial observations in some space of interest such that it maximizes information about entities present in that space.

  • Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning

    Abstract In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly […]

  • A Framework for understanding and Visualizing Strategies of RL Agents

    Abstract Recent years have seen significant advances in explainable AI as the need to understand deep learning models has gained importance with the increased emphasis on trust and ethics in AI. Comprehensible models for sequential decision tasks are a particular challenge as they require understanding not only individual predictions but a series of predictions that […]

  • Experimental Evaluation of Subject Matter Expert-Oriented Knowledge Base Authoring Tools

    We describe a large-scale experiment in which non-artificial intelligence subject matter experts (SMEs)—with neither artificial intelligence background nor extensive training in the task—author knowledge bases (KBs) following a challenge problem specification with a strong question-answering component.

  • VALET:  Rule-Based Information Extraction for Rapid Deployment

    We present VALET, a framework for rule-based information extraction written in Python. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets.

  • An Ontology-Based Dialogue Management Framework for Virtual Personal Assistants in Common Lisp

    We present a new approach to dialogue specification for Virtual Personal Assistants (VPAs) based on so-called dialogue workflow graphs. Our approach relies on Semantic Web technology (OWL), implemented in Common Lisp with the help of the Racer reasoner.