Robust Intelligence (RI)
See program guidelines for contact information.
The Robust Intelligence (RI) program encompasses all aspects of the computational understanding and modeling of intelligence in complex, realistic contexts. In contrast to systems that use limited reasoning strategies or address problems in narrow unchanging contexts, robust intelligence may be characterized by flexibility, resourcefulness, creativity, real-time responsiveness and long-term reflection, use of a variety of modeling or reasoning approaches, ability to learn and adapt performance at a level of intelligence seen in humans and animals, and awareness of and competence in larger natural, built, and social contexts. The RI program advances and integrates the research traditions of artificial intelligence, computer vision, human language research, robotics, machine learning, computational neuroscience, cognitive science, and related areas.
Researchers across all areas of RI are addressing progressively richer environments, larger-scale data and more diverse computing platforms, and more sophisticated computational and statistical approaches, looking to nature in many cases to model cognitive and computational processes. Interactions across traditional disciplines are also of increasing importance. For example, speech and dialogue research seeks to understand the cognitive psychological underpinnings of conversation that contribute to the robustness of human speech perception and intention understanding. Computer vision is exploring approaches developed in language processing to represent the semantic information in images and video in ways useful for mining, navigation, and robotic interaction, and working with ideas developed in computer graphics and physics-based modeling to understand and depict collections of images. Language and vision can be used together in a complimentary way to enhance understanding, for instance, of an image with use of text that discusses or describes it. A cognitive architecture may bridge sophisticated planning and problem solving modules with perception and action modules, perhaps accounting for certain human or animal behaviors and the ways in which they are learned and applied in new contexts. Multi-agent systems may need to tackle planning and learning as well as coordination in novel environments. Robotic systems need to understand and interact with humans in unfamiliar, unstructured, and dynamic environments. Computational understanding of neurons, networks, and the brain increasingly draws on computer vision, robotics, and machine learning, and provides insights into the neural coding, representations, and learning underlying intelligent behavior in nature.
These examples are meant to convey the general goals of RI, not to limit its scope. The program supports projects that will advance the frontiers of all RI research areas, as well as those that integrate different aspects of these fields.
More information on topics of interest to the RI program is available at:
Funding Opportunities for the Robust Intelligence Program:
THIS PROGRAM IS PART OF