Autonomous Learning in Mobile Cognitive Machines
Abstract
Intelligence is a capability ascribed typically to animals, but not usually to plants. Animals can move while plants do not. Is the mobility a necessary condition or driving force for the emergence of intelligence? We hypothesize that mobility plays a foundational role in evolving animal and human intelligence, thus, is fundamentally important in understanding and creating embodied cognitive systems (Clark, 1996). In this project we aim to develop a new class of machine learning algorithms for mobile cognitive systems that actively collect data by sensing and interacting with the environment. We envision a new paradigm of autonomous AI that overcomes the previous AI paradigms of top-down/rule-driven symbolic and bottom-up/data-driven statistical systems. Inspired by the dual process theory of mind (Kahneman, 2011), we develop a dual-memory learning architecture for mobile cognitive machines that combines the hippocampus and cortex models of memory to learn rapidly, flexibly, and robustly. We use mobile robot platforms to investigate the autonomous learning algorithms and demonstrate their capability in real-world home environments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA23861714128
Entities
People
- Byoung-tak Zhang
Organizations
- Air Force Office of Scientific Research
- Seoul National University
- United States Air Force