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Bio-Inspired AI Research
ELBICA lab: Enhancing Lives with Bio-Inspired Computational Approaches
Our overall goal is to develop state-of-the-art computational approaches to understand where meaningful decisions come from and to use this knowledge to enhance human-technology partnerships through the development of bio-inspired AI systems, data visualization tools, and web services that promote inclusion and assist people.
We build Artificial Intelligence (AI) systems that use reinforcement learning techniques (RL) and bio and cognitive inspiration. A cognitively inspired computational architecture is a means through which autonomous agents make decisions using internal representations of information modeled after a human-like structure: such as using emotions as an attentional mechanism that impacts decision-making or learning from internal visual representations and previous experiences. This approach helps us understand where meaningful decisions come from and create AI systems that assist humans in making better decisions. Our research methods focus on combining bio-inspired computational approaches with RL techniques to enable human-inspired AI systems to learn and solve tasks via experience.