Colloquium/keynote UICC 2022: Learning Fast and Slow: Levels of Learning in General Autonomous Intelligent Agents

Friday, February 25, 2022, 5:00pm to 6:00pm
University of Iowa Main Library, Shambaugh Auditorium
125 West Washington Street, Iowa City, IA 52242

Abstract: General autonomous intelligent agents with ongoing existence have many challenges when it comes to learning. On the one hand, they must continually react to their environment, focusing their computational resources and using their available knowledge to make the best decision for the current situation. On the other hand, they need to learn everything they can from their experience, building up their knowledge so that they are prepared to make decisions in the future. We posit two distinct levels of learning in general autonomous intelligent agents. Level 1 (L1) are architectural learning mechanisms that are innate, automatic, effortless, and outside of the agent’s control. Level 2 (L2) are deliberate learning strategies that are controlled by the agent's knowledge, whose purpose is to create experiences for L1 mechanisms to learn from.

We describe these levels and provide examples from our research in interactive task learning (ITL), where an agent learns a novel task through natural interaction with an instructor. ITL is challenging because it requires a tight integration of many of the cognitive capabilities embodied in human-level intelligence: multiple types of reasoning, problem solving, and learning; multiple forms of knowledge representations; natural language interaction; dialog management; and interaction with an external environment – all in real time. Moreover, any successful approach must be general – the agent cannot be pre-engineered with the knowledge for a given task – everything about a task must be learned or transferred from other tasks.

Our agent builds on our research with the Soar cognitive architecture, using a combination of innate L1 mechanisms and L2 strategies to learn ~60 puzzles and games, as well as mobile robotic tasks. Our agent is embodied in a tabletop robot, a small mobile robot, and a Fetch robot. This research is supported by ONR and AFOSR.

Bio: John E. Laird is the John L. Tishman Professor of Engineering at the University of Michigan, where he has been since 1986. He received his Ph.D. in Computer Science from Carnegie Mellon University in 1983 working with Allen Newell. From 1984 to 1986, he was a member of research staff at Xerox Palo Alto Research Center. He is one of the original developers of the Soar architecture and leads its continued evolution. He is a founder of Soar Technology, Inc., and the Center for Integrated Cognition. He is a Fellow of AAAI, AAAS, ACM, and the Cognitive Science Society. In 2018, he was awarded the Herbert A. Simon Prize for Advances in Cognitive Systems.

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Full The University of Iowa Computing Conference 2022 details here.

Individuals with disabilities are encouraged to attend all University of Iowa–sponsored events. If you are a person with a disability who requires a reasonable accommodation in order to participate in this program, please contact Mitchell Hermon in advance at 515-822-5084 or