Jiayuan Mao aims to build intelligent agents that continually learn, reason, and plan: agents that can answer queries, infer human intentions, and pursue long-horizon goals spanning hours to days. In this talk, she will describe a general framework for learning and reasoning with neuro-symbolic concepts. Drawing inspirations from theories and studies in cognitive science, neuro-symbolic concepts serve as compositional abstractions of the physical world, representing object properties, relations, and affordances, and actions. Each concept combines symbolic programs, which specify how concepts can be structured and composed, with neural networks, which ground them in sensory inputs and agent actions. Like words combining into sentences, these concepts can be reused and recombined to support reasoning and planning in novel situations. Dr. Mao shows that systems that leverage neuro-symbolic concepts demonstrate superior data efficiency, enable agents to reason and plan more quickly, and achieve strong generalization in novel situations and for novel goals.

https://upenn.zoom.us/j/97645937545

Meeting ID: 976 4593 7545