While subsymbolic modeling (machine learning, large language models) excels at capturing statistical patterns of the world from historical data, symbolic modeling (constraint
satisfaction, SAT solving, planning, knowledge graphs) provides efficient reasoning on
top of explicitly captured mechanics of the world with various levels of abstraction. The
efficient interleaving of these two paradigms remains an open research challenge — one
that underpins the next evolution of composite AI, neurosymbolic AI, and, in contemporary terms, agentic AI. In this talk, we provide an overview of the state of the agentic AI field, and we dive into the principles and techniques developed and deployed to
production by Filuta AI across industries such as energy, logistics, automotive, dual-use,
and digital entertainment.
20.11.2025, 16.15, A-312, FEL ČVUT, Karlovo náměstí 13, Praha 2, online: https://www.praguecomputerscience.cz/
