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About

Thomas Lukasiewicz is a versatile computer scientist whose research spans knowledge representation, reasoning under uncertainty, and brain-inspired machine learning. He has made foundational contributions to the formalization of action reasoning, particularly through his development of the action language **E**, which enables agents to reason about sensing actions under both qualitative and probabilistic uncertainty using autoepistemic description logics. This work, appearing in multiple influential forms since 2004 and accumulating nearly 50 citations, established rigorous frameworks for handling incomplete and uncertain information in dynamic environments. His game-theoretic agent programming language **GTGolog** further extended this vision to multi-agent settings, elegantly combining Golog-style explicit programming with Markov game planning. More recently, Lukasiewicz has emerged as a prominent voice in the push to move deep learning beyond backpropagation. His highly cited explorations of **predictive coding**—a biologically plausible alternative to error backpropagation—have attracted significant attention, with related papers accumulating over 44 citations since 2022 alone. These works position predictive coding as a scalable, brain-inspired learning paradigm with transformative potential for next-generation AI. Together, his contributions reflect a career dedicated to building more principled, explainable, and cognitively grounded intelligent systems.