I am Xi Li (李熙), an Associate Professor of Logic at the Central South University (中南大学), Department of Philosophy. I received my PhD in Logic from Peking University (北京大学) in 2015.

My research lies at the intersection of mathematical logic, algorithmic information theory, causal inference, and the philosophical foundations of artificial intelligence. I am particularly interested in questions about what it means for a machine—or a mind—to learn, reason, act, and self-improve under uncertainty.

Research Interests

• Kolmogorov Complexity — the study of the minimal description length of objects; a rigorous, machine-independent measure of randomness and information content. My work explores its connections to computability theory, probabilistic inference, and the nature of mathematical truth.
• Solomonoff Induction — the theoretically optimal framework for universal inductive inference, grounded in algorithmic probability. I examine its philosophical implications for Bayesian epistemology and the limits of machine learning.
• Universal Reinforcement Learning — extending Solomonoff's ideas to sequential decision-making. I investigate whether agents that maximise expected reward in all computable environments are a coherent model of rational agency.
• Causal Inference — the formal analysis of cause-and-effect relationships using causal graphs and structural causal models. I am interested in the metaphysical foundations of causal inference, and what this framework means for autonomous AI agents that must act and make decisions in the real world.
• Rational Decision Theory — classical and non-classical frameworks for decision under uncertainty, including evidential vs causal vs functional decision theory, and logical uncertainty.
• Philosophy of Artificial Intelligence — foundational questions about machine intelligence: Can machines understand? What is intelligence? How should we evaluate AI systems philosophically rather than just empirically?

Teaching

At CSU I regularly teach:

• Mathematical Logic — propositional logic, boolean algebra, predicate logic, modal logic, and computability
• Philosophy of Artificial Intelligence — an interdisciplinary course bridging analytic philosophy, cognitive science, and AI theory, with a focus on complexity, induction, causation, emergence, agency, consciousness, and the ethics of AI

Course materials, slides, and reading lists are available on the Teaching page.

Selected Publications

My recent work addresses the philosophical foundations of machine learning, the relationship between Kolmogorov complexity and inductive bias, and causal accounts of rational agency. Some of my publications, with abstracts and download links, are on the Research page.

Contact

I welcome inquiries from students and researchers interested in logic, algorithmic information theory, causal inference, or the philosophy of AI. The best way to reach me is by email or through the Guestbook.