- 강의실: 공학 3관 xxx호
- Textbooks
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Third edition), Prentice Hall, 2010 [book]

- Textbooks (additional)
- D. L. Poole, A. K. Mackworth, Artificial Intelligence: Foundations of Computational Agents, (2nd edition), Cambridge University Press, 2017 [book]
- I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT Press, 2016 [book]
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998 [link] [2nd_edition]
- D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009 [book]

- References

- Lecture 1: Introduction [pdf]
- Lecture 2: Rational agents [pdf]
- Intelligent Agent [pdf]
- Lecture 3: Problem solving by search [pdf]
- [lecture at Brown U] [pdf]
- [Russell] Problem solving and search [pdf]
- [Russell] Problem solving and search [pdf]
- Lecture 4: Informed search [pdf]
- Beyond classical search [pdf]
- A* Parsing (by D. Klein and C. Manning) [pdf]
- [Lecture at Brown U]: Informed search [pdf]
- [Russell] Informed search [pdf], Local search [pdf]
- Lecture 5: Constraint satisfaction problems [pdf] [pdf]
- [Sandholm] Constraint satisfaction problems [pdf]
- [Russell] Constraint satisfaction problems [pdf]
- [Ref.] Cheeseman, P., Kanefsky, B., & Taylor, W. (1991). Where the really hard problems are. IJCAI '91 [pdf]
- Lecture 6: Adversarial search [pdf] [pdf]
- [Lecture at Brown U] Adversarial search [pdf]
- [Russell] Game playing [pdf], AlphaGo [pdf], MoGo [pdf]
- Lecture 7: Propositional logic [pdf]
- Lecture 8: Satisfiability and model construction [pdf] [pdf]
- Lecture 9: Predicate logic [pdf]
- SWI-Prolog [link], Sample [pdf]
- Prolog Tutorial [link] [pdf][pdf]
- [Lecture at Brown U] [pdf]
- [Russell] First order logic [pdf]
- Lecture 10: Knowledge_representation [pdf]
- Lecture 11: Modern knowledge base: Introduction [pdf], YAGO [pdf], KnowItAll
- YAGO: A core of semantic knowledge unifying WordNet and Wikipedia, WWW '07 [pdf][link]
- YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia, AI '13 [pdf]
- Knowledge Vault, KDD '14 [pdf]
- KnowItAll, WWW '04 [pdf]
- Lecture 12: Classical Planning [pdf]
- AAAI '17 tutorial: Planning and Robotics[pdf]
- ICAPS Competitions [link]
- [Ref] B. Bonet, H. Geffner, Planning as heuristic search, AI '01 [pdf]
- [Ref] J. Hoffmann, B. Nebel, The FF Planning Systems, JAIR '01 [pdf]
- [Ref] Action Planning: Computational Complexity [pdf]
- [Lecture at Brown U] [pdf]
- Lecture 13: Modern board game AI - Monte Carlo Tree Search [pdf] [pdf]
- Lecture 14: RL - Introduction [pdf]
- Lecture 15: RL - Markov Decision Process[pdf]
- Lecture 16: RL - Planning in Markov Decision Process [pdf]
- Lecture 17: RL - Monte Carlo learning [pdf]
- Lecture 18: RL - Temporal difference learning (Q-learning, SARSA)[pdf]
- Lecture 19: RL - Value function approximation [pdf]
- Lecture 20: RL - Deep Q learning [pdf]
- Lecture 21: RL - Policy gradient [pdf] [pdf]
- Lecture 22: RL - Summary [pdf]
- Lecture 23: Neural networks [pdf]
- Lecture 24: Probabilistic reasoning [pdf]
- Lecture 25: Probabilistic graphical model [pdf]
- [Ref] Koehn. Bayesian network [pdf]
- Lecture 26: Probabilistic reasoning over time [pdf]
- Lecture 27: Statistical learning [pdf]
- Lecture 28: AI: Philosophical foundations