Ph.D. HCC — AI Reading List

Background Readings 

  • To read and understand the papers below, you should be familiar with the content of an AI courses such as CS 3600 or CS 6601. 

  • The standard AI textbook is: Stuart Russell & Peter Norvig. (2021) Artificial Intelligence: A Modern Approach Prentice-Hall, 4th edition. 

  • In particular, you should know the material in: 

  • Chapter 3: Solving Problems by Search  

  • Chapter 7: Logical Agents  

  • Chapter 8: First-Order Logic 

  • Chapter 11: Automated Planning  

  • Chapter 12: Quantifying Uncertainty 

  • Chapter 13: Probabilistic Reasoning 

  • Chapter 14: Probabilistic Reasoning over time 

  • Chapter 19: Learning from Examples 

  • Note that essentially the same material is covered in the 3rd edition, although the chapter numbering is different. 

Knowledge Representation [7] 

  • Davis, R.; Shrobe, H.; and Szolovits, P. 1993. What is a knowledge representation? AI Magazine 14(1):17–33. 

  • Newell. (1982) The Knowledge Level. AI Magazine. 2,2 (Summer 1981),1-20. 

  • Frames and semantic nets 

  • Minsky, M. (1974). A framework for representing knowledge.  

  • Ontologies 

  • D. Lenat & R. Guha (1990), Building Large Knowledge-Based Systems, chapters 1-2, Addison-Wesley. 

  • B. Chandrasekaran, J. Josephson & V. Benjamins. (1999) What are ontologies and why do we need them? IEEE Intelligent Systems 14(1) 20-26 

  • Cognitive architecture 

  • Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 13-26.  

  • Critique of GOFAI 

  • Brooks, R. A. (1990). Elephants don’t play chess. Robotics and Autonomous Systems, 6 

Inference & Reasoning [6] 

  • Probabilistic graphical models 

  • Embedding/neural?/learned distributed representations 

  • Case-based reasoning 

  • William Murdock & Ashok Goel Meta-Case-Based Reasoning: Self-Improvement through Self-Understanding JETAI (2008) 20:1-38. 

  • Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial intelligence review, 6(1), 3-34. 

  • Logical inference and reasoning 

  • Forbus, K. D. (1984). Qualitative process theory. Artificial intelligence, 24(1-3), 85-168. 

  • Schank, R & Abelson, R. (1997). Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures. Chapters 1-3. 

Planning & Problem Solving [3] 

  • Search 

  • Classical (state- and plan-space) Planning 

  • Daniel S. Weld: Recent Advances in AI Planning. AI Magazine 20(2): 93-123 (1999) 

  • Hierarchical planning 

  • Langley, P., & Shrobe, H. E. (2021). Hierarchical problem networks for knowledge-based planning. Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems. 

Learning [7] 

  • ML Basics (i.e., classification/regression) 

  • Pedro Domingos A Few Useful Things to Know about Machine Learning CACM 55(10): 78-87, 2012 

  • Backpropagation and neural network learning 

  • Duda, R. O., et al. (2001). Pattern recognition (2nd ed.). New York: Wiley. Chapter 6: Multilayer Neural Networks. 

  • Reinforcement Learning 

  • Sutton & Barto (2020) Reinforcement Learning: An Introduction (2nd edition). Chapter 3 (Finite Markov Decision Processes) and Chapter 6 (Temporal-Difference Learning) 

  • Inductive Logic Programming and Relational Learning 

  • Cropper, A., Dumančić, S., & Muggleton, S. H. (2020). Turning 30: New ideas in inductive logic programming. Pdf: https://arxiv.org/abs/2002.11002 

  • Probabilistic Programming (i.e., Bayesian Learning) 

  • Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338. 

  • Human-in-the-Loop / interactive Learning 

  • Simard, P. Y., Amershi, S., Chickering, D. M., Pelton, A. E., Ghorashi, S., Meek, C., ... & Wernsing, J. (2017). Machine teaching: A new paradigm for building machine learning systems. arXiv preprint arXiv:1707.06742. 

  • Critique of data-driven systems 

  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623). 

AI & Humans [11] 

  • Licklider, J. C. (1960). Man-computer symbiosis. IRE transactions on human factors in electronics, (1), 4-11. 

  • Human-AI Interaction 

  • Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020, April). Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 chi conference on human factors in computing systems (pp. 1-13). 

  • MacLellan, C. J., Harpstead, E., Marinier III, R. P., & Koedinger, K. R. (2018). A framework for natural cognitive system training interactions. Advances in Cognitive Systems, 6, 1-16. 

  • Shneiderman, B., & Maes, P. (1997). Direct manipulation vs. interface agents. Interactions, 4(6), 42–61. 

  • Nass, C., Steuer, J., & Tauber, E. R. (1994). Computers are social actors. Conference Companion on Human Factors in Computing Systems - CHI 94. doi: 10.1145/259963.260288 

  • Ernesto Arias, Hal Eden, and Gerhard Fisher. 1997. Enhancing communication, facilitating shared understanding, and creating better artifacts by integrating physical and computational media for design. In Proceedings of the 2nd conference on Designing interactive systems: processes, practices, methods, and techniques (DIS '97). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/263552.263553 

  • Mueller, S. T., Hoffman, R. R., Clancey, W., Emrey, A., & Klein, G. (2019). Explanation in human-AI systems: A literature meta-review, synopsis of key ideas and publications, and bibliography for explainable AI. 

  • AI & Ethics 

  • Costanza-Chock, S. (2018). Design justice, AI, and escape from the matrix of domination. Journal of Design and Science, 3(5). 

  • Computational Creativity 

  • Cohen, P. (2016). Harold Cohen and AARON. AI Magazine, 37(4), 63-66. 

  • McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. In Computational Intelligence in Music, Sound, Art and Design: 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings 8 (pp. 35-50). Springer International Publishing. https://arxiv.org/pdf/1903.02166.pdf 

  • Weisz, J. D., Muller, M., He, J., & Houde, S. (2023). Toward General Design Principles for Generative AI Applications. https://arxiv.org/abs/2301.05578