Erik T. Mueller

Erik T. Mueller


Better Computers for a More Caring World

My life's work is about using computers to create a more caring world for people. I've done this work in three realms: artificial intelligence, human-computer interaction, and the performing arts. To learn more about my projects and goals than I can explain in this brief summary, you can order my books. I hope you'll get in touch if you're interested in my ideas.

Artificial Intelligence

My book Daydreaming in Humans and Machines presents a computer model of the human stream of thought, or daydreaming. The model demonstrates how daydreaming can make us more creative, help us learn from past mistakes, help us plan for the future, and improve our emotional intelligence.

Computers often make mistakes because they don't understand much about the human world. My work on commonsense reasoning and commonsense architectures is concerned with developing techniques to give computers more common sense.

Human-Computer Interaction

My book Computers and Caring is about building digital assistants that teach us how to be more caring to ourselves, others, and the environment.

I was originally attracted to computers because of their simplicity and beauty. I remember being fascinated by symbols like ∇ in APL and constructs like the λ expression in Lisp. But now computers have become too complex and unappealing. My book Transparent Computers and my WIRED article are about how to make computers simple and beautiful again, which makes them more transparent and user-friendly. I discuss this in my video on Transparent Computers.

My work on Watson for Healthcare is concerned with building intelligent assistants that improve healthcare. This technology helps medical professionals with diagnosis and treatment. I discuss this in my video on Going Beyond Fact-Based Question Answering.

Performing Arts

The theme of using computers to create a more caring world for people is also reflected in my work in the performing arts. In my web series Wall Geek, the characters build The Niceness Exchange, a trading system that incentivizes being nice. My solo show The Computer That Loved traces my lifelong interest in the interaction between computers and people.


Erik T. Mueller is Founder and CEO of Symbolic AI, LLC. Previously, Mueller was a Research Staff Member at IBM Research where he developed artificial intelligence systems including the Watson Jeopardy! system, Watson for Healthcare, WatsonPaths, dialogue systems, and needs-based recommendation systems. He won the AAAI Feigenbaum Prize with the IBM Watson Team. He has 8 patents on AI, with 6 more pending, and is the author of several books on artificial intelligence. He has a Ph.D. and M.S. in computer science from UCLA and an S.B. in computer science from MIT.



How can computers reason like humans? Commonsense Reasoning How can computers understand language? Natural Language Processing with ThoughtTreasure
How can computers be more transparent? Transparent Computers What would it be like to speak fluent French? Fluent French
How can computers daydream? Daydreaming in Humans and Machines How can computers help us be more caring? Computers and Caring



  1. Mueller, Erik T. (2015, November 13). Google's TensorFlow alone will not revolutionize AI. WIRED.
  2. Mueller, Erik T., & Minsky, Henry (2015). Using thought-provoking children's questions to drive artificial intelligence research. CoRR, abs/1508.06924.
  3. Mueller, Erik T. (2015). Commonsense reasoning: An event calculus based approach (2nd Ed.). Waltham, MA: Morgan Kaufmann/Elsevier.
  4. Mueller, Erik T. (2015). Computers and caring: Using technology to help us care.
  5. Lally, Adam,  Bagchi, Sugato,  Barborak, Michael A.,  Buchanan, David W.,  Chu-Carroll, Jennifer,  Ferrucci, David A.,  Glass, Michael R.,  Kalyanpur, Aditya,  Mueller, Erik T.,  Murdock, J. William,  Patwardhan, Siddharth,  Prager, John M.,  & Welty, Christopher A. (2014). WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information. Research Report RC25489 (WAT1409-048), IBM Research, Yorktown Heights, NY.
  6. Ferrucci, David,  Levas, Anthony,  Bagchi, Sugato,  Gondek, David,  & Mueller, Erik T. (2013). Watson: Beyond Jeopardy! Artificial Intelligence, 199-200, 93-105.
  7. Mueller, Erik T. (2013). Computational models of narrative. Sprache und Datenverarbeitung (International Journal for Language Data Processing), 37(1/2), 11-39.
  8. Hajishirzi, Hannaneh, & Mueller, Erik T. (2012). Question answering in natural language narratives using symbolic probabilistic reasoning. In H. Chad Lane, G. Michael Youngblood, & Philip McCarthy (Eds.), Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: AAAI Press.
  9. Hajishirzi, Hannaneh,  Amir, Eyal,  Mueller, Erik T.,  & Hockenmaier, Julia (2011). Reasoning about RoboCup soccer narratives. Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence. Corvallis, Oregon: AUAI Press.
  10. Hajishirzi, Hannaneh, & Mueller, Erik T. (2011). Symbolic probabilistic reasoning for narratives. In Ernest Davis, Patrick Doherty, & Esra Erdem (Eds.), Logical Formalizations of Commonsense Reasoning: Papers from the 2011 AAAI Spring Symposium. Technical Report SS-11-06. Menlo Park, CA: AAAI Press.
  11. Finlayson, Mark,  Gervás, Pablo,  Mueller, Erik T.,  Narayanan, Srini,  & Winston, Patrick (Eds.). (2010). Computational Models of Narrative: Papers from the 2010 AAAI Fall Symposium. Technical Report FS-10-04. Menlo Park, CA: AAAI Press.
  12. Mueller, Erik T. (2009). Automating commonsense reasoning using the event calculus. Communications of the ACM, 52(1), 113-117.
  13. Havasi, Catherine,  Lieberman, Henry,  & Mueller, Erik T. (2009). CSIUI 2009: Story understanding and generation for aware and interactive interface design. Proceedings of the 2009 International Conference on Intelligent User Interfaces (p. 491). New York: Association for Computing Machinery.
  14. Mueller, Erik T. (2008). Event calculus. In Frank van Harmelen, Vladimir Lifschitz, & Bruce Porter (Eds.), Handbook of Knowledge Representation (pp. 671-708). Amsterdam: Elsevier.
  15. Mueller, Erik T. (2007). Modelling space and time in narratives about restaurants. Literary and Linguistic Computing, 22(1), 67-84.
  16. Hillis, Danny,  McCarthy, John,  Mitchell, Tom M.,  Mueller, Erik T.,  Riecken, Doug,  Sloman, Aaron,  & Winston, Patrick Henry (2007). In honor of Marvin Minsky's contributions on his 80th birthday. AI Magazine, 28(4), 103-110.
  17. Mueller, Erik T. (2007). Understanding goal-based stories through model finding and planning. In Brian S. Magerko & Mark O. Riedl (Eds.), Intelligent Narrative Technologies: Papers from the AAAI Fall Symposium (pp. 95-101). Technical Report FS-07-05. Menlo Park, CA: AAAI Press.
  18. Mueller, Erik T. (2007). Discrete event calculus with branching time. In Eyal Amir, Vladimir Lifschitz, & Rob Miller (Eds.), Logical Formalizations of Commonsense Reasoning: Papers from the 2007 AAAI Spring Symposium (pp. 126-131). Technical Report SS-07-05. Menlo Park, CA: AAAI Press.
  19. Mueller, Erik T. (2006). Commonsense reasoning. San Francisco: Morgan Kaufmann/Elsevier.
  20. Mueller, Erik T. (2006). Event calculus and temporal action logics compared. Artificial Intelligence, 170(11), 1017-1029.
  21. Mueller, Erik T., & Sutcliffe, Geoff (2005). Reasoning in the event calculus using first-order automated theorem proving. In Ingrid Russell & Zdravko Markov (Eds.), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (pp. 840-841). Menlo Park, CA: AAAI Press.
  22. Mueller, Erik T., & Sutcliffe, Geoff (2005). Discrete event calculus deduction using first-order automated theorem proving. In Boris Konev & Stephan Schulz (Eds.), Proceedings of the Fifth International Workshop on the Implementation of Logics (pp. 43-56). Montevideo, Uruguay. Technical Report ULCS-05-003, Department of Computer Science, University of Liverpool.
  23. Mueller, Erik T. (2004). Event calculus reasoning through satisfiability. Journal of Logic and Computation, 14(5), 703-730.
  24. Mueller, Erik T. (2004). Understanding script-based stories using commonsense reasoning. Cognitive Systems Research, 5(4), 307-340.
  25. Mueller, Erik T. (2004). A tool for satisfiability-based commonsense reasoning in the event calculus. In Valerie Barr & Zdravko Markov (Eds.), Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (pp. 147-152). Menlo Park, CA: AAAI Press.
  26. Mueller, Erik T. (2003). Story understanding through multi-representation model construction. In Graeme Hirst & Sergei Nirenburg (Eds.), Text Meaning: Proceedings of the HLT-NAACL 2003 Workshop (pp. 46-53). East Stroudsburg, PA: Association for Computational Linguistics.
  27. McCarthy, John,  Minsky, Marvin,  Sloman, Aaron,  Gong, Leiguang,  Lau, Tessa,  Morgenstern, Leora,  Mueller, Erik T.,  Riecken, Doug,  Singh, Moninder,  & Singh, Push (2002). An architecture of diversity for commonsense reasoning. IBM Systems Journal, 41(3), 530-539.
  28. Mueller, Erik T. (2002). Story understanding. In Lynn Nadel (Ed.), Encyclopedia of Cognitive Science (Vol. 4, pp. 238-246). London: Nature Publishing Group.
  29. Singh, Push,  Lin, Thomas,  Mueller, Erik T.,  Lim, Grace,  Perkins, Travell,  & Zhu, Wan Li (2002). Open Mind Common Sense: Knowledge acquisition from the general public. In Robert Meersman & Zahir Tari (Eds.), Lecture Notes in Computer Science: Vol. 2519. On the Move to Meaningful Internet Systems 2002: DOA/CoopIS/ODBASE 2002 (pp. 1223-1237). Berlin: Springer.
  30. Mueller, Erik T. (2001). Machine-understandable news for e-commerce and web applications. Proceedings of the 2001 International Conference on Artificial Intelligence (pp. 1113-1119). CSREA Press.
  31. Mueller, Erik T. (2000). A calendar with common sense. Proceedings of the 2000 International Conference on Intelligent User Interfaces (pp. 198-201). New York: Association for Computing Machinery.