A Cognitive Architecture for Physical Agents

Icarus is a computational theory of the cognitive architecture that incorporates ideas from multiple traditions, including work on production systems, hierarchical task networks, and logic programming. The framework relies on four assumptions that distinguish it from alternative candidates:

  • Primacy of action and perception over cognition;
  • Separation of categories from skills;
  • Hierarchical structure of long-term memory; and
  • Correspondence between long-term and short-term structures.
  • Our recent papers explain these assumptions and particular abilities in more detail. We have used Icarus to develop a number of synthetic characters for simulated environments, as well as for traditional tasks from the AI and cognitive science literature. Current research includes incorporating mechanisms for forward-chaining problem solving, counterfactual reaing, model-based learning from delayed reward, generating episodic traces, and learning from other agents' behaviors.

    This research has been funded by DARPA IPTO, the Office of Naval Research, and the National Science Foundation. Support for earlier work came from the Air Force Office of Scientific Research, NASA Ames Research Center, and DaimlerChrysler Research and Technology.


    Current Contributors to the Project

  • Nima Asgharbeygi
  • Dongkyu Choi
  • Glenn Iba
  • Wayne Iba
  • Professor Pat Langley
  • Nan Li
  • Negin Nejati
  • Dr. Daniel Shapiro
  • Nishant Trivedi


    Past Contributors to the Project

  • Kamal Ali
  • Dr. Sachiyo Arai
  • Meg Aycinena
  • Gary Cleveland
  • Kirstin Cummings
  • Andreea Danielescu
  • Tom Fawcett
  • Wende Frost
  • Dr. Ryutaro Ichise
  • Anupam Khulbe
  • Chunki Park
  • Dr. Seth Rogers
  • Michael Siliski
  • David Stracuzzi
  • Stefan Tang
  • Jiang Xuan


  • Related Papers

    Iba, W. F., & Langley, P. (in press). Exploring moral reasoning in a cognitive architecture. Proceedings of the Thirty-Third Annual Meeting of the Cognitive Science Society. Boston.

    Langley, P., Trivedi, N., & Banister, M. (2010). A command language for taskable virtual agents. Proceedings of the Sixth Conference Artificial Intelligence and Interactive Digital Entertainment. Stanford, CA: AAAI Press.

    Danielescu, A., Stracuzzi, D. J., Li, N., & Langley, P. (2010). Learning from errors by counterfactual reasoning in a unified cognitive architecture. Proceedings of the Thirty-Second Annual Meeting of the Cognitive Science Society. Portland.

    Li, N., Stracuzzi, D. J., Langley, P., & Nejati, N. (2009). Learning hierarchical skills from problem solutions using means-ends analysis. Proceedings of the Thirty-First Annual Meeting of the Cognitive Science Society. Amsterdam.

    Stracuzzi, D. J., Li, N., Cleveland, G., & Langley, P. (2009). Representing and reasoning over time in a cognitive architecture. Proceedings of the Thirty-First Annual Meeting of the Cognitive Science Society. Amsterdam.

    Konik, T., O'Rorke, P., Shapiro, D., Choi, D., Nejati, N., & Langley, P. (2009). Skill transfer through goal-driven representation mapping. Cognitive Systems Research, 10, 270-285.

    Langley, P., Choi, D., & Rogers, S. (2009). Acquisition of hierarchical reactive skills in a unified cognitive architecture. Cognitive Systems Research, 10, 316-332.

    Li, N., Stracuzzi, D., Cleveland, G., Langley, P., Konik, T., Shapiro, D., Ali, K., Molineaux, M., & Aha, D. (2009). Learning hierarchical skills for game agents from video of human behavior. Proceedings of the IJCAI-09 Workshop on Learning Structural Knowledge from Observations. Pasadena, CA.

    Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10, 141-160.

    Li, N., Stracuzzi, D., & Langley, P. (2008). Learning conceptual predicates for teleoreactive logic programs. Proceedings of the Eighteenth International Conference on Inductive Logic Programming. Prague: Springer.

    Choi, D., Morgan, M., Park, C., & Langley, P. (2007). A testbed for evaluation of architectures for physical agents. Proceedings of the AAAI-2007 Workshop on Evaluating Architectures for Intelligence. Vancouver, BC: AAAI Press

    Langley, P. (2007). Varieties of problem solving in a unified cognitive architecture. Proceedings of the Twenty-Ninth Annual Meeting of the Cognitive Science Society. Nashville, TN.

    Choi, D., Konik, T., Nejati, N., Park, C., & Langley, P. (2007). Structural transfer of cognitive skills. Proceedings of the Eighth International Conference on Cognitive Modeling. Ann Arbor, MI.

    Choi, D., Konik, T., Nejati, N., Park, C., & Langley, P. (2007). A believable agent for first-person shooter games. Proceedings of the Third Annual Artificial Intelligence and Interactive Digital Entertainment Conference (pp. 71-73). Stanford, CA: AAAI Press.

    Langley, P., & Choi, D. (2006). A unified cognitive architecture for physical agents. Proceedings of the Twenty-First National Conference on Artificial Intelligence. Boston: AAAI Press.

    Nejati, N., Langley, P., & Konik, T. (2006). Learning hierarchical task networks by observation. Proceedings of the Twenty-Third International Conference on Machine Learning (pp. 665-672). Pittsburgh, PA.

    Asgharbeygi, N., Stracuzzi, D., & Langley, P. (2006). Relational temporal difference learning. Proceedings of the Twenty-Third International Conference on Machine Learning (pp. 49-56). Pittsburgh, PA.

    Langley, P. (2006). Cognitive architectures and general intelligent systems. AI Magazine, 27, 33-44.

    Langley, P., & Choi, D. (2006). Learning recursive control programs from problem solving. Journal of Machine Learning Research, 7, 493-518.

    Langley, P. (2005). An adaptive architecture for physical agents. Proceedings of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (pp. 18-25). Compiegne, France: IEEE Computer Society Press.

    Langley, P., Choi, D., & Rogers, S. (2005). Interleaving learning, problem solving, and execution in the Icarus architecture (Technical Report). Computational Learning Laboratory, CSLI, Stanford University, CA.

    Choi, D., & Langley, P. (2005). Learning teleoreactive logic programs from problem solving. Proceedings of the Fifteenth International Conference on Inductive Logic Programming (pp. 51-68). Bonn, Germany: Springer.

    Asgharbeygi, N., Nejati, N., Langley, P., & Arai, S. (2005). Guiding inference through relational reinforcement learning. Proceedings of the Fifteenth International Conference on Inductive Logic Programming (pp. 20-37). Bonn, Germany: Springer.

    Langley, P., & Rogers, S. (2005). An extended theory of human problem solving. Proceedings of the Twenty-seventh Annual Meeting of the Cognitive Science Society. Stresa, Italy.

    Langley, P., & Rogers, S. (2004). Cumulative learning of hierarchical skills. Proceedings of the Third International Conference on Development and Learning. San Diego, CA: IEEE Press.

    Langley, P. (2004). Cognitive architectures and the construction of intelligent agents. Proceedings of the AAAI-2004 Workshop on Intelligent Agent Architectures (pp. 82). Stanford, CA.

    Langley, P., Arai, S., & Shapiro, D. (2004). Model-based learning with hierarchical relational skills. Proceedings of the ICML-2004 Workshop on Relational Reinforcement Learning. Banff, Alberta.

    Langley, P., Cummings, K., & Shapiro, D. (2004). Hierarchical skills and cognitive architectures. Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society (pp. 779-784). Chicago, IL.

    Choi, D., Kaufman, M., Langley, P., Nejati, N., & Shapiro, D. (2004). An architecture for persistent reactive behavior. Proceedings of the Third International Joint Conference on Autonomous Agents and Multi Agent Systems (pp. 988-995). New York: ACM Press.

    Langley, P., Choi, D., & Shapiro, D. (2004). A cognitive architecture for physical agents (Technical Report). Computational Learning Laboratory, CSLI, Stanford University, CA.

    Ichise, R., Shapiro, D., & Langley, P. (in press). Structured program induction from behavioral traces. IEICE Transactions on Information and Systems (in Japanese).

    Langley, P., Shapiro, D., Aycinena, M., & Siliski, M. (2003). A value-driven architecture for intelligent behavior. Proceedings of the IJCAI-2003 Workshop on Cognitive Modeling of Agents and Multi-Agent Interactions (pp. 10-18). Acapulco, Mexico.

    Langley, P., & Laird, J. E. (2002). Cognitive architectures: Research issues and challenges (Technical Report). Institute for the Study of Learning and Expertise, Palo Alto, CA.

    Ichise, R., Shapiro, D. G., & Langley, P. (2002). Learning hierarchical skills from observation (pp. 247-258). Proceedings of the Fifth International Conference on Discovery Science.

    Shapiro, D., & Langley, P. (2002). Separating skills from preference: Using learning to program by reward. Proceedings of the Nineteenth International Conference on Machine Learning (pp. 570-577). Sydney: Morgan Kaufmann.

    Shapiro, D., Langley, P., & Shachter, R. (2001). Using background knowledge to speed reinforcement learning in physical agents. Proceedings of the Fifth International Conference on Autonomous Agents (pp. 254-261). Montreal: ACM Press.

    Shapiro, D., & Langley, P. (1999). Controlling physical agents through reactive logic programming. Proceedings of the Third International Conference on Autonomous Agents (pp. 386-387). Seattle: ACM Press.

    Langley, P. (1997). Learning to sense selectively in physical domains. Proceedings of the First International Conference on Autonomous Agents (pp. 217-226). Marina del Rey, CA: ACM Press.



    For more information, please send email to dgs@stanford.edu .