Modern philosophy has formulated many of the foundational questions germane to mathematics and science, and has answered several of them. Decision theory, game theory, logic, statistical causal inference and the theory of computation have all advanced significantly as a result of recent philosophical research. The mission of LSEC is to turn such advances into useful computational tools that will help scientists do research or help students learn. Examples of such tools include: simulation engines (e.g., of decision or game theoretic agents), intelligent tutors (e.g., of formal deductive reasoning), efficient search procedures (e.g., for causal explanations of statistical data), and interactive courseware (e.g., to teach mathematical reasoning or the general methodology of controlled experiments). LSEC will pursue its goals by housing one or two permanent project scientists, one or two visitors, and several undergraduate and graduate research fellows in a pleasant wing (Baker 139) with 3 offices and a large common space that contains plenty of state-of-the-art hardware and software.
LSEC is connected to several other labs and centers at Carnegie Mellon. Many of the LSEC associates are also members of CALD: the Center for Automated Learning and Discovery which combines faculty from Philosophy, Statistics, Computer Science, Robotics, and Language Technologies with a common interest in practically computable methods to learn from data. The Center for the Advancement of Applied Ethics, which is also part of the Philosophy Department, specializes in creating multimedia programs that teach practical methods for analyzing and responding to real ethical problems. The Human-Computer Interaction Institute (HCII) which has faculty from Computer Science, Design, the Software Engineering Institute, and Psychology. HCII faculty have expertise in designing, implementing, and evaluating user interfaces, educational software, and computer mediated interaction in general. The Center for Innovation in Learning studies pedagogy but specializes in computer based education. The Pittsburgh Area Cognitive Tutoring Center is working to improve American mathematics education through the development and deployment of computer tutors based on modern cognitive science.
The faculty of the Philosophy Department includes experts in a variety of areas:
Current Fellows
Undergraduate Research Fellowships
Applications can be submitted to the address below by hardcopy or email
by Oct. 15, 1998, and decisions will be made by November 1st.
Applications should include:
Suggestions for Undergraduate Projects (1998)
Causal and Statistical Reasoning (General Contacts: Peter Spirtes, Richard Scheines, Clark Glymour, Greg Cooper, or Steve Klepper)
- Simulation environments. Simulating data from known models has proved crucial in developing algorithms for causal inference, but our simulation environment is quite limited. Extending its functionality and giving it a more imaginative interface would help the project. (Contact: Richard Scheines)
- Educational Modules. One branch of the causal reasoning project is educational. The Dept. of Education has funded us to build web-based software to teach causal reasoning with statistical data. We are now constructing modules that have interactive Java applets to teach these concepts, and have a number of projects that would benefit from an undergraduate research project. (Contact: Richard Scheines or Clark Glymour)
- How Humans Learn Causal Structure. We believe that humans learn about
causal structure in a different way than our computer algorithms do, but
we don't know. Research is needed into how humans learn about causation,
and how they might be trained to do so more effectively. (Contact: Clark
Glymour)
- Proof Search in Elementary Set Theory. Proof Search in Natural Deduction is now feasible with the Intercalation Calculus (Sieg and Byrnes, 1997). Extending the Intercalation Calculus to Elementary Set Theory would be mathematically interesting and be useful pedagogically. (Contacts: Wilfried Sieg)
- How Novices and Experts Construct Proofs. Many powerful algorithms
exist for finding proofs in formal logical systems. Some are explicitly
built to use strategies that mimic human experts, and some not, even though
little is known about how novice and experts actually search for proofs.
Research needs to be done on how human expertise in proof search can be
used to help computers become more efficient. (Contacts: Richard Scheines,
Wilfried Sieg, or Herb Simon)
Simulating Social Interaction (Contacts: Cristina Bicchieri or John Miller)
Cognitive Basis of Learning (Contacts: Herb Simon)
Glymour, C., Scheines, R., Spirtes, P., & Kelly, K. (1987). Discovering Causal Structure. Academic Press, San Diego, CA.
Scheines, R. (1997) "Estimating Latent Causal Influences," in Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, ed. D. Madigan.
Scheines, R., and Sieg, W. (1993). The Carnegie Mellon Proof Tutor. In Judith V. Boettcher (Ed.), 101 Success Stories of Information Technology in Higher Education: The Joe Wyatt Challenge. McGraw-Hill.
Scheines, R., and Sieg, W. (1994). Computer Environments for Proof Construction. Interactive Learning Environments, v. 4, pp 159-169.
Sieg, W., and Byrnes, J. (1996) Normal Natural Deduction Proofs (in classical logic) Technical Report CMU-PHIL-74, Dept. of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213
Sieg, W., and Scheines, R. (1992). Searching for proofs (in sentential logic). In: Leslie Burkholder (Ed.), Philosophy and the Computer. Boulder: Westview Press.
Scheines, R., Spirtes, P., Glymour, C., and Meek C. (1994). TETRAD II: Tools for Discovery. Lawrence Erlbaum Associates, Hillsdale, NJ.
Scheines, R., Glymour, C., Spirtes, P., Meek, C. and Richardson, T. (1996). "The TETRAD Project: Constraint based aids to model specification," Multivariate Behavioral Research., to appear.
Scheines, R., Spirtes, P., and Glymour, C. (1993). "Prediction and Causation," in Advanded Technology for Developers, Jane Klimasauskas (editor), Vol. 2, August, High-Tech Communications, Sewickley, PA, pp. 1-12.
Scheines, R. (1997). "An Introduction to Causal Inference," in Causality in Crisis?, ed. V. Mckim and S. Turner (eds.), Univ. of Notre Dame Press.
Scott, D.S. (1991) "Exploration with Mathematica," in CMU Computer Science, A 25th Anniversary Commemorative, R. Rashid (ed.), Reading, MA: ACM Press, Addison-Wesley Publishing Co., pp. 501-515.
Spirtes, P., Glymour C., and Scheines, R. (1993). Causation, Prediction and Search, Springer Lecture Notes in Statistics.
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Send email to R.Scheines@andrew.cmu.edu