Current Research
Evolutionary Games; Social and Cultural Evolution, Modeling, and Simulation
Currently I am working on a number of projects intersecting computer science and social science. This work centers around modeling human behavior, decision making, social norms, culture and their evolution using multi-agent systems and through the framework of evolutionary game theory. Other projects involve problems relating to the dynamics of cultural/social information in structured populations (e.g. social networks) and how to appropriately model and play against human opponents in games.
Past Research
Computer Music and Computational Aesthetics
Prior to coming to UMD, I worked on several projects relating to computer music, music information retrieval, and computational aesthetics with Bill Manaris. This research focuses around applications of machine learning techniques to music and aesthetics:
A National Science Foundation (NSF) project on music information retrieval. This work involves the development of a music-search engine prototype to search music libraries based on aesthetic similarity.
A genetic programming music generation system called NEvMusE (Neuro-Evolutionary Music Environment). We conducted several experiments using trained Artificial Neural Networks (ANNs) as fitness critics, i.e. Artificial Art Critics, during the evolutionary music composition process. The ANNs trained on statistical proportions of musical pieces. Samples of evolved music pieces can be found here: NEvMuse
Publications
- P. Shakarian, P. Roos, and A. Johnson. A Review of Evolutionary Graph Theory With Applications to Game Theory. Biosystems, Elsevier. To appear. 2011. P. Shakarian and P. Roos. Fast and Deterministic Computation of Fixation Probabilities in Evolutionary Graphs. In Sixth IASTED International Conference on Computational Intelligence and Bioinformatics. To appear. Nov 2011.
- B. Manaris, P. Roos, D. Krehbiel, T. Zalonis, and J.R. Armstrong, Zipf's Law, Power Laws and Music Aesthetics, in T. Li, M. Ogihara, G. Tzanetakis (eds.), Music Data Mining, pp. 169-216, CRC Press - Taylor & Francis, July 2011.
- P. Roos, J. R. Carr, and D. S. Nau. Evolution of state-dependent risk preferences. ACM Transactions on Intelligent Systems and Technology (TIST) 1(1):6:1–6:21, Oct. 2010.
- P. Roos and D. S. Nau. Risk preference and sequential choice in evolutionary games. Advances in Complex Systems 13(4):559–578, Aug. 2010.
- P.Roos and D. S. Nau, State-Dependent Risk Preferences in Evolutionary Games, In S.-K. Chai, J. J. Salerno, and P. L. Mabry, editors, Advances in Social Computing: Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP 2010, volume LNCS 6007, pp. 23-31. Springer, Mar. 2010.
- P. Roos and D. S. Nau, Conditionally risky behavior vs expected value maximization in evolutionary games, In Sixth Conference of the European Social Simulation Association (ESSA 2009), Guildford, England, Sept. 2009.
- B. Manaris, D. Krehbiel, P. Roos, T. Zalonis, "Armonique: Experiments in Content-Based Similarity Retrieval Using Power-Law Melodic and Timbre Metrics", Proceedings of the Ninth International Conference on Music Information Retrieval, Philadelphia, PA, pp. 343-348, Sep. 2008.
- P. Roos and B. Manaris, "A Music Information Retrieval Approach Based on Power Laws", Proceedings of 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-07), Patras, Greece, vol. 2, pp. 27-31, Oct. 2007.
- B. Manaris, P. Roos, P. Machado, D. Krehbiel, L. Pellicoro, and J. Romero, "A Corpus-Based Hybrid Approach to Music Analysis and Composition", Proceedings of 22nd Conference on Artificial Intelligence (AAAI-07), Vancouver, BC, pp. 839-845, Jul. 2007.