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by Anne Condon

Download Computational Models of Games (ACM Distinguished Dissertation) fb2, epub

ISBN: 0262031523
Author: Anne Condon
Language: English
Publisher: The MIT Press; First edition (August 11, 1989)
Pages: 147
Category: Mathematics
Subcategory: Science
Rating: 4.1
Votes: 123
Size Fb2: 1787 kb
Size ePub: 1237 kb
Size Djvu: 1602 kb
Other formats: lrf doc txt docx


ACM Distinguished Dissertation ACM Fellow A. Nico Habermann Award. H. Jabbari and A. Condon. A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures BMC Bioinformatics, 15:147, 2014

ACM Distinguished Dissertation ACM Fellow A. University of Wisconsin–Madison University of British Columbia. A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures BMC Bioinformatics, 15:147, 2014. A. Condon, J. Manuch and C. Thachuk, The complexity of string partitioning, Journal of Discrete Algorithms, 32:24-43, 2015. Preliminary version appeared in the 23rd Annual Symposium on Combinatorial Pattern Matching, Springer-Verlag Lecture Notes in Computer Science 7354:159-172, 2012.

Computational Models of Games book Computational Models of Games (ACM Distinguished Dissertation). 0262031523 (ISBN13: 9780262031523).

Computational Models of Games book. Computational Models of Games (ACM Distinguished Dissertation).

We investigate simple stochastic games (SSGs): a kind of two-person games under uncertainty, the original model of which . Condon, . Computational Models of Games, (An ACM distinguished Dissertation 1988) MIT Press, Cambridge, MA (1989).

We investigate simple stochastic games (SSGs): a kind of two-person games under uncertainty, the original model of which was introduced in. We consider the computational complexity of. 1. deciding whether a given SSG is stopping (discounted) or not . The Complexity of Stochastic Games, Information and Computation 96 (1992) 203–224. CrossRefGoogle Scholar.

FREE shipping on qualifying offers. The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers

FREE shipping on qualifying offers. The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning.

Flag as Inappropriate. Computational Models of Games (1987). In 2010, the Association for Computing Machinery named her an ACM Fellow "for contributions to complexity theory and leadership in advancing women in computing".

Anne Condon University of British Columbia

Anne Condon University of British Columbia. Computational Challenges and Opportunities in RNA Secondary Structure Prediction Abstract: DNA and RNA molecules have proven to be very versatile materials. Structures formed by RNA molecules play important regulatory and catalytic roles in the cell, and show promise in therapeutics. She has received an ACM Distinguished Dissertation Award, NSF National Young Investigator Award, and University College Cork Distinguished Alumna Award for her work, and currently holds the NSERC/GM Canada Chair.

oceedings{ngTF, title {Distinguishing tests for nondeterministic and probabilistic machines}, author . Computational models of games. ACM distinguished dissertations.

oceedings{ngTF, title {Distinguishing tests for nondeterministic and probabilistic machines}, author {Rajeev Alur and Costas A. Courcoubetis and Mihalis Yannakakis}, booktitle {STOC}, year {1995} .

Algorithms for graph partitioning on the planted partition model. A Marathe, AE Condon, RM Corn. Journal of Computational Biology 8 (3), 201-219, 2001.

Professor of Computer Science, University of British Columbia. Algorithms for graph partitioning on the planted partition model. Random Structures & Algorithms 18 (2), 116-140, 2001. Demonstration of a word design strategy for DNA computing on surfaces. AG Frutos, Q Liu, AJ Thiel, AMW Sanner, AE Condon, LM Smith, RM Corn.

ACM Distinguished Service Award. ACM Names 2019 Distinguished Members. ACM Honors Shwetak Patel with ACM Prize in Computing. Presented annually to the author(s) of the best doctoral dissertation(s) in computer science and engineering. ACM Doctoral Dissertation Award. ACM-IEEE CS Eckert-Mauchly Award. ACM Grace Murray Hopper Award. Advanced Grades of Membership. Winning dissertations will be published in the ACM Digital Library as part of the ACM Books Series.

Modeling games provides a deeper understanding of computational models in general. Researchers in artificial intelligence have looked to chess as a model of thinking that can be automated while those in distributed computing and cryptography need models that reflect the competitive nature of distributed and cryptographic protocols. Computational Models of Games describes a model of two person games - called a probabilistic game automaton - that unifies other important models that have been developed to reflect the game-like properties of computational problems. It also covers interesting models of games not previously studied (introducing games against unknown nature, for example) and proves new results on time bounded game automata, space bounded game automata with complete information, and space bounded game automata with partial information. By incorporating the three important features of randomness, secrecy, and limited power for the players, the probabilistic game automaton models in a natural way many problems that computer scientists confront and provides insight into their complexity. It generalizes computational models such as the alternating Turing machines of Chandra, Kozen, and Stockmeyer, Papadimitriou's games against nature, the Arthur-Merlin games of Babai, and the interactive proof systems of Goldwasser, Micali, and Rackoff. Anne Condon received her doctorate from The University of Washington and is Assistant Professor at The University of Wisconsin Computational Models of Games is a 1988 ACM Distinguished Dissertation

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