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The Derivations of Temporal Logic Formulas

. Finite sequences and tuples of elements of a non-empty sets. Formalized Mathematics , 1( 3 ):529-536, 1990. [6] Czesław Bylinski. Functions and their basic properties. Formalized Mathematics , 1( 1 ):55-65, 1990. [7] Czesław Bylinski. Functions from a set to a set. Formalized Mathematics , 1( 1 ):153-164, 1990. [8] Czesław Bylinski. Partial functions. Formalized Mathematics , 1( 2 ):357-367, 1990. [9] Mariusz Giero. The axiomatization of propositional linear time temporal logic. Formalized Mathematics

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Evolutionary algorithms and fuzzy sets for discovering temporal rules

Transactions on Fuzzy Systems 15(4): 616-635. Ale, J.M. and Rossi, G. H. (2000). An approach to discovering temporal association rules, Proceedings of the 2000 ACM Symposium on Applied Computing (SAC’00), Como, Italy, pp. 294-300. Bayardo, Jr., R.J. and Agrawal, R. (1999). Mining the most interesting rules, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp. 145-154. Ben Aicha, F., Bouani, F. and Ksouri, M. (2013). A multivariable multiobjective predictive

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The Axiomatization of Propositional Linear Time Temporal Logic

. Temporal Logic and State Systems . Springer-Verlag, 2008. [10] Andrzej Trybulec. Domains and their Cartesian products. Formalized Mathematics , 1( 1 ):115-122, 1990. [11] Andrzej Trybulec. Defining by structural induction in the positive propositional language. Formalized Mathematics , 8( 1 ):133-137, 1999. [12] Zinaida Trybulec. Properties of subsets. Formalized Mathematics , 1( 1 ):67-71, 1990. [13] Edmund Woronowicz. Many-argument relations. Formalized

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The Properties of Sets of Temporal Logic Subformulas

. [10] Czesław Bylinski. Partial functions. Formalized Mathematics , 1( 2 ):357-367, 1990. [11] Czesław Bylinski. Some basic properties of sets. Formalized Mathematics , 1( 1 ):47-53, 1990. [12] Agata Darmochwał. Finite sets. Formalized Mathematics , 1( 1 ):165-167, 1990. [13] Mariusz Giero. The axiomatization of propositional linear time temporal logic. FormalizedMathematics , 19( 2 ):113-119, 2011, doi: 10.2478/v10037-011-0018-1. [14] Mariusz Giero. The derivations of temporal logic formulas

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Ontology–based access to temporal data with Ontop: A framework proposal

References Abiteboul, S., Hull, R. and Vianu, V. (1995). Foundations of Databases , Addison Wesley Publ. Co., Boston, MA. Allen, J.F. (1983). Maintaining knowledge about temporal intervals, Communications of the ACM 26 (11): 832–843. Alur, R. and Henzinger, T.A. (1993). Real-time logics: Complexity and expressiveness, Information and Computation 104 (1): 35–77. Anicic, D., Fodor, P., Rudolph, S. and Stojanovic, N. (2011). EP-SPARQL: A unified language for event processing and stream reasoning, Proceedings of the 20th International

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Weak Completeness Theorem for Propositional Linear Time Temporal Logic

. Formalized Mathematics , 1( 1 ):165-167, 1990. [16] Mariusz Giero. The axiomatization of propositional linear time temporal logic. Formalized Mathematics , 19( 2 ):113-119, 2011, doi: 10.2478/v10037-011-0018-1. [17] Mariusz Giero. The derivations of temporal logic formulas. Formalized Mathematics , 20( 3 ):215-219, 2012, doi: 10.2478/v10037-012-0025-x. [18] Mariusz Giero. The properties of sets of temporal logic subformulas. Formalized Mathematics , 20( 3 ):221-226, 2012, doi: 10.2478/v10037-012-0026-9. [19

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Propositional Linear Temporal Logic with Initial Validity Semantics

1 This work was supported by the University of Bialystok grants: BST447 Formalization of temporal logics in a proof-assistant. Application to System Verification , and BST225 Database of mathematical texts checked by computer . R eferences [1] Grzegorz Bancerek. The fundamental properties of natural numbers. Formalized Mathematics , 1( 1 ):41–46, 1990. [2] Grzegorz Bancerek. The ordinal numbers. Formalized Mathematics , 1( 1 ):91–96, 1990. [3] Grzegorz Bancerek and Krzysztof Hryniewiecki. Segments of natural numbers and finite

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Evolving small-board Go players using coevolutionary temporal difference learning with archives

. Kim, K.-J., Choi, H. and Cho, S.-B. (2007). Hybrid of evolution and reinforcement learning for Othello players, IEEE Symposium on Computational Intelligence and Games, CIG 2007, Honolulu, HI, USA , pp. 203-209. Krawiec, K. and Szubert, M. (2010). Coevolutionary temporal difference learning for small-board Go, IEEE Congress on Evolutionary Computation, Barcelona, Spain , pp. 1-8. Lasker, E. (1960). Go and Go-Moku: The Oriental Board Games , Dover Publications, New York, NY. Lubberts, A

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Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

, ACM SIGMOD Record 32 (2): 5-14. Hulten, G., Spencer, L. and Domingos, P. (2001). Mining time-changing data streams, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA , pp. 97-106. Ikonomovska, E., Gama, J. and Dzeroski, S. (2011). Learning model trees from evolving data streams, Data Mining and Knowledge Discovery 23 (1): 128-168. Krizanovic, K., Galic, Z. and Baranovic, M. (2011). Data types and operations for spatio-temporal data streams

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Distributed scheduling of sensor networks for identification of spatio-temporal processes

-843. Patan, M. (2009a). Decentralized mobile sensor routing for parameter estimation of distributed systems, Proceedings of the 1st IFAC Workshop on Estimation and Control of Networked Systems, NecSys 2009, Venice, Italy , pp. 210-215. Patan, M. (2009b). Distributed configuration of sensor networks for parameter estimation in spatio-temporal systems, Proceedings of the European Control Conference, ECC'09, Budapest, Hungary , pp. 4871-4876. Patan, M., Chen, Y. and Tricaud, C. (2008). Resource-constrained sensor routing

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