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View all- Guzzo ASaccà D(2018)Semi-Inflationary DATALOG: A declarative database language with procedural featuresAI Communications10.5555/1218852.121885418:2(79-92)Online publication date: 26-Dec-2018
Event choice datalog: a logic programming language for reasoning in multiple dimensions
Pages 238 - 249
Abstract
This paper presents a rule-based declarative database language which extends DATALOG to express events and nondeterministic state transitions, by using the choice construct to model uncertainty in dynamic rules. The proposed language, called Event Choice DATALOG (DATALOG!ev for short), provides a powerful mechanism to formulate queries on the evolution of a knowledge base, given a sequence of events envisioned to occur in the future. A distinguished feature of this language is the use of multiple spatio-temporal dimensions in order to model a finer control of evolution. A comprehensive study of the computational complexity of answering DATALOG!ev queries is reported.
References
[1]
M. Abadi and Z. Manna. Temporal Logic Programming. Journal of Symbolic Computation, 8(3), pages 277--295, 1989.]]
[2]
J. J. Alferes, A. Brogi, J. A. Leite, and L.M. Pereira. Evolving Logic Programs. In Proc. of JELIA'02, pages 50--61, 2002.]]
[3]
J. J. Alferes, J. A. Leite, L. M. Pereira,H. Przymusinska, and T. C. Przymusinski. Dynamic Logic Programming. In Proc. of KR'98, pages 98--111, 1998.]]
[4]
J. J. Alferes, L. M. Pereira,H. Przymusinska, and T. C. Przymusinski. LUPS: A language for updating logic programs. Artificial Intelligence, 138(1--2):87--116, 2002.]]
[5]
C. Baral. A Systematic Approach to reason with time and situations. Unpublished.]]
[6]
C. Baral, and M. Gelfond. Reasoning About Effects of Concurrent Actions. JLP, 31(1-3):85--117, 1997.]]
[7]
C. Baral, M. Gelfond, and A. Provetti. Representing Actions: Laws, Observations and Hypotheses. JLP, 31(1-3):201--243, 1997.]]
[8]
C. Bettini, X. Sean~Wang, and S. Jajodia. A General Framework for Time Granularity and Its Application to Temporal Reasoning. AMAI, 22(1-2):29--58, 1998.]]
[9]
I. Cervesato, M. Franceschet, and A. Montanari. A Guided Tour through Some Extensions of the Event Calculus. Computational Intelligence, 16(2):307--347, 2000.]]
[10]
I. Cervesato, and A. Montanari. A General Modal Framework for the Event Calculus and Its Skeptical and Credulous Variants. JLP, 38(2):111--164, 1999.]]
[11]
L. Chittaro, and C. Combi. Temporal Granularity and Indeterminacy in Reasoning About Actions and Change: An Approach Based on the Event Calculus. Annals of Mathematics and Artificial Intelligence, 36(1--2):81--119, 2002.]]
[12]
P. Cholewinski, V. Marek, and M. Truszczynski. Default Reasoning System DeReS. In Proc. of thephInternational Conference on Principles of Knowledge Representation and Reasoning, pages 518--528, 1996.]]
[13]
J. Chomicki, and T. Imielinski. Relational Specifications of Infinite Query Answers. In Proc. of SIGMOD'89, pages 174--183, 1989.]]
[14]
R. Clayton, J. Cleary, B. Pfahringer, and M. Utting. Starlog homepage. http://www.scms.waikato.ac.nz/cs/Research/starlog/.]]
[15]
Curtis E. Dyreson, W. S. Evans, Hong~Lin, and R. T. Snodgrass. Efficiently Supported Temporal Granularities. TKDE, 12(4):568--587, 2000.]]
[16]
T. Eiter, W. Faber, N. Leone, G. Pfeifer, and A. Polleres. A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity. TOCL, 2003. To appear.]]
[17]
T. Eiter, M. Fink, G. Sabbatini, and H. Tompits. A Framework for Declarative Update Specifications in Logic Programs. In Proc. of IJCAI'01, pages 649--654, 2001.]]
[18]
M. Franceschet, and A. Montanari. A graph-theoretic approach to efficiently reason about partially ordered events in (Modal) Event Calculus. AMAI, 30(1-4):93--118, 2000.]]
[19]
A. Gabaldon. Programming Hierarchical Task Networks in the Situation Calculus. In Proc. ofphAIPS'02 Workshop on On-line Planning and Scheduling, 2002.]]
[20]
M. Gelfond, and V. Lifschitz. The Stable Model Semantics for Logic Programming. In Proc. of ICLP/SLP'88, pages 1070--1080, 1988. MIT Press.]]
[21]
M. Gelfond, and V. Lifschitz. Action Languages. Electronic Transactions on Artificial Intelligence, 2(3-4):193--210, 1998.]]
[22]
E. Giunchiglia, and V. Lifschitz. An Action Language Based on Causal Explanation: Preliminary Report. In Proc. of AAAI '98, pages 623--630, 1998.]]
[23]
I. A. Goralwalla, Y. Leontiev, M. T. Özsu, D. Szafron, and C. Combi. Temporal Granularity: Completing the Puzzle. Journal of Intelligent Information Systems, 16(1):41--63, 2001.]]
[24]
T. Janhunen, I. Niemela, P. Simons, P., and J.H. You. Unfolding partiality and disjunctions in stable model semantics. In Proc. of thephInternational Conference on Principles of Knowledge Representation and Reasoning, pages 411--419, 2000.]]
[25]
S. Hanks, and D. McDermott. Nonmonotonic Logic and Temporal Projection. Artificial Intelligence, 33(3):379--412, 1987.]]
[26]
R. A. Kowalski, and M. J. Sergot. A Logic-based Calculus of Events. New Generation Computing, 4:67--95, 1986.]]
[27]
J. A. Leite, J. J. Alferes, and L. M. Pereira. Multidimensional Dynamic Knowledge representation. In Proc. of LPNMR'01, pages 365--378, 2001. Lecture Notes in AI (LNAI).]]
[28]
N. Leone, G. Pfeifer, W. Faber, F. Calimeri, T. Dell'Armi, T. Eiter, G. Gottlob, G. Ianni, G. Ielpa, C. Koch, S. Perri, and A. Polleres. The DLV System. In Proc. of JELIA'02, LNCS 2424, pages 537--540, 2002.]]
[29]
H. Levesque, R. Reiter, Y. Lesperance, F. Lin, and R. Scherl. GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31:59-84, 1997.]]
[30]
V. Lifschitz and H. Turner. Representing transition systems by logic programs, In Proc. of the 5th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, pages 92--106, 1999.]]
[31]
F. Lin and Y. Zhao. ASSAT: Computing Answer Sets of a Logic Program by SAT Solvers, In Proc. of theph8th National Conference on Artificial Intelligence, 112, 2002.]]
[32]
V. W. Marek, and Miroslaw Truszczyński. Autoepistemic Logic. Journal of the ACM, 38(3):588--619, 1991.]]
[33]
J. McCarthy, and P. J. Hayes. Some Philosophical Problems from the Standpoint of Artificial Intelligence. Machine Intelligence 4, pages 463--502. Edinburgh University Press, 1969.]]
[34]
A. Montanari, E. Maim, E. Ciapessoni, and E. Ratto. Dealing with time granularity in the Event Calculus. In Proc. of FGCS'92., pages 702--712, 1992.]]
[35]
I. Niemelä, P. Simons, and T. Syrjänen. Smodels: A System for Answer Set Programming. In Chitta Baral and Miroslaw Truszczyński, editors, Proc. of NMR'00, 2000.]]
[36]
M. Ali Orgun, and Wanli Ma. An Overview of Temporal and Modal Logic Programming. In Proc. of ICTL'94, pages 445--479, 1994. Springer-Verlag.]]
[37]
D. Saccà and C. Zaniolo. Stable Models and Non-Determinism in Logic Programs with Negation. In Proc. ACM Symp. on Principles of Database Systems, pages 205--218, 1990.]]
[38]
E. D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115-135, 1974.]]
[39]
K. Sagonas, T. Swift, and D.S. Warren. XSB as an Efficient Deductive Database Engine. In Proc. of thephACM SIGMOD International Conference on Management of Data, pages 442--453, 1994.]]
[40]
G. J. Sussman. The Virtuous Nature of Bugs. Readings in Planning, chapter~3, pages 111--117. Morgan Kaufmann, 1990.]]
[41]
T. Syrjanen and I. Niemela. The Smodels systems. In Proc. of thephInternational Conference on Logic Programming and Nonmonotonic Reasoning, pages 434--438, 2001.]]
[42]
H. Turner. Representing Actions in Logic Programs and Default Theories: A Situation Calculus Approach. Journal of Logic Programming, 31(1--3):245--298, 1997.]]
[43]
H. Turner. A Logic of Universal Causation. Artificial Intelligence, 113:87--123, 1999.]]
[44]
M. Vardi. The Complexity of Relational Query Languages. In Proc. of STOC'82, pages 137--146, 1982.]]
[45]
C. Zaniolo. Active Database Rules with Transaction-Conscious Stable Model Semantics. In Proc. of DOOD'95, pages 55--72, LNCS 1013, 1995.]]
Index Terms
- Event choice datalog: a logic programming language for reasoning in multiple dimensions
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Published: 24 August 2004
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View all- Guzzo ASaccà D(2018)Semi-Inflationary DATALOG: A declarative database language with procedural featuresAI Communications10.5555/1218852.121885418:2(79-92)Online publication date: 26-Dec-2018
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