Answer Set Solving in Practice


 Ashlyn Carpenter
 2 years ago
 Views:
Transcription
1 Answer Set Solving in Practice Torsten Schaub University of Potsdam Potassco Slide Packages are licensed under a Creative Commons Attribution 3.0 Unported License. Torsten Schaub Answer Set Solving in Practice November 15, / 454
2 Motivation: Overview 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
3 Motivation Outline 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
4 Motivation Informatics What is the problem? versus How to solve the problem? Problem Solution Computer Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
5 Motivation Informatics What is the problem? versus How to solve the problem? Problem Solution Computer Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
6 Motivation Traditional programming What is the problem? versus How to solve the problem? Problem Solution Computer Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
7 Motivation Traditional programming What is the problem? versus How to solve the problem? Problem Solution Programming Interpreting Program Executing Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
8 Motivation Declarative problem solving What is the problem? versus How to solve the problem? Problem Solution Interpreting Computer Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
9 Motivation Declarative problem solving What is the problem? versus How to solve the problem? Problem Solution Modeling Interpreting Representation Solving Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
10 Motivation Declarative problem solving What is the problem? versus How to solve the problem? Problem Solution Modeling Interpreting Representation Solving Output Torsten Schaub Answer Set Solving in Practice November 15, / 454
11 Nutshell Outline 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
12 Nutshell Answer Set Programming in a Nutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities ASP has its roots in (deductive) databases logic programming (with negation) (logicbased) knowledge representation and (nonmonotonic) reasoning constraint solving (in particular, SATisfiability testing) ASP allows for solving all search problems in NP (and NP NP ) in a uniform way ASP is versatile as reflected by the ASP solver clasp, winning first places at ASP, CASC, MISC, PB, and SAT competitions ASP embraces many emerging application areas Torsten Schaub Answer Set Solving in Practice November 15, / 454
13 Nutshell Answer Set Programming in a Nutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities ASP has its roots in (deductive) databases logic programming (with negation) (logicbased) knowledge representation and (nonmonotonic) reasoning constraint solving (in particular, SATisfiability testing) ASP allows for solving all search problems in NP (and NP NP ) in a uniform way ASP is versatile as reflected by the ASP solver clasp, winning first places at ASP, CASC, MISC, PB, and SAT competitions ASP embraces many emerging application areas Torsten Schaub Answer Set Solving in Practice November 15, / 454
14 Nutshell Answer Set Programming in a Nutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities ASP has its roots in (deductive) databases logic programming (with negation) (logicbased) knowledge representation and (nonmonotonic) reasoning constraint solving (in particular, SATisfiability testing) ASP allows for solving all search problems in NP (and NP NP ) in a uniform way ASP is versatile as reflected by the ASP solver clasp, winning first places at ASP, CASC, MISC, PB, and SAT competitions ASP embraces many emerging application areas Torsten Schaub Answer Set Solving in Practice November 15, / 454
15 Nutshell Answer Set Programming in a Nutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities ASP has its roots in (deductive) databases logic programming (with negation) (logicbased) knowledge representation and (nonmonotonic) reasoning constraint solving (in particular, SATisfiability testing) ASP allows for solving all search problems in NP (and NP NP ) in a uniform way ASP is versatile as reflected by the ASP solver clasp, winning first places at ASP, CASC, MISC, PB, and SAT competitions ASP embraces many emerging application areas Torsten Schaub Answer Set Solving in Practice November 15, / 454
16 Nutshell Answer Set Programming in a Nutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities ASP has its roots in (deductive) databases logic programming (with negation) (logicbased) knowledge representation and (nonmonotonic) reasoning constraint solving (in particular, SATisfiability testing) ASP allows for solving all search problems in NP (and NP NP ) in a uniform way ASP is versatile as reflected by the ASP solver clasp, winning first places at ASP, CASC, MISC, PB, and SAT competitions ASP embraces many emerging application areas Torsten Schaub Answer Set Solving in Practice November 15, / 454
17 Nutshell Answer Set Programming in a Nutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities ASP has its roots in (deductive) databases logic programming (with negation) (logicbased) knowledge representation and (nonmonotonic) reasoning constraint solving (in particular, SATisfiability testing) ASP allows for solving all search problems in NP (and NP NP ) in a uniform way ASP is versatile as reflected by the ASP solver clasp, winning first places at ASP, CASC, MISC, PB, and SAT competitions ASP embraces many emerging application areas Torsten Schaub Answer Set Solving in Practice November 15, / 454
18 Nutshell Answer Set Programming in a Hazelnutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities tailored to Knowledge Representation and Reasoning Torsten Schaub Answer Set Solving in Practice November 15, / 454
19 Nutshell Answer Set Programming in a Hazelnutshell ASP is an approach to declarative problem solving, combining a rich yet simple modeling language with highperformance solving capacities tailored to Knowledge Representation and Reasoning ASP = DB+LP+KR+SAT Torsten Schaub Answer Set Solving in Practice November 15, / 454
20 Shifting paradigms Outline 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
21 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Automated planning, Kautz and Selman (ECAI 92) Represent planning problems as propositional theories so that models not proofs describe solutions Torsten Schaub Answer Set Solving in Practice November 15, / 454
22 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Automated planning, Kautz and Selman (ECAI 92) Represent planning problems as propositional theories so that models not proofs describe solutions Torsten Schaub Answer Set Solving in Practice November 15, / 454
23 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Automated planning, Kautz and Selman (ECAI 92) Represent planning problems as propositional theories so that models not proofs describe solutions Torsten Schaub Answer Set Solving in Practice November 15, / 454
24 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Automated planning, Kautz and Selman (ECAI 92) Represent planning problems as propositional theories so that models not proofs describe solutions Torsten Schaub Answer Set Solving in Practice November 15, / 454
25 Shifting paradigms Model Generation based Problem Solving Representation constraint satisfaction problem propositional horn theories propositional theories propositional theories propositional theories propositional programs propositional programs propositional programs firstorder theories firstorder theories firstorder theories firstorder theories autoepistemic theories default theories. Solution assignment smallest model models minimal models stable models minimal models supported models stable models models minimal models stable models Herbrand models expansions extensions. Torsten Schaub Answer Set Solving in Practice November 15, / 454
26 Shifting paradigms Model Generation based Problem Solving Representation constraint satisfaction problem propositional horn theories propositional theories propositional theories propositional theories propositional programs propositional programs propositional programs firstorder theories firstorder theories firstorder theories firstorder theories autoepistemic theories default theories. Solution assignment smallest model models minimal models stable models minimal models supported models stable models models minimal models stable models Herbrand models expansions extensions. Torsten Schaub Answer Set Solving in Practice November 15, / 454
27 Shifting paradigms Model Generation based Problem Solving Representation Solution constraint satisfaction problem assignment propositional horn theories smallest model propositional theories models SAT propositional theories minimal models propositional theories stable models propositional programs minimal models propositional programs supported models propositional programs stable models firstorder theories models firstorder theories minimal models firstorder theories stable models firstorder theories Herbrand models autoepistemic theories expansions default theories extensions.. Torsten Schaub Answer Set Solving in Practice November 15, / 454
28 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Torsten Schaub Answer Set Solving in Practice November 15, / 454
29 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Torsten Schaub Answer Set Solving in Practice November 15, / 454
30 Shifting paradigms LPstyle playing with blocks Prolog program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Prolog queries? above(a,c). true.? above(c,a). no. Torsten Schaub Answer Set Solving in Practice November 15, / 454
31 Shifting paradigms LPstyle playing with blocks Prolog program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Prolog queries? above(a,c). true.? above(c,a). no. Torsten Schaub Answer Set Solving in Practice November 15, / 454
32 Shifting paradigms LPstyle playing with blocks Prolog program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Prolog queries? above(a,c). true.? above(c,a). no. Torsten Schaub Answer Set Solving in Practice November 15, / 454
33 Shifting paradigms LPstyle playing with blocks Prolog program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Prolog queries (testing entailment)? above(a,c). true.? above(c,a). no. Torsten Schaub Answer Set Solving in Practice November 15, / 454
34 Shifting paradigms Shuffled Prolog program on(a,b). on(b,c). above(x,y) : above(x,z), on(z,y). above(x,y) : on(x,y). LPstyle playing with blocks Prolog queries? above(a,c). Fatal Error: local stack overflow. Torsten Schaub Answer Set Solving in Practice November 15, / 454
35 Shifting paradigms Shuffled Prolog program on(a,b). on(b,c). above(x,y) : above(x,z), on(z,y). above(x,y) : on(x,y). LPstyle playing with blocks Prolog queries? above(a,c). Fatal Error: local stack overflow. Torsten Schaub Answer Set Solving in Practice November 15, / 454
36 Shifting paradigms Shuffled Prolog program on(a,b). on(b,c). above(x,y) : above(x,z), on(z,y). above(x,y) : on(x,y). LPstyle playing with blocks Prolog queries (answered via fixed execution)? above(a,c). Fatal Error: local stack overflow. Torsten Schaub Answer Set Solving in Practice November 15, / 454
37 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Torsten Schaub Answer Set Solving in Practice November 15, / 454
38 Shifting paradigms KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Torsten Schaub Answer Set Solving in Practice November 15, / 454
39 Shifting paradigms Formula on(a, b) on(b, c) (on(x, Y ) above(x, Y )) (on(x, Z) above(z, Y ) above(x, Y )) SATstyle playing with blocks Herbrand model { on(a, b), on(b, c), on(a, c), on(b, b), above(a, b), above(b, c), above(a, c), above(b, b), above(c, b) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
40 Shifting paradigms Formula on(a, b) on(b, c) (on(x, Y ) above(x, Y )) (on(x, Z) above(z, Y ) above(x, Y )) SATstyle playing with blocks Herbrand model { on(a, b), on(b, c), on(a, c), on(b, b), above(a, b), above(b, c), above(a, c), above(b, b), above(c, b) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
41 Shifting paradigms Formula on(a, b) on(b, c) (on(x, Y ) above(x, Y )) (on(x, Z) above(z, Y ) above(x, Y )) SATstyle playing with blocks Herbrand model { on(a, b), on(b, c), on(a, c), on(b, b), above(a, b), above(b, c), above(a, c), above(b, b), above(c, b) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
42 Shifting paradigms Formula on(a, b) on(b, c) (on(x, Y ) above(x, Y )) (on(x, Z) above(z, Y ) above(x, Y )) SATstyle playing with blocks Herbrand model { on(a, b), on(b, c), on(a, c), on(b, b), above(a, b), above(b, c), above(a, c), above(b, b), above(c, b) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
43 Shifting paradigms Formula on(a, b) on(b, c) (on(x, Y ) above(x, Y )) (on(x, Z) above(z, Y ) above(x, Y )) SATstyle playing with blocks Herbrand model (among 426!) { on(a, b), on(b, c), on(a, c), on(b, b), above(a, b), above(b, c), above(a, c), above(b, b), above(c, b) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
44 Rooting ASP Outline 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
45 Rooting ASP KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Torsten Schaub Answer Set Solving in Practice November 15, / 454
46 Rooting ASP KR s shift of paradigm Theorem Proving based approach (eg. Prolog) 1 Provide a representation of the problem 2 A solution is given by a derivation of a query Model Generation based approach (eg. SATisfiability testing) 1 Provide a representation of the problem 2 A solution is given by a model of the representation Answer Set Programming (ASP) Torsten Schaub Answer Set Solving in Practice November 15, / 454
47 Rooting ASP Model Generation based Problem Solving Representation Solution constraint satisfaction problem assignment propositional horn theories smallest model propositional theories models propositional theories minimal models propositional theories stable models propositional programs minimal models propositional programs supported models propositional programs stable models firstorder theories models firstorder theories minimal models firstorder theories stable models firstorder theories Herbrand models autoepistemic theories expansions default theories extensions.. Torsten Schaub Answer Set Solving in Practice November 15, / 454
48 Rooting ASP Answer Set Programming at large Representation Solution constraint satisfaction problem assignment propositional horn theories smallest model propositional theories models propositional theories minimal models propositional theories stable models propositional programs minimal models propositional programs supported models propositional programs stable models firstorder theories models firstorder theories minimal models firstorder theories stable models firstorder theories Herbrand models autoepistemic theories expansions default theories extensions.. Torsten Schaub Answer Set Solving in Practice November 15, / 454
49 Rooting ASP Answer Set Programming commonly Representation Solution constraint satisfaction problem assignment propositional horn theories smallest model propositional theories models propositional theories minimal models propositional theories stable models propositional programs minimal models propositional programs supported models propositional programs stable models firstorder theories models firstorder theories minimal models firstorder theories stable models firstorder theories Herbrand models autoepistemic theories expansions default theories extensions.. Torsten Schaub Answer Set Solving in Practice November 15, / 454
50 Rooting ASP Answer Set Programming in practice Representation Solution constraint satisfaction problem assignment propositional horn theories smallest model propositional theories models propositional theories minimal models propositional theories stable models propositional programs minimal models propositional programs supported models propositional programs stable models firstorder theories models firstorder theories minimal models firstorder theories stable models firstorder theories Herbrand models autoepistemic theories expansions default theories extensions.. Torsten Schaub Answer Set Solving in Practice November 15, / 454
51 Rooting ASP Answer Set Programming in practice Representation Solution constraint satisfaction problem assignment propositional horn theories smallest model propositional theories models propositional theories minimal models propositional theories stable models propositional programs minimal models propositional programs supported models propositional programs stable models firstorder theories models firstorder theories minimal models firstorder theories stable models firstorder theories Herbrand models autoepistemic theories expansions default theories extensions firstorder programs stable Herbrand models Torsten Schaub Answer Set Solving in Practice November 15, / 454
52 Rooting ASP ASPstyle playing with blocks Logic program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Stable Herbrand model { on(a, b), on(b, c), above(b, c), above(a, b), above(a, c) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
53 Rooting ASP ASPstyle playing with blocks Logic program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Stable Herbrand model { on(a, b), on(b, c), above(b, c), above(a, b), above(a, c) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
54 Rooting ASP ASPstyle playing with blocks Logic program on(a,b). on(b,c). above(x,y) : on(x,y). above(x,y) : on(x,z), above(z,y). Stable Herbrand model (and no others) { on(a, b), on(b, c), above(b, c), above(a, b), above(a, c) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
55 Rooting ASP ASPstyle playing with blocks Logic program on(a,b). on(b,c). above(x,y) : above(z,y), on(x,z). above(x,y) : on(x,y). Stable Herbrand model (and no others) { on(a, b), on(b, c), above(b, c), above(a, b), above(a, c) } Torsten Schaub Answer Set Solving in Practice November 15, / 454
56 Rooting ASP ASP versus LP ASP Prolog Model generation Query orientation Bottomup Topdown Modeling language Programming language Rulebased format Instantiation Unification Flat terms Nested terms (Turing +) NP( NP ) Turing Torsten Schaub Answer Set Solving in Practice November 15, / 454
57 Rooting ASP ASP Constructive Logic Closed (and open) world reasoning Model generation Bottomup ASP versus SAT SAT Classical Logic Open world reasoning Modeling language Complex reasoning modes Satisfiability testing Satisfiability Satisfiability Enumeration/Projection Intersection/Union Optimization (Turing +) NP( NP ) NP Torsten Schaub Answer Set Solving in Practice November 15, / 454
58 ASP solving Outline 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
59 ASP solving ASP solving Problem Solution Modeling Interpreting Logic Program Grounder Solver Solving Stable Models Torsten Schaub Answer Set Solving in Practice November 15, / 454
60 ASP solving SAT solving Problem Solution Programming Interpreting Formula (CNF) Solver Classical Models Solving Torsten Schaub Answer Set Solving in Practice November 15, / 454
61 ASP solving Rooting ASP solving Problem Solution Modeling Interpreting Logic Program Grounder Solver Solving Stable Models Torsten Schaub Answer Set Solving in Practice November 15, / 454
62 ASP solving Rooting ASP solving Problem Solution Modeling KR Interpreting Logic Program LP Grounder Solver DB Solving SAT Stable Models DB+KR+LP Torsten Schaub Answer Set Solving in Practice November 15, / 454
63 Using ASP Outline 1 Motivation 2 Nutshell 3 Shifting paradigms 4 Rooting ASP 5 ASP solving 6 Using ASP Torsten Schaub Answer Set Solving in Practice November 15, / 454
64 Using ASP Two sides of a coin ASP as Highlevel Language Express problem instance(s) as sets of facts Encode problem (class) as a set of rules Read off solutions from stable models of facts and rules ASP as Lowlevel Language Compile a problem into a logic program Solve the original problem by solving its compilation Torsten Schaub Answer Set Solving in Practice November 15, / 454
65 Using ASP What is ASP good for? Combinatorial search problems in the realm of P, NP, and NP NP (some with substantial amount of data), like Automated Planning Code Optimization Composition of Renaissance Music Database Integration Decision Support for NASA shuttle controllers Model Checking Product Configuration Robotics Systems Biology System Synthesis (industrial) Teambuilding and many many more Torsten Schaub Answer Set Solving in Practice November 15, / 454
66 Using ASP What is ASP good for? Combinatorial search problems in the realm of P, NP, and NP NP (some with substantial amount of data), like Automated Planning Code Optimization Composition of Renaissance Music Database Integration Decision Support for NASA shuttle controllers Model Checking Product Configuration Robotics Systems Biology System Synthesis (industrial) Teambuilding and many many more Torsten Schaub Answer Set Solving in Practice November 15, / 454
67 Using ASP What does ASP offer? Integration of DB, KR, and SAT techniques Succinct, elaborationtolerant problem representations Rapid application development tool Easy handling of dynamic, knowledge intensive applications including: data, frame axioms, exceptions, defaults, closures, etc Torsten Schaub Answer Set Solving in Practice November 15, / 454
68 Using ASP What does ASP offer? Integration of DB, KR, and SAT techniques Succinct, elaborationtolerant problem representations Rapid application development tool Easy handling of dynamic, knowledge intensive applications including: data, frame axioms, exceptions, defaults, closures, etc ASP = DB+LP+KR+SAT Torsten Schaub Answer Set Solving in Practice November 15, / 454
69 Using ASP What does ASP offer? Integration of DB, KR, and SAT techniques Succinct, elaborationtolerant problem representations Rapid application development tool Easy handling of dynamic, knowledge intensive applications including: data, frame axioms, exceptions, defaults, closures, etc ASP = DB+LP+KR+SMT Torsten Schaub Answer Set Solving in Practice November 15, / 454
70 Using ASP [1] C. Anger, M. Gebser, T. Linke, A. Neumann, and T. Schaub. The nomore++ approach to answer set solving. In G. Sutcliffe and A. Voronkov, editors, Proceedings of the Twelfth International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR 05), volume 3835 of Lecture Notes in Artificial Intelligence, pages SpringerVerlag, [2] C. Anger, K. Konczak, T. Linke, and T. Schaub. A glimpse of answer set programming. Künstliche Intelligenz, 19(1):12 17, [3] Y. Babovich and V. Lifschitz. Computing answer sets using program completion. Unpublished draft, [4] C. Baral. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Torsten Schaub Answer Set Solving in Practice November 15, / 454
71 Using ASP [5] C. Baral, G. Brewka, and J. Schlipf, editors. Proceedings of the Ninth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 07), volume 4483 of Lecture Notes in Artificial Intelligence. SpringerVerlag, [6] C. Baral and M. Gelfond. Logic programming and knowledge representation. Journal of Logic Programming, 12:1 80, [7] S. Baselice, P. Bonatti, and M. Gelfond. Towards an integration of answer set and constraint solving. In M. Gabbrielli and G. Gupta, editors, Proceedings of the Twentyfirst International Conference on Logic Programming (ICLP 05), volume 3668 of Lecture Notes in Computer Science, pages SpringerVerlag, [8] A. Biere. Adaptive restart strategies for conflict driven SAT solvers. Torsten Schaub Answer Set Solving in Practice November 15, / 454
72 Using ASP In H. Kleine Büning and X. Zhao, editors, Proceedings of the Eleventh International Conference on Theory and Applications of Satisfiability Testing (SAT 08), volume 4996 of Lecture Notes in Computer Science, pages SpringerVerlag, [9] A. Biere. PicoSAT essentials. Journal on Satisfiability, Boolean Modeling and Computation, 4:75 97, [10] A. Biere, M. Heule, H. van Maaren, and T. Walsh, editors. Handbook of Satisfiability, volume 185 of Frontiers in Artificial Intelligence and Applications. IOS Press, [11] G. Brewka, T. Eiter, and M. Truszczyński. Answer set programming at a glance. Communications of the ACM, 54(12):92 103, [12] K. Clark. Torsten Schaub Answer Set Solving in Practice November 15, / 454
73 Using ASP Negation as failure. In H. Gallaire and J. Minker, editors, Logic and Data Bases, pages Plenum Press, [13] M. D Agostino, D. Gabbay, R. Hähnle, and J. Posegga, editors. Handbook of Tableau Methods. Kluwer Academic Publishers, [14] E. Dantsin, T. Eiter, G. Gottlob, and A. Voronkov. Complexity and expressive power of logic programming. In Proceedings of the Twelfth Annual IEEE Conference on Computational Complexity (CCC 97), pages IEEE Computer Society Press, [15] M. Davis, G. Logemann, and D. Loveland. A machine program for theoremproving. Communications of the ACM, 5: , [16] M. Davis and H. Putnam. A computing procedure for quantification theory. Torsten Schaub Answer Set Solving in Practice November 15, / 454
Curriculum Vitae Dr. Yi Zhou
Curriculum Vitae Dr. Yi Zhou Personal Information Name: Yi Zhou Major: Computer Science Tel (work): +6124736 0802 Tel (home): +6124736 8357 Email: yzhou@scm.uws.edu.au Homepage: https://www.scm.uws.edu.au/~yzhou/
More informationAn Heuristics for Load Balancing and Granularity Control in the Parallel Instantiation of Disjunctive Logic Programs
An Heuristics for Load Balancing and Granularity Control in the Parallel Instantiation of Disjunctive Logic Programs Simona Perri, Francesco Ricca, and Marco Sirianni Dipartimento di Matematica, Università
More informationSurviving Solver Sensitivity: An ASP Practitioner s Guide
Surviving Solver Sensitivity: An ASP Practitioner s Guide BRYAN SILVERTHORN 1, YULIYA LIERLER 2, and MARIUS SCHNEIDER 3 1 Department of Computer Science The University of Texas at Austin Austin, TX, USA
More informationOne More Decidable Class of Finitely Ground Programs
One More Decidable Class of Finitely Ground Programs Yuliya Lierler and Vladimir Lifschitz Department of Computer Sciences, University of Texas at Austin {yuliya,vl}@cs.utexas.edu Abstract. When a logic
More informationCooperating Answer Set Programming
Cooperating Answer Set Programming Davy Van Nieuwenborgh 1,, Stijn Heymans 2, and Dirk Vermeir 1 1 Dept. of Computer Science Vrije Universiteit Brussel, VUB Pleinlaan 2, B1050 Brussels, Belgium {dvnieuwe,
More informationTesting ASP programs in ASPIDE
Testing ASP programs in ASPIDE Onofrio Febbraro 1, Kristian Reale 2, and Francesco Ricca 2 1 DLVSystem s.r.l.  P.zza Vermicelli, Polo Tecnologico, 87036 Rende, Italy febbraro@dlvsystem.com 2 Dipartimento
More informationBetween Restarts and Backjumps
Between Restarts and Backjumps Antonio Ramos, Peter van der Tak, and Marijn Heule Department of Software Technology, Delft University of Technology, The Netherlands Abstract. This paper introduces a novel
More informationRandom vs. StructureBased Testing of AnswerSet Programs: An Experimental Comparison
Random vs. StructureBased Testing of AnswerSet Programs: An Experimental Comparison Tomi Janhunen 1, Ilkka Niemelä 1, Johannes Oetsch 2, Jörg Pührer 2, and Hans Tompits 2 1 Aalto University, Department
More informationSolving SAT and SAT Modulo Theories: from an Abstract DavisPutnamLogemannLoveland Procedure to DPLL(T)
Solving SAT and SAT Modulo Theories: from an Abstract DavisPutnamLogemannLoveland Procedure to DPLL(T) ROBERT NIEUWENHUIS and ALBERT OLIVERAS Technical University of Catalonia, Barcelona and CESARE
More informationAutomatic GenreDependent Composition using Answer Set Programming
Automatic GenreDependent Composition using Answer Set Programming Sarah Opolka and Philipp Obermeier and Torsten Schaub University of Potsdam Potsdam, Germany Abstract Harmonic music composition adheres
More informationUniversity of Potsdam Faculty of Computer Science. Clause Learning in SAT Seminar Automatic Problem Solving WS 2005/06
University of Potsdam Faculty of Computer Science Clause Learning in SAT Seminar Automatic Problem Solving WS 2005/06 Authors: Richard Tichy, Thomas Glase Date: 25th April 2006 Contents 1 Introduction
More informationEquivalence in Answer Set Programming
Equivalence in Answer Set Programming Mauricio Osorio, Juan Antonio Navarro, José Arrazola Universidad de las Américas, CENTIA Sta. Catarina Mártir, Cholula, Puebla 72820 México {josorio, ma108907, arrazola}@mail.udlap.mx
More informationnpsolver A SAT Based Solver for Optimization Problems
npsolver A SAT Based Solver for Optimization Problems Norbert Manthey and Peter Steinke Knowledge Representation and Reasoning Group Technische Universität Dresden, 01062 Dresden, Germany peter@janeway.inf.tudresden.de
More informationModelling and Implementing a Knowledge Base for Checking Medical Invoices with DLV
Modelling and Implementing a Knowledge Base for Checking Medical Invoices with DLV Christoph Beierle 1, Oliver Dusso 1, Gabriele KernIsberner 2 1 Dept. of Computer Science, FernUniversität in Hagen, 58084
More informationAn Introduction to AI Planning
An Introduction to AI Planning Ute Schmid Applied CS/Cognitive Systems Bamberg University Goals, Methods, Topics of AI Basic concepts of AI Planning Deductive Planning vs. Statebased (Strips) Planning
More informationA Modular Representation of a Business Process Planner
A Modular Representation of a Business Process Planner Shahab Tasharrofi and Evgenia Ternovska School of Computing Science Simon Fraser University Canada 1st International Workshop on Knowledgeintensive
More informationA search based Sudoku solver
A search based Sudoku solver Tristan Cazenave Labo IA Dept. Informatique Université Paris 8, 93526, SaintDenis, France, cazenave@ai.univparis8.fr Abstract. Sudoku is a popular puzzle. In this paper we
More informationKBS KnowledgeBased Systems Group
Answer Set Programming in a Nutshell Thomas Eiter Institute of Information Systems Vienna University of Technology (TU Wien) KBS KnowledgeBased Systems Group eiter@kr.tuwien.ac.at GK Kolloqium Mathematische
More informationLogic Programming with Ordered Disjunction
Logic Programming with Ordered Disjunction Gerhard Brewka Universität Leipzig Institut für Informatik Augustusplatz 1011 04109 Leipzig, Germany brewka@informatik.unileipzig.de Abstract Logic programs
More informationUpdating Action Domain Descriptions
Updating Action Domain Descriptions Thomas Eiter, Esra Erdem, Michael Fink, and Ján Senko Institute of Information Systems, Vienna University of Technology, Vienna, Austria Email: (eiter esra michael jan)@kr.tuwien.ac.at
More informationOpus: University of Bath Online Publication Store http://opus.bath.ac.uk/
Sureshkumar, A. (2006) AnsProlog* Programming Environment (APE): Investigating software tools for answer set programming through the implementation of an integrated development environment. Other. Department
More informationCSE 459/598: Logic for Computer Scientists (Spring 2012)
CSE 459/598: Logic for Computer Scientists (Spring 2012) Time and Place: T Th 10:3011:45 a.m., M109 Instructor: Joohyung Lee (joolee@asu.edu) Instructor s Office Hours: T Th 4:305:30 p.m. and by appointment
More informationBoundedwidth QBF is PSPACEcomplete
Boundedwidth QBF is PSPACEcomplete Albert Atserias 1 and Sergi Oliva 2 1 Universitat Politècnica de Catalunya Barcelona, Spain atserias@lsi.upc.edu 2 Universitat Politècnica de Catalunya Barcelona, Spain
More informationData Integration and Answer Set Programming
Data Integration and Answer Set Programming Thomas Eiter Knowledge Based Systems Group, Institute of Information Systems, Vienna University of Technology, A1040 Vienna, Austria eiter@kr.tuwien.ac.at Abstract.
More informationA SAT Based Scheduler for Tournament Schedules
A SAT Based Scheduler for Tournament Schedules Hantao Zhang, Dapeng Li, and Haiou Shen Department of Computer Science The University of Iowa Iowa City, IA 522421419, USA {dapli, hshen, hzhang}@cs.uiowa.edu
More informationPhone: +43 1 4277 783 21 Fax: +43 1 4277 8 783 21 Email: wolfgang.dvorak@univie.ac.at Homepage: homepage.univie.ac.at/wolfgang.
Wolfgang Dvořák University of Vienna Research Group: Theory and Applications of Algorithms Währinger Straße 29, A1090 Wien, Austria Phone: +43 1 4277 783 21 Fax: +43 1 4277 8 783 21 Email: wolfgang.dvorak@univie.ac.at
More informationPresentation Overview AGENTBASED SOFTWARE ENGINEERING. 1 The AOP Proposal. 1.1 Specifics
AGENTBASED SOFTWARE ENGINEERING Mike Wooldridge & Michael Fisher Department of Computing Manchester Metropolitan University United Kingdom M.Wooldridge@doc.mmu.ac.uk Presentation Overview 1. Introduce
More informationHigh Integrity Software Conference, Albuquerque, New Mexico, October 1997.
MetaAmphion: Scaling up HighAssurance Deductive Program Synthesis Steve Roach Recom Technologies NASA Ames Research Center Code IC, MS 2692 Moffett Field, CA 94035 sroach@ptolemy.arc.nasa.gov Jeff Van
More informationAnswer Set Application Programming: a Case Study on Tetris
Technical Communications of ICLP 2015. Copyright with the Authors. 1 Answer Set Application Programming: a Case Study on Tetris PETER SCHÜLLER Department of Computer Engineering, Faculty of Engineering,
More informationL. THORNE MCCARTY. Courses Taught, Law School: Contracts; Copyrights, Patents and Trademarks; Artificial Intelligence and Legal Reasoning (seminar).
L. THORNE MCCARTY Department of Computer Science Rutgers, The State University of New Jersey 110 Frelinghuysen Road Piscataway, New Jersey 088548019 Email: mccarty@cs.rutgers.edu EDUCATION: Harvard Law
More informationTheory of Automated Reasoning An Introduction. AnttiJuhani Kaijanaho
Theory of Automated Reasoning An Introduction AnttiJuhani Kaijanaho Intended as compulsory reading for the Spring 2004 course on Automated Reasononing at Department of Mathematical Information Technology,
More informationBlock 1 Block 3. Figure 2: A Programmable Interconnection Network
Reconfigurable Interconnect Synthesis via Quantified Boolean Satisfiability Subramanyan Siva½ Ranga Vemuri½ Willard Vanfleet¾ John Franco½ John Schlipf½ ½Department of ECECS, University of Cincinnati,
More informationSatisfiability Checking
Satisfiability Checking SATSolving Prof. Dr. Erika Ábrahám Theory of Hybrid Systems Informatik 2 WS 10/11 Prof. Dr. Erika Ábrahám  Satisfiability Checking 1 / 40 A basic SAT algorithm Assume the CNF
More informationAGENTS AND SOFTWARE ENGINEERING
AGENTS AND SOFTWARE ENGINEERING Michael Wooldridge Queen Mary and Westfield College, University of London London E1 4NS, United Kingdom M.J.Wooldridge@qmw.ac.uk Abstract Software engineers continually
More informationConstraint Programming. Master Parisien de Recherche en Informatique
Constraint Programming Master Parisien de Recherche en Informatique 2008/2009 François Fages et Sylvain Soliman Project team Contraintes INRIA ParisRocquencourt http://contraintes.inria.fr/ fages Francois.Fages@inria.fr
More informationCS510 Software Engineering
CS510 Software Engineering Propositional Logic Asst. Prof. Mathias Payer Department of Computer Science Purdue University TA: Scott A. Carr Slides inspired by Xiangyu Zhang http://nebelwelt.net/teaching/15cs510se
More informationUPDATES OF LOGIC PROGRAMS
Computing and Informatics, Vol. 20, 2001,????, V 2006Nov6 UPDATES OF LOGIC PROGRAMS Ján Šefránek Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University,
More informationLecture 13 of 41. More Propositional and Predicate Logic
Lecture 13 of 41 More Propositional and Predicate Logic Monday, 20 September 2004 William H. Hsu, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Reading: Sections 8.18.3, Russell and Norvig
More informationBig Data: A Geometric Explanation of a Seemingly Counterintuitive Strategy
Big Data: A Geometric Explanation of a Seemingly Counterintuitive Strategy Olga Kosheleva and Vladik Kreinovich University of Texas at El Paso 500 W. University El Paso, TX 79968, USA olgak@utep.edu, vladik@utep.edu
More informationLecture 7: NPComplete Problems
IAS/PCMI Summer Session 2000 Clay Mathematics Undergraduate Program Basic Course on Computational Complexity Lecture 7: NPComplete Problems David Mix Barrington and Alexis Maciel July 25, 2000 1. Circuit
More informationFaster SAT Solving with Better CNF Generation
Faster SAT Solving with Better CNF Generation Benjamin Chambers Panagiotis Manolios Northeastern University {bjchamb, pete}@ccs.neu.edu Daron Vroon General Theological Seminary of the Episcopal Church
More informationSolving the Employee Timetabling Problem Using Advanced SAT & ILP Techniques
JOURNAL OF COMPUTERS, VOL. 8, NO. 4, APRIL 2013 851 Solving the Employee Timetabling Problem Using Advanced SAT & ILP Techniques Fadi A. Aloul*, Syed Z. H. Zahidi, Anas AlFarra, Basel AlRoh American
More informationCourse Outline Department of Computing Science Faculty of Science. COMP 37103 Applied Artificial Intelligence (3,1,0) Fall 2015
Course Outline Department of Computing Science Faculty of Science COMP 710  Applied Artificial Intelligence (,1,0) Fall 2015 Instructor: Office: Phone/Voice Mail: EMail: Course Description : Students
More informationSudoku as a SAT Problem
Sudoku as a SAT Problem Inês Lynce IST/INESCID, Technical University of Lisbon, Portugal ines@sat.inescid.pt Joël Ouaknine Oxford University Computing Laboratory, UK joel@comlab.ox.ac.uk Abstract Sudoku
More informationA Logic Approach for LTL System Modification
A Logic Approach for LTL System Modification Yulin Ding and Yan Zhang School of Computing & Information Technology University of Western Sydney Kingswood, N.S.W. 1797, Australia email: {yding,yan}@cit.uws.edu.au
More informationA Subatomic Proof System
A Subatomic Proof System Andrea Aler Tubella Alessio Guglielmi University of Bath In this work we show a proof system, called SA, that generates propositional proofs by employing a single, linear, simple
More informationA Contribution to Expert Decisionbased Virtual Product Development
A Contribution to Expert Decisionbased Virtual Product Development László Horváth, Imre J. Rudas Institute of Intelligent Engineering Systems, John von Neumann Faculty of Informatics, Óbuda University,
More informationTableaux Modulo Theories using Superdeduction
Tableaux Modulo Theories using Superdeduction An Application to the Verification of B Proof Rules with the Zenon Automated Theorem Prover Mélanie Jacquel 1, Karim Berkani 1, David Delahaye 2, and Catherine
More informationChaff: Engineering an Efficient SAT Solver
Matthew W. Moskewicz Department of EECS UC Berkeley Chaff: Engineering an Efficient SAT Solver moskewcz@alumni.princeton.edu Conor F. Madigan Department of EECS MIT cmadigan@mit.edu Ying Zhao, Lintao Zhang,
More informationIntroduction to Logic in Computer Science: Autumn 2006
Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today Now that we have a basic understanding
More informationLogic in general. Inference rules and theorem proving
Logical Agents Knowledgebased agents Logic in general Propositional logic Inference rules and theorem proving First order logic Knowledgebased agents Inference engine Knowledge base Domainindependent
More informationArtificial Intelligence
Artificial Intelligence ICS461 Fall 2010 1 Lecture #12B More Representations Outline Logics Rules Frames Nancy E. Reed nreed@hawaii.edu 2 Representation Agents deal with knowledge (data) Facts (believe
More informationQuery Answering in PeertoPeer Data Exchange Systems
Query Answering in PeertoPeer Data Exchange Systems Leopoldo Bertossi and Loreto Bravo Carleton University, School of Computer Science, Ottawa, Canada {bertossi,lbravo}@scs.carleton.ca Abstract. The
More informationFirstOrder Stable Model Semantics and FirstOrder Loop Formulas
Journal of Artificial Intelligence Research 42 (2011) 125180 Submitted 03/11; published 10/11 FirstOrder Stable Model Semantics and FirstOrder Loop Formulas Joohyung Lee Yunsong Meng School of Computing,
More informationGenerating models of a matched formula with a polynomial delay
Generating models of a matched formula with a polynomial delay Petr Savicky Institute of Computer Science, Academy of Sciences of Czech Republic, Pod Vodárenskou Věží 2, 182 07 Praha 8, Czech Republic
More informationComputation Beyond Turing Machines
Computation Beyond Turing Machines Peter Wegner, Brown University Dina Goldin, U. of Connecticut 1. Turing s legacy Alan Turing was a brilliant mathematician who showed that computers could not completely
More informationMemory Efficient AllSolutions SAT Solver and Its Application for Reachability Analysis
Memory Efficient AllSolutions SAT Solver and Its Application for Reachability Analysis Orna Grumberg, Assaf Schuster, and Avi Yadgar Computer Science Department, Technion, Haifa, Israel Abstract. This
More informationKLMLean 2.0: A Theorem Prover for KLM Logics of Nonmonotonic Reasoning
KLMLean 2.0: A Theorem Prover for KLM Logics of Nonmonotonic Reasoning Laura Giordano 1, Valentina Gliozzi 2, and Gian Luca Pozzato 2 1 Dipartimento di Informatica  Università del Piemonte Orientale A.
More informationDetection of Inconsistencies in Complex Product Configuration Data Using Extended Propositional SATChecking
From: FLAIRS01 Proceedings. Copyright 2001, AAAI (www.aaai.org). All rights reserved. Detection of Inconsistencies in Complex Product Configuration Data Using Extended Propositional SATChecking Carsten
More informationFoundational Proof Certificates
An application of proof theory to computer science INRIASaclay & LIX, École Polytechnique CUSO Winter School, Proof and Computation 30 January 2013 Can we standardize, communicate, and trust formal proofs?
More informationData Integration Using IDLogic
Data Integration Using IDLogic Bert Van Nuffelen 1, Alvaro CortésCalabuig 1, Marc Denecker 1, Ofer Arieli 2, and Maurice Bruynooghe 1 1 Department of Computer Science, Katholieke Universiteit Leuven,
More informationEFFICIENT KNOWLEDGE BASE MANAGEMENT IN DCSP
EFFICIENT KNOWLEDGE BASE MANAGEMENT IN DCSP Hong Jiang Mathematics & Computer Science Department, Benedict College, USA jiangh@benedict.edu ABSTRACT DCSP (Distributed Constraint Satisfaction Problem) has
More informationDescription. Time and place. Office, phones, etc. Spring Semester, 2005 Instructor: J. Horty. PHIL 858: Revising and Combining Beliefs
Spring Semester, 2005 Instructor: J. Horty PHIL 858: Revising and Combining Beliefs Description This seminar focuses on the related problems of belief revision (revising beliefs with new, possibly conflicting
More informationLecture 1: Introduction
Programming Languages Lecture 1: Introduction Benjamin J. Keller Department of Computer Science, Virginia Tech Programming Languages Lecture 1 Introduction 2 Lecture Outline Preview History of Programming
More informationSchool of Computer Science
School of Computer Science Computer Science  Honours Level  2014/15 October 2014 General degree students wishing to enter 3000 level modules and non graduating students wishing to enter 3000 level
More informationCS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 RealTime Systems. CSCI 522 High Performance Computing
CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate
More informationCurriculum Vitae. John M. Zelle, Ph.D.
Curriculum Vitae John M. Zelle, Ph.D. Address Department of Math, Computer Science, and Physics Wartburg College 100 Wartburg Blvd. Waverly, IA 50677 (319) 3528360 email: john.zelle@wartburg.edu Education
More informationConsistent Answers from Integrated Data Sources
Consistent Answers from Integrated Data Sources Leopoldo Bertossi 1, Jan Chomicki 2 Alvaro Cortés 3, and Claudio Gutiérrez 4 1 Carleton University, School of Computer Science, Ottawa, Canada. bertossi@scs.carleton.ca
More informationOpenWBO: a Modular MaxSAT Solver
OpenWBO: a Modular MaxSAT Solver Ruben Martins 1, Vasco Manquinho 2, and Inês Lynce 2 1 University of Oxford, Department of Computer Science, United Kingdom ruben.martins@cs.ox.ac.uk 2 INESCID / Instituto
More informationIntroduction to Artificial Intelligence
Introduction to Artificial Intelligence 1st year undergraduate degrees with AI and/or CS http://www.cs.bham.ac.uk/~jxb/iai.html Lecturer: Dr. John A. Bullinaria http://www.cs.bham.ac.uk/~jxb John A. Bullinaria,
More informationAssociation Rules Mining: Application to LargeScale Satisfiability
Association Rules Mining: Application to LargeScale Satisfiability Habiba Drias, Youcef Djenouri LRIA, USTHB Abstract Big data mining is a promising technology for industrial applications such as business
More informationSemantic Information Elicitation from Unstructured Medical Records
Semantic Information Elicitation from Unstructured Medical Records Massimo Ruffolo 1,3, Vittoria Cozza 2, Lorenzo Gallucci 1,2 Marco Manna 4, Mariarita Pizzonia 2 1 Exeura s.r.l. 2 DEIS  Department of
More informationLanguage. Johann Eder. Universitat Klagenfurt. Institut fur Informatik. Universiatsstr. 65. A9020 Klagenfurt / AUSTRIA
PLOP: A Polymorphic Logic Database Programming Language Johann Eder Universitat Klagenfurt Institut fur Informatik Universiatsstr. 65 A9020 Klagenfurt / AUSTRIA February 12, 1993 Extended Abstract The
More informationBuilding the Knowledge Base System IDP 3
Building the Knowledge Base System IDP 3 Marc Denecker Knowledge Representation and Reasoning, Department of Computer Science, KULeuven Marc.Denecker@cs.kuleuven.be Abstract IDP 3 is a Knowledge Base
More informationVerifying Refutations with Extended Resolution
Verifying Refutations with Extended Resolution Marijn J. H. Heule, Warren A. Hunt Jr., and Nathan Wetzler The University of Texas at Austin Abstract. Modern SAT solvers use preprocessing and inprocessing
More informationTheoretical Computer Science Bridging Course  Introduction / General Info. Summer Term 2016 Fabian Kuhn
Theoretical Computer Science Bridging Course  Introduction / General Info Summer Term 2016 Fabian Kuhn About the Course Topics Foundations of theoretical computer science Introduction to logic No lectures
More information270024  LI  Logics in Information Technology
Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 270  FIB  Barcelona School of Informatics 723  CS  Department of Computer Science BACHELOR'S DEGREE IN INFORMATICS ENGINEERING
More informationData Validation with OWL Integrity Constraints
Data Validation with OWL Integrity Constraints (Extended Abstract) Evren Sirin Clark & Parsia, LLC, Washington, DC, USA evren@clarkparsia.com Abstract. Data validation is an important part of data integration
More informationKEEP THIS COPY FOR REPRODUCTION PURPOSES. I ~~~~~Final Report
MASTER COPY KEEP THIS COPY FOR REPRODUCTION PURPOSES 1 Form Approved REPORT DOCUMENTATION PAGE I OMS No. 07040188 Public reoorting burden for this collection of information is estimated to average I hour
More informationCOMPUTER SCIENCE TRIPOS
CST.98.5.1 COMPUTER SCIENCE TRIPOS Part IB Wednesday 3 June 1998 1.30 to 4.30 Paper 5 Answer five questions. No more than two questions from any one section are to be answered. Submit the answers in five
More informationCNFSAT: Predictive Models, Dimensional Reduction, and Phase Transition
CNFSAT: Predictive Models, Dimensional Reduction, and Phase Transition Neil P. Slagle College of Computing Georgia Institute of Technology Atlanta, GA npslagle@gatech.edu Abstract CNFSAT embodies the P
More informationM.S. Project Proposal. SAT Based Attacks on SipHash
M.S. Project Proposal SAT Based Attacks on SipHash Santhosh Kantharaju Siddappa Department of Computer Science Rochester Institute of Technology Chair Prof. Alan Kaminsky Reader Prof. Stanisław P. Radziszowski
More informationSecondOrder Characterizations of Definientia in Formula Classes
Faculty of Computer Science Institute of Artificial Intelligence Knowledge Representation and Reasoning SecondOrder Characterizations of Definientia in Formula Classes Christoph Wernhard KRR Report 1403
More informationON FUNCTIONAL SYMBOLFREE LOGIC PROGRAMS
PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical and Mathematical Sciences 2012 1 p. 43 48 ON FUNCTIONAL SYMBOLFREE LOGIC PROGRAMS I nf or m at i cs L. A. HAYKAZYAN * Chair of Programming and Information
More informationA Chart Parsing implementation in Answer Set Programming
A Chart Parsing implementation in Answer Set Programming Ismael Sandoval Cervantes Ingenieria en Sistemas Computacionales ITESM, Campus Guadalajara elcoraz@gmail.com Rogelio Dávila Pérez Departamento de
More informationMagic Sets and their Application to Data Integration
Magic Sets and their Application to Data Integration Wolfgang Faber, Gianluigi Greco, Nicola Leone Department of Mathematics University of Calabria, Italy {faber,greco,leone}@mat.unical.it Roadmap Motivation:
More informationThe Road from Software Testing to Theorem Proving
The Road from Software Testing to Theorem Proving A Short Compendium of my Favorite Software Verification Techniques Frédéric Painchaud DRDC Valcartier / Robustness and Software Analysis Group December
More informationBorne inférieure de circuit : une application des expanders
Borne inférieure de circuit : une application des expanders Simone Bova 1 Florent Capelli 2 Friedrich Slivovski 1 Stefan Mengel 3 1 TU Wien, 2 IMJPRG, Paris 7, 3 CRIL, Lens 6 Novembre 2015 JGA 2015 Motivation
More informationNeighborhood Data and Database Security
Neighborhood Data and Database Security Kioumars Yazdanian, FrkdCric Cuppens email: yaz@ tlscs.cert.fr  cuppens@ tlscs.cert.fr CERT / ONERA, Dept. of Computer Science 2 avenue E. Belin, B.P. 4025,31055
More informationThe MiniZinc Challenge compares different solvers on a
The MiniZinc Challenge 2008 2013 Peter J. Stuckey, Thibaut Feydy, Andreas Schutt, Guido Tack, Julien Fischer n MiniZinc is a solveragnostic modeling language for defining and solving combinatorial satisfaction
More informationSystem BV is NPcomplete
System BV is NPcomplete Ozan Kahramanoğulları 1,2 Computer Science Institute, University of Leipzig International Center for Computational Logic, TU Dresden Abstract System BV is an extension of multiplicative
More informationSlot machines. an approach to the Strategy Challenge in SMT solving. Stéphane GrahamLengrand CNRS  Ecole Polytechnique  SRI International,
Slot machines an approach to the Strategy Challenge in SMT solving Stéphane GrahamLengrand CNRS  Ecole Polytechnique  SRI International, SMTWorkshop, 19th July 2015 2 Disclaimer This talk will not
More informationDETECTING SUSPICIOUS INPUT IN INTELLIGENT SYSTEMS USING ANSWER SET PROGRAMMING NICHOLAS GIANOUTSOS, B.S. A THESIS COMPUTER SCIENCE
DETECTING SUSPICIOUS INPUT IN INTELLIGENT SYSTEMS USING ANSWER SET PROGRAMMING by NICHOLAS GIANOUTSOS, B.S. A THESIS IN COMPUTER SCIENCE Submitted to the Graduate Faculty of Texas Tech University in Partial
More informationEvolutionary SAT Solver (ESS)
Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, August 35, 2011,
More informationCurriculum Vitae. May 10, 1975 (Born in Alexandria, Egypt)
Curriculum Vitae NAME: BIRTH DATE: ADDRESS: Islam Tharwat Elkabani May 10, 1975 (Born in Alexandria, Egypt) Beirut Arab University, Faculty of Science, Department of Mathematics and Computer Science, Beirut,
More informationComputational Methods for Database Repair by Signed Formulae
Computational Methods for Database Repair by Signed Formulae Ofer Arieli (oarieli@mta.ac.il) Department of Computer Science, The Academic College of TelAviv, 4 Antokolski street, TelAviv 61161, Israel.
More informationSatisfiability and Validity
6.825 Techniques in Artificial Intelligence Satisfiability and Validity Lecture 4 1 Last time we talked about propositional logic. There's no better way to empty out a room than to talk about logic. So
More informationTweety: A Comprehensive Collection of Java Libraries for Logical Aspects of Artificial Intelligence and Knowledge Representation
draft 20140130 Tweety: A Comprehensive Collection of Java Libraries for Logical Aspects of Artificial Intelligence and Knowledge Representation Matthias Thimm Institute for Web Science and Technologies
More informationOptimizing Description Logic Subsumption
Topics in Knowledge Representation and Reasoning Optimizing Description Logic Subsumption Maryam FazelZarandi Company Department of Computer Science University of Toronto Outline Introduction Optimization
More informationSoftware Modeling and Verification
Software Modeling and Verification Alessandro Aldini DiSBeF  Sezione STI University of Urbino Carlo Bo Italy 34 February 2015 Algorithmic verification Correctness problem Is the software/hardware system
More information