Entrants' System Descriptions

CVC4 0.0

Architecture

Strategies

Implementation

Expected Competition Performance

E-Darwin 1.5

Architecture

Strategies

Implementation

E-KRHyper 1.3

Architecture

An E-hyper tableau is a tree whose nodes are labeled with clauses and which is built up by the application of the inference rules of the E-hyper tableau calculus. The calculus rules are designed such that most of the reasoning is performed using positive unit clauses. Splitting is done without rigid variables. Instead, variables which would be shared between branches are prevented by ground substitutions, which are guessed from the Herbrand universe and constrained by rewrite rules. Redundancy rules allow the detection and removal of clauses that are redundant with respect to a branch. The hyper extension inference from the original hyper tableau calculus is equivalent to a series of E-hyper tableau calculus inference applications. Therefore the implementation of the hyper extension in KRHyper by a variant of semi-naive evaluation [Ull89] is retained in E-KRHyper, where it serves as a shortcut inference for the resolution of non-equational literals.

Strategies

Implementation

E-MaLeS 1.1

Architecture

From the known performance of different E strategies over the TPTP library, the system learns which strategy is most likely to solve a problem. Given a new problem, E-MaLeS extracts the problems features (number of terms, number of literals, etc) and applies the learned function to the features. The result is a (hopefully optimal) strategy. As a new addition in 1.1, E-MaLeS learns not only the optimal strategy, but also for how long it should run each strategy. This allows better strategy scheduling.

Strategies

Implementation

EP 1.4pre

Architecture

EP 1.4pre is just a combination of E 1.4pre in verbose mode and a proof analysis tool extracting the used inference steps. For the LTB division, a control program uses a SInE-like analysis to extract reduced axiomatizations that are handed to several instances of E.

Strategies

The automatic mode is based on a static partition of the set of all clausal problems based on a number of simple features (number of clauses, maximal symbol arity, presence of equality, presence of non-unit and non-Horn clauses,...). Each class of clauses is automatically assigned a heuristic that performs well on problems from this class in test runs. About 100 different strategies have been evaluated on all untyped first-order problems from TPTP 4.1.0.

Implementation

EP 1.6pre

Architecture

EP 1.6pre is just a combination of E 1.6pre in verbose mode and a proof analysis tool extracting the used inference steps. For the LTB and MZR divisions, a control program uses a SInE-like analysis to extract reduced axiomatizations that are handed to several instances of E.

Strategies

The automatic mode is based on a static partition of the set of all clausal problems based on a number of simple features (number of clauses, maximal symbol arity, presence of equality, presence of non-unit and non-Horn clauses,...). Each class of clauses is automatically assigned a heuristic that performs well on problems from this class in test runs. About 60 different strategies have been evaluated on all bug-free untyped first-order problems from TPTP 5.3.0.

Implementation

FIMO 0.3

Architecture

Strategies

Fimo features symmetry breaking and incremental answer set solving provided by the underlying iASP system iClingo. Additionally, Fimo deskolemizes some of the Skolem functions by transforming them to aggregates in the resulting logic program.

Implementation

iProver 0.9

Architecture

Major additions in the current version are:

• answer computation,
• several modes for model output using first-order definitions in term algebra,
• Brand's transformation.

Strategies

Implementation

iProver is available from

iProver 0.99

Architecture

In the LTB division, iProver uses axiom selection based on the SInE algorithm [HV11] as implemented in Vampire [HKV11], i.e., axiom selection is done by Vampire and proof attempts are done by iProver.

iProver features are summaries below with recent additions marked with (*).

• proof extraction for both instantiation and resolution (*),
• model representation, using first-order definitions in term algebra,
• answer substitutions,
• semantic filtering (*),
• type inference [CLS11] (*),
• Brand's transformation.

Strategies

Implementation

iProver is available from

iProver-Eq 0.8

Architecture

iProver-Eq consists of three core components: i) ground reasoning by an SMT solver, ii) first-order equational reasoning on literals in a candidate model by a labelled unit superposition calculus [KS10,KS10] and iii) instantiation of clauses with substitutions obtained by ii).

Given a set of first-order clauses, iProver-Eq first abstracts it to a set of ground clauses which are then passed to the ground solver. If the ground abstraction is unsatisfiable, then the set of first-order clauses is also unsatisfiable. Otherwise, literals are selected from the first-order clauses based on the model of the ground solver. The labelled unit superposition calculus checks whether selected literals are conflicting. If they are conflicting, then clauses are instantiated such that the ground solver has to refine its model in order to resolve the conflict. Otherwise, satisfiability of the initial first-order clause set is shown.

Clause selection and literal selection in the unit superposition calculus are implemented in separate given clause algorithms. Relevant substitutions are accumulated in labels during unit superposition inferences and then used to instantiate clauses. For redundancy elimination iProver-Eq uses demodulation, dismatching constraints and global subsumption. In order to efficiently propagate redundancy elimination from instantiation into unit superposition, we implemented different representations of labels based on sets, AND/OR-trees and OBDDs. Non-equational resolution and equational superposition inferences provide further simplifications.

For the LTB division, iProver-Eq-SInE runs iProver-Eq as the underlying inference engine of SInE [HV11], i.e., axiom selection is done by SInE, and proof attempts are done by iProver-Eq.

Strategies

If no equational literals occur in the input, iProver-Eq falls back to the inference rules of iProver, otherwise the latter are disabled and only unit superposition is used. If all clauses are unit equations, no instances need to be generated and the calculus is run without the otherwise necessary bookkeeping.

Implementation

iProver-Eq is available from

Isabelle 2012

Architecture

leanCoP---2.2

Architecture

Strategies

Implementation

leanCoP can read formulae in leanCoP syntax as well as in (raw) TPTP first-order syntax. Equality axioms and axioms to support distinct objects are automatically added if required. The leanCoP core prover returns a very compact connection proof, which can be translated into different proof formats, e.g., into a lean (unofficial) TPTP syntax format for representing connection proofs [OS10] or into a readable text proof.

The leanCoP core prover and all its additional components run on any (standard) Prolog system; ECLiPSe, SICStus and SWI Prolog are currently explicitly supported. The shell script that implements the fixed strategy scheduling runs on Linux/Unix and MacOS platforms as well as on most Windows platforms.

The source code of leanCoP 2.2 is available under the GNU general public license. Together with more information it can be found on the leanCoP website at

leanCoP-ARDE---2.2

Architecture

Strategies

Implementation

Expected Competition Performance

LEO-II---1.4

Architecture

Strategies

Implementation

LEO-II's parser supports the TPTP THF0 language and also the TPTP languages FOF and CNF.

Unfortunately the LEO-II system still uses only a very simple sequential collaboration model with first-order ATPs instead of using the more advanced, concurrent and resource-adaptive OANTS architecture [BS+08] as exploited by its predecessor LEO.

The LEO-II system is distributed under a BSD style license, and it is available from

MaLARea 0.4

Architecture

Strategies

Implementation

MaLARea is available from

Muscadet 4.2

Architecture

Strategies

The system is also able to work with second order statements. It may also receive knowledge and know-how for a specific domain from a human user; see [Pas89] and [Pas93]. These two possibilities are not used while working with the TPTP Library.

Implementation

Muscadet is available from

Nitrox 2012

Architecture

Strategies

The translation from HOL to Kodkod's first-order relational logic (FORL) is parameterized by the cardinalities of the atomic types occurring in it. Nitrox enumerates the possible cardinalities for the universe. If a formula has a finite counterexample, the tool eventually finds it, unless it runs out of resources.

Nitpick is optimized to work with higher-order logic (HOL) and its definitional principles (e.g., (co)inductive predicates, (co)inductive datatypes, (co)recursive functions). When invoked on untyped first-order problem, few of its optimizations come into play, and the problem handed to Kodkod is essentially a first-order relational logic (FORL) rendering of the TPTP FOF problem. There are two main exceptions:

• Nested quantifiers are moved as far inside the formula as possible before Kodkod gets a chance to look at them [BN10].
• Definitions invoked with fixed arguments are specialized.

Implementation

Expected Competition Performance

Paradox 3.0

Architecture

Paradox incorporates the following features: Polynomial-time clause splitting heuristics, the use of incremental SAT, static symmetry reduction techniques, and the use of sort inference.

Strategies

1. Analyze the problem, finding an upper bound N on the domain size of models, where N is possibly infinite. A finite such upper bound can be found, for example, for EPR problems.
2. Flatten the problem, and split clauses and simplify as much as possible.
3. Instantiate the problem for domain sizes 1 up to N, applying the SAT solver incrementally for each size. Report "SATISFIABLE" when a model is found.
4. When no model of sizes smaller or equal to N is found, report "CONTRADICTION".

Implementation

Expected Competition Performance

Princess 2012-06-04

Architecture

The internal calculus of Princess only supports uninterpreted predicates; uninterpreted functions are encoded as predicates, together with the usual axioms. Through appropriate translation of quantified formulae with functions, the e-matching technique common in SMT solvers can be simulated; triggers in quantified formulae are chosen based on heuristics similar to those in the Simplify prover.

Strategies

For CASC, Princess will run a schedule with a small number of configurations for each problem (portfolio method). The schedule is determined either statically, or dynamically using syntactic attributes of problems (such as number and kind of quantifiers, etc), based on training using a random sample of problems from the TPTP library.

Implementation

Prover9 2009-11A

Architecture

Prover9 has available positive ordered (and nonordered) resolution and paramodulation, negative ordered (and nonordered) resolution, factoring, positive and negative hyperresolution, UR-resolution, and demodulation (term rewriting). Terms can be ordered with LPO, RPO, or KBO. Selection of the "given clause" is by an age-weight ratio.

Proofs can be given at two levels of detail: (1) standard, in which each line of the proof is a stored clause with detailed justification, and (2) expanded, with a separate line for each operation. When FOF problems are input, proof of transformation to clauses is not given.

Completeness is not guaranteed, so termination does not indicate satisfiability.

Strategies

Given a problem, Prover9 adjusts its inference rules and strategy according to syntactic properties of the input clauses such as the presence of equality and non-Horn clauses. Prover9 also does some preprocessing, for example, to eliminate predicates.

In previous CASC competitions, Prover9 has used LPO to order terms for demodulation and for the inference rules, with a simple rule for determining symbol precedence. For CASC 2012, we are going to use KBO instead.

For the FOF problems, a preprocessing step attempts to reduce the problem to independent subproblems by a miniscope transformation; if the problem reduction succeeds, each subproblem is clausified and given to the ordinary search procedure; if the problem reduction fails, the original problem is clausified and given to the search procedure.

Implementation

PS-E---1.0

Architecture

Strategies

Implementation

Satallax 2.1

Architecture

The theoretical basis of search is a complete ground tableau calculus for higher-order logic [BS10] with a choice operator [BB11]. A problem is given in the THF format. A branch is formed from the axioms of the problem and the negation of the conjecture (if any is given). From this point on, Satallax tries to determine unsatisfiability or satisfiability of this branch.

Satallax progressively generates higher-order formulae and corresponding propositional clauses [Bro11]. These formulae and propositional clauses correspond to instances of the tableau rules. Satallax uses the SAT solver MiniSat as an engine to test the current set of propositional clauses for unsatisfiability. If the clauses are unsatisfiable, then the original branch is unsatisfiable. If there are no quantifiers at function types, the generation of higher-order formulae and corresponding clauses may terminate [BS09,BS09]. In such a case, if MiniSat reports the final set of clauses as satisfiable, then the original set of higher-order formulae is satisfiable (by a standard model in which all types are interpreted as finite sets).

Strategies

A strategy schedule is an ordered collection of modes with information about how much time the mode should be allotted. Satallax tries each of the modes for a certain amount of time sequentially. Satallax 2.1 has eight strategy schedules which were determined through experimentation using the THF problems in version 5.1.0 of the TPTP library. One of these eight strategy schedules is chosen based on the amount of time Satallax is given to solve the problem. For example, if Satallax is given 180 seconds to solve the problem, then a schedule with 38 modes is chosen.

Implementation

Satallax 2.4

Architecture

Satallax progressively generates higher-order formulae and corresponding propositional clauses [Bro11]. These formulae and propositional clauses correspond to instances of the tableau rules. Satallax uses the SAT solver MiniSat as an engine to test the current set of propositional clauses for unsatisfiability. If the clauses are unsatisfiable, then the original branch is unsatisfiable.

Strategies

Implementation

SPASS+T 2.2.14

Architecture

Strategies

Implementation

SPASS+T 2.2.16

Architecture

Strategies

Implementation

STP---1.0

Architecture

The current system implements a complete calculus based on binary resolution.

Strategies

Implementation

Expected Competition Performance

SuperZenon 0.0.1

Architecture

Superdeduction [BHK07] is a variant of deduction modulo [DHK03], which is an extension of logical deduction systems consisting in canonically adding ad hoc deduction rules translating axiomatic theories. This has several advantages:

• Proofs are shorter, more readable, and close to the mathematical reasoning.
• Automated proof search may speed up in such systems as some systematic parts of the proofs are "compiled" into superdeduction rules.

TPS 3.120601S1b

Architecture

Strategies

• Re-ordering conjunctions and disjunctions to alter the way paths through the formula are enumerated.
• The use of primitive substitutions and gensubs [And89].
• Path-focused duplication [Iss90].
• Dual instantiation of definitions, and generating substitutions for higher-order variables which contain abbreviations already present in the theorem to be proved [BA98].
• Component search [Bis99].
• Generating and solving set constraints [Bro02].
• Generating connections using extensional and equational reasoning [Bro07].

Implementation

Vampire-LTB 1.8

Architecture

Substitution tree and code tree indexes are used to implement all major operations on sets of terms, literals and clauses. Although the kernel of the system works only with clausal normal form, the shell accepts a problem in the full first-order logic syntax, clausifies it and performs a number of useful transformations before passing the result to the kernel. Also the axiom selection algorithm Sine [HV11] can be enabled as part of the preprocessing.

When a theorem is proved, the system produces a verifiable proof, which validates both the clausification phase and the refutation of the CNF.

Strategies

1. Choice of the main procedure:
   • Limited Resource Strategy
   • DISCOUNT loop
   • Otter loop
   • Goal oriented mode based on tabulation
2. A variety of optional simplifications.
3. Parameterized reduction orderings.
4. A number of built-in literal selection functions and different modes of comparing literals.
5. Age-weight ratio that specifies how strongly lighter clauses are preferred for inference selection.
6. Set-of-support strategy.

Implementation

Expected Competition Performance

Vampire 2.6

Architecture

Substitution tree and code tree indexes are used to implement all major operations on sets of terms, literals and clauses. Although the kernel of the system works only with clausal normal form, the shell accepts a problem in the full first-order logic syntax, clausifies it and performs a number of useful transformations before passing the result to the kernel. Also the axiom selection algorithm Sine [HV11] can be enabled as part of the preprocessing.

When a theorem is proved, the system produces a verifiable proof, which validates both the clausification phase and the refutation of the CNF.

Strategies

• Choice of the main procedure:
  • Limited Resource Strategy
  • DISCOUNT loop
  • Otter loop
  • Goal oriented mode based on tabulation
  • Instantiation using the Inst-gen calculus
• A variety of optional simplifications.
• Parameterized reduction orderings.
• A number of built-in literal selection functions and different modes of comparing literals.
• Age-weight ratio that specifies how strongly lighter clauses are preferred for inference selection.
• Set-of-support strategy.

Implementation

Expected Competition Performance

Zenon 0.7.1

Architecture

Zenon outputs formal proofs that can be checked by Coq or Isabelle.

Strategies

Implementation

References

Abr96

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AB+96

Andrews P.B., Bishop M., Issar S., Nesmith. D., Pfenning F., Xi H. (1996), TPS: A Theorem-Proving System for Classical Type Theory, Journal of Automated Reasoning 16(3), 321-353.

AB06

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And81

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And89

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BG98

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Backes J., Brown C.E. (2011), Analytic Tableaux for Higher-Order Logic with Choice, Journal of Automated Reasoning 47(4), 451-479.

BT07

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BN+06

Barrett C. Nieuwenhuis R., Oliveras A., Tinelli C. (2006), Splitting on Demand in SAT Modulo Theories, Hermann M., Voronkov A., Proceedings of the 13th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (Phnom Penh, Cambodia), 512-526, Lecture Notes in Artificial Intelligence 4246.

BFT04

Baumgartner P., Fuchs A., Tinelli C. (2004), Darwin - A Theorem Prover for the Model Evolution Calculus, Sutcliffe G., Schulz S., Tammet T., Proceedings of the Workshop on Empirically Successful First Order Reasoning, 2nd International Joint Conference on Automated Reasoning (Cork, Ireland).

BFN96

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BFP07

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BPT11

Baumgartner P., Pelzer B., Tinelli C. (2011), Model Evolution with Equality - Revised and Implemented, Journal of Symbolic Computation, To appear.

BT03

Baumgartner P., Tinelli C. (2003), The Model Evolution Calculus, Baader F., Proceedings of the 19th International Conference on Automated Deduction (Miami, USA), 350-364, Lecture Notes in Artificial Intelligence 2741, Springer-Verlag.

BT05

Baumgartner P., Tinelli C. (2005), The Model Evolution Calculus with Equality, Nieuwenhuis R., Proceedings of the 20th International Conference on Automated Deduction (Tallinn, Estonia), 392-408, Lecture Notes in Artificial Intelligence 3632, Springer-Verlag.

BP+08

Benzmüller C., Paulson L., Theiss F., Fietzke A. (2008), LEO-II - A Cooperative Automatic Theorem Prover for Higher-Order Logic, Baumgartner P., Armando A., Gilles D., Proceedings of the 4th International Joint Conference on Automated Reasoning (Sydney, Australia), 162-170, Lecture Notes in Artificial Intelligence 5195, Springer-Verlag.

BRS08

Benzmüller C., Rabe F., Sutcliffe G. (2008), THF0 - The Core TPTP Language for Classical Higher-Order Logic, Baumgartner P., Armando A., Gilles D., Proceedings of the 4th International Joint Conference on Automated Reasoning (Sydney, Australia), 491-506, Lecture Notes in Artificial Intelligence 5195, Springer-Verlag.

BS+08

Benzmüller C., Sorge V., Jamnik M., Kerber M. (2008), Combined Reasoning by Automated Cooperation, Journal of Applied Logic 6(3), 318-342.

Ben99

Benzmüller C. (1999), Extensional Higher-order Paramodulation and RUE-Resolution, Ganzinger H., Proceedings of the 16th International Conference on Automated Deduction (Trento, Italy), 399-413, Lecture Notes in Artificial Intelligence 1632, Springer-Verlag.

Bib87

Bibel W. (1987), Automated Theorem Proving, Vieweg and Sohn.

BA98

Bishop M., Andrews P.B. (1998), Selectively Instantiating Definitions, Kirchner C., Kirchner H., Proceedings of the 15th International Conference on Automated Deduction (Lindau, Germany), 365-380, Lecture Notes in Artificial Intelligence 1421, Springer-Verlag.

Bis99

Bishop M. (1999), A Breadth-First Strategy for Mating Search, Ganzinger H., Proceedings of the 16th International Conference on Automated Deduction (Trento, Italy), 359-373, Lecture Notes in Artificial Intelligence 1632, Springer-Verlag.

BBP11

Blanchette J., Boehme S., Paulson L. (2011), Extending Sledgehammer with SMT Solvers, Bjorner N., Sofronie-Stokkermans V., Proceedings of the 23rd International Conference on Automated Deduction (Wroclaw, Poland), 116-130, Lecture Notes in Artificial Intelligence 6803, Springer-Verlag.

BN10

Blanchette J., Nipkow T. (2010), Nitpick: A Counterexample Generator for Higher-Order Logic Based on a Relational Model Finder, Kaufmann M. Paulson L., Proceedings of the 1st International Conference on Interactive Theorem Proving (Edinburgh, United Kingdom), 131-146, Lecture Notes in Computer Science 6172, Springer-Verlag.

BP+12

Blanchette J., Popescu A., Wand D., Weidenbach C. (2012), More SPASS with Isabelle, Beringer L., Felty A., Proceedings of Interactive Theorem Proving 2012 (Princeton, United States), Lecture Notes in Artificial Intelligence.

Ble71

Bledsoe W.W. (1971), Splitting and Reduction Heuristics in Automatic Theorem Proving, Artificial Intelligence 2, 55-77.

BDD07

Bonichon R., Delahaye D., Doligez D. (2007), Zenon : An Extensible Automated Theorem Prover Producing Checkable Proofs, Dershowitz N., Voronkov A., Proceedings of the 14th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (Yerevan, Armenia), 151-165, Lecture Notes in Artificial Intelligence 4790.

BHK07

Brauner P., Houtmann C., Kirchner C. (2007), Principles of Superdeduction, Ong L., Proceedings of the 22nd Annual IEEE Symposium on Logic in Computer Science (Wroclaw, Poland), 41-50, IEEE Computer Society Press.

BS09

Brown C.E., Smolka G. (2009), Terminating Tableaux for the Basic Fragment of Simple Type Theory, Giese M., Waaler A., Proceedings of the 18th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (Oslo, Norway), 138-151, Lecture Notes in Artificial Intelligence 5697, Springer-Verlag.

BS09

Brown C.E., Smolka G. (2009), Extended First-Order Logic, Nipkow T., Urban C., Proceedings of the 22nd International Conference on Theorem Proving in Higher Order Logics (Munich, Germany), 164-179, Lecture Notes in Computer Science 5674, Springer-Verlag.

BS10

Brown C.E., Smolka G. (2010), Analytic Tableaux for Simple Type Theory and its First-Order Fragment, Logical Methods in Computer Science 6(2).

Bro02

Brown C.E. (2002), Solving for Set Variables in Higher-Order Theorem Proving, Voronkov A., Proceedings of the 18th International Conference on Automated Deduction (Copenhagen, Denmark), 408-422, Lecture Notes in Artificial Intelligence 2392, Springer-Verlag.

Bro07

Brown C.E. (2007), Automated Reasoning in Higher-Order Logic: Set Comprehension and Extensionality in Church's Type Theory, Studies in Logic: Logic and Cognitive Systems 10, College Publications.

Bro11

Brown C.E. (2011), Reducing Higher-Order Theorem Proving to a Sequence of SAT Problems, Bjorner N., Sofronie-Stokkermans V., Proceedings of the 23rd International Conference on Automated Deduction (Wroclaw, Poland), 147-161, Lecture Notes in Artificial Intelligence 6803, Springer-Verlag.

Bro12

Brown C.E. (2012), Satallax: An Automated Higher-Order Prover (System Description), Gramlich B., Miller D., Sattler U., Proceedings of the 6th International Joint Conference on Automated Reasoning (Manchester, United Kingdom), To appear, Lecture Notes in Artificial Intelligence.

CLS11

Claessen K., Lilliestrom A., Smallbone N. (2011), Sort It Out with Monotonicity - Translating between Many-Sorted and Unsorted First-Order Logic, Bjorner N., Sofronie-Stokkermans V., Proceedings of the 23rd International Conference on Automated Deduction (Wroclaw, Poland), 207-221, Lecture Notes in Artificial Intelligence 6803, Springer-Verlag.

CS03

Claessen K., Sörensson N. (2003), New Techniques that Improve MACE-style Finite Model Finding, Baumgartner P., Fermueller C., Proceedings of the CADE-19 Workshop: Model Computation - Principles, Algorithms, Applications (Miami, USA).

dMB08

de Moura L., Bjorner N. (2008), Z3: An Efficient SMT Solver, Ramakrishnan C., Rehof J., Proceedings of the 14th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (Budapest, Hungary), 337-340, Lecture Notes in Artificial Intelligence 4963, Springer-Verlag.

DHK03

Dowek G., Hardin T., Kirchner C. (2003), Theorem Proving Modulo, Journal of Automated Reasoning 31(1), 33-72.

ES04

E{\'e}n N., Sörensson N. (2004), An Extensible SAT-solver, Giunchiglia E., Tacchella A., Proceedings of the 6th International Conference on Theory and Applications of Satisfiability Testing (Santa Margherita Ligure, Italy), 502-518, Lecture Notes in Computer Science 2919, Springer-Verlag.

GK03

Ganzinger H., Korovin K. (2003), New Directions in Instantiation-Based Theorem Proving, Kolaitis P., Proceedings of the 18th IEEE Symposium on Logic in Computer Science (Ottawa, Canada), 55-64, IEEE Press.

GK04

Ganzinger H., Korovin K. (2004), Integrating Equational Reasoning into Instantiation-Based Theorem Proving, Marcinkowski J., Tarlecki A., Proceedings of the 18th International Workshop on Computer Science Logic, 13th Annual Conference of the EACSL (Karpacz, Poland), 71-84, Lecture Notes in Computer Science 3210, Springer-Verlag.

GSS11

Gebser M., Sabuncu O., Schaub T. (2011), An Incremental Answer Set Programming Based System for Finite Model Computation, AI Communications 24(2), 195-212.

HKV10

Hoder K., Kovacs L., Voronkov A. (2010), Interpolation and Symbol Elimination in Vampire, Giesl J., Haehnle R., Proceedings of the 5th International Joint Conference on Automated Reasoning (Edinburgh, United Kingdom), 188-195, Lecture Notes in Artificial Intelligence 6173.

HKV11

Hoder K., Kovacs L., Voronkov A. (2011), Invariant Generation in Vampire, Abdulla P., Leino R., Proceedings of the 17th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (Saarbruecken, Germany), 60-64, Lecture Notes in Computer Science 6605, Springer-Verlag.

HV11

Hoder K., Voronkov A. (2011), Sine Qua Non for Large Theory Reasoning, Sofronie-Stokkermans V., Bj{\oe}rner N., Proceedings of the 23rd International Conference on Automated Deduction (Wroclaw, Poland), 299-314, Lecture Notes in Artificial Intelligence 6803, Springer-Verlag.

Iss90

Issar S. (1990), Path-Focused Duplication: A Search Procedure for General Matings, Dietterich T. Swartout W., Proceedings of the 8th National Conference on Artificial Intelligence (Boston, USA), 221-226, American Association for Artificial Intelligence / MIT Press.

JB+12

Jacquel M., Berkani K., Delahaye D., Dubois C. (2012), Tableaux Modulo Theories using Superdeduction: An Application to the Verification of B Proof Rules with the Zenon Automated Theorem Prover, Gramlich B., Miller D., Sattler U., Proceedings of the 6th International Joint Conference on Automated Reasoning (Manchester, United Kingdom), To appear, Lecture Notes in Artificial Intelligence.

KS10

Korovin K., Sticksel C. (2010), iProver-Eq - An Instantiation-Based Theorem Prover with Equality, Giesl J., Haehnle R., Proceedings of the 5th International Joint Conference on Automated Reasoning (Edinburgh, United Kingdom), 196-202, Lecture Notes in Artificial Intelligence 6173.

KS10

Korovin K., Sticksel C. (2010), Labelled Unit Superposition Calculi for Instantiation-based Reasoning, Fermüller C., Voronkov A., Proceedings of the 17th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (Yogyakarta, Indonesia), 459-473, Lecture Notes in Computer Science 6397, Springer-Verlag.

Kor08

Korovin K. (2008), iProver - An Instantiation-Based Theorem Prover for First-order Logic (System Description), Baumgartner P., Armando A., Gilles D., Proceedings of the 4th International Joint Conference on Automated Reasoning (Sydney, Australia), 292-298, Lecture Notes in Artificial Intelligence 5195.

Kor09

Korovin K. (2009), An Invitation to Instantiation-Based Reasoning: From Theory to Practice, Podelski A., Voronkov A., Wilhelm R., Volume in Memoriam of Harald Ganzinger, 163-166, Lecture Notes in Computer Science 5663, Springer-Verlag.

Kv+12

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