OTTER 3.3 reference manual.

McCune, William

doi:10.2172/822573

Cited by 93 publications

(68 citation statements)

References 23 publications

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“…By construction this is false (in Step 1 we defined R, G, B such that |R| < |G|). Hence, we have found an interpretation for predicates in S and in R, namely R, G, B, edge which is a model of the left part of condition (24) and not of its right part. Thus, the second-order non-equivalence stated in (24) holds.…”

Section: Figmentioning

confidence: 84%

“…In our research we used the first-order refutation theorem prover Otter [24], and the finite model finder Mace [23], both in full-automatic mode.…”

Section: Experiments With the Theorem Provermentioning

confidence: 99%

“…We report the results on using theorem provers and finite model finders (Otter [24], and Mace [23], respectively) for reasoning on specifications of constraint problems, represented as existential second order logic (ESO) formulae. We focus on two forms of reasoning:…”

Section: Introductionmentioning

confidence: 99%

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Using a Theorem Prover for Reasoning on Constraint Problems

Cadoli

Mancini

2005

Lecture Notes in Computer Science

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Abstract. Specifications of constraint problems can be considered logical formulae. As a consequence, it is possible to infer their properties by means of automated reasoning tools, with the goal of automatically synthesizing transformations that can make the solving process more efficient. The purpose of this paper is to link two important technologies: automated theorem proving (ATP) and constraint programming (CP). We report the results on using ATP technology for checking existence of symmetries, checking whether a given formula breaks a symmetry, and checking existence of functional dependencies in a specification. The output of the reasoning phase is a transformed constraint program, consisting in a reformulated specification and, possibly, a search strategy. We show our techniques on problems such as Graph coloring, Sailco inventory, and Protein folding.

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Section: Figmentioning

confidence: 84%

“…In our research we used the first-order refutation theorem prover Otter [24], and the finite model finder Mace [23], both in full-automatic mode.…”

Section: Experiments With the Theorem Provermentioning

confidence: 99%

See 1 more Smart Citation

Using a Theorem Prover for Reasoning on Constraint Problems

Cadoli

Mancini

2005

Lecture Notes in Computer Science

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“…The approach of using finite model finders for looking for small counter-examples has already been successfully adopted in other contexts, e.g., software verification, and systems exhibiting such a feature do exist (cf., e.g., Alloy 1 ). In our research we used the first-order refutation theorem prover Otter [24], and the finite model finder Mace [23], both in full-automatic mode.…”

Section: Experiments With the Theorem Provermentioning

confidence: 99%

Using a Theorem Prover for Reasoning on Constraint Problems

Cadoli

Mancini

2007

Applied Artificial Intelligence

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“…Answer predicates are implemented in a number of theorem provers, including Vampire, Otter [28] and Gandalf [43]. For Vampire, one can also specify that only definite answers should be considered.…”

Section: Adapting Vampire For Large Ontologiesmentioning

confidence: 99%

Reasoning Support for Expressive Ontology Languages Using a Theorem Prover

Horrocks

Воронков

2006

Lecture Notes in Computer Science

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Abstract. It is claimed in [45] that first-order theorem provers are not efficient for reasoning with ontologies based on description logics compared to specialised description logic reasoners. However, the development of more expressive ontology languages requires the use of theorem provers able to reason with full first-order logic and even its extensions. So far, theorem provers have extensively been used for running experiments over TPTP containing mainly problems with relatively small axiomatisations. A question arises whether such theorem provers can be used to reason in real time with large axiomatisations used in expressive ontologies such as SUMO. In this paper we answer this question affirmatively by showing that a carefully engineered theorem prover can answer queries to ontologies having over 15,000 first-order axioms with equality. Ontologies used in our experiments are based on the language KIF, whose expressive power goes far beyond the description logic based languages currently used in the Semantic Web.State-of-the-art theorem provers for first-order logic (FOL) are highly sophisticated and efficient programs. Moreover, they are very flexible tools and can be tuned to a number of applications. For example, Vampire [35] provides a large collection of parameters that can be used to give better performance for various classes of applications. In addition, Vampire implements a number of literal selection functions and internally contains a library for defining such functions in a simple way; this makes it possible to simulate various proof-search algorithms and even provide decision procedures for decidable classes of first-order logic (see, e.g., [23]).However, there was a common belief that provers like Vampire cannot directly be used for efficient reasoning with very large ontologies using expressive languages such as KIF [14] for two reasons. Firstly, these provers are optimised for reasoning with relatively small axiomatisations. Secondly, they do not support some extensions of FOL required in KIF.In this paper we describe an adaptation of Vampire to support reasoning for expressive ontology languages and present experimental results which show that it can be used for efficient reasoning with large ontologies using extensions of the first-order language.The authors are partially supported by grants from EPSRC. This paper is structured as follows. In Section 1 we briefly overview expressive languages for ontologies, including the language KIF, and FO provers. In Section 2 we describe the adaptation of Vampire for reasoning with large ontologies. For our experiments we selected ontologies implemented in KIF since they offer a high degree of sophistication compared to ontologies using Description Logic (DL) based languages, and there are publicly available KIF-based ontologies containing thousands of FO formulas with equality. However, our adaptation is quite general, and could be used for other expressive ontology languages.Note that there is no way to compare Vampire with description logic prove...

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OTTER 3.3 reference manual.

Cited by 93 publications

References 23 publications

Using a Theorem Prover for Reasoning on Constraint Problems

Using a Theorem Prover for Reasoning on Constraint Problems

Using a Theorem Prover for Reasoning on Constraint Problems

Reasoning Support for Expressive Ontology Languages Using a Theorem Prover

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