Dealing with Degeneracies in Automated Theorem Proving in Geometry

We report, through different examples, the current development in GeoGebra, a widespread Dynamic Geometry software, of geometric automated reasoning tools by means of computational algebraic geometry algorithms. Then we introduce and analyze the case of the degeneracy conditions that so often arise in the automated deduction in geometry context, proposing two different ways for dealing with them. One is working with the saturation of the hypotheses ideal with respect to the ring of geometrically independent variables, as a way to globally handle the statement over all non-degenerate components. The second is considering the reformulation of the given hypotheses ideal—considering the independent variables as invertible parameters—and developing and exploiting the specific properties of this zero-dimensional case to analyze individually the truth of the statement over the different non-degenerate components.

The 10th IJCAR automated theorem proving system competition – CASC-J10

The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic Automated Theorem Proving (ATP) systems. CASC-J10 was the twenty-fifth competition in the CASC series. Twenty-four ATP systems and system variants competed in the various competition divisions. This paper presents an outline of the competition design, and a commentated summary of the results.

On the arithmetic of automated theorem proving

We propose a model to assign prime numbers to axioms and theorems, then by comparing equivalent numbers, it results in new equivalent theorems.

Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)

Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.

Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving

Modern theorem provers such as Vampire utilise premise selection algorithms to control the proof search explosion. Premise selection heuristics often employ an array of continuous and discrete parameters. The quality of recommended premises varies depending on the parameter assignment. In this work, we introduce a principled probabilistic framework for optimisation of a premise selection algorithm. We present results using Sumo Inference Engine (SInE) and the Archive of Formal Proofs (AFP) as a case study. Our approach can be used to optimise heuristics on large theories in minimum number of steps.