209 research outputs found
Towards Ranking Geometric Automated Theorem Provers
The field of geometric automated theorem provers has a long and rich history,
from the early AI approaches of the 1960s, synthetic provers, to today
algebraic and synthetic provers.
The geometry automated deduction area differs from other areas by the strong
connection between the axiomatic theories and its standard models. In many
cases the geometric constructions are used to establish the theorems'
statements, geometric constructions are, in some provers, used to conduct the
proof, used as counter-examples to close some branches of the automatic proof.
Synthetic geometry proofs are done using geometric properties, proofs that can
have a visual counterpart in the supporting geometric construction.
With the growing use of geometry automatic deduction tools as applications in
other areas, e.g. in education, the need to evaluate them, using different
criteria, is felt. Establishing a ranking among geometric automated theorem
provers will be useful for the improvement of the current
methods/implementations. Improvements could concern wider scope, better
efficiency, proof readability and proof reliability.
To achieve the goal of being able to compare geometric automated theorem
provers a common test bench is needed: a common language to describe the
geometric problems; a comprehensive repository of geometric problems and a set
of quality measures.Comment: In Proceedings ThEdu'18, arXiv:1903.1240
Towards a Geometry Automated Provers Competition
The geometry automated theorem proving area distinguishes itself by a large
number of specific methods and implementations, different approaches
(synthetic, algebraic, semi-synthetic) and different goals and applications
(from research in the area of artificial intelligence to applications in
education).
Apart from the usual measures of efficiency (e.g. CPU time), the possibility
of visual and/or readable proofs is also an expected output against which the
geometry automated theorem provers (GATP) should be measured.
The implementation of a competition between GATP would allow to create a test
bench for GATP developers to improve the existing ones and to propose new ones.
It would also allow to establish a ranking for GATP that could be used by
"clients" (e.g. developers of educational e-learning systems) to choose the
best implementation for a given intended use.Comment: In Proceedings ThEdu'19, arXiv:2002.1189
Integrating DGSs and GATPs in an Adaptative and Collaborative Blended-Learning Web-Environment
The area of geometry with its very strong and appealing visual contents and
its also strong and appealing connection between the visual content and its
formal specification, is an area where computational tools can enhance, in a
significant way, the learning environments.
The dynamic geometry software systems (DGSs) can be used to explore the
visual contents of geometry. This already mature tools allows an easy
construction of geometric figures build from free objects and elementary
constructions. The geometric automated theorem provers (GATPs) allows formal
deductive reasoning about geometric constructions, extending the reasoning via
concrete instances in a given model to formal deductive reasoning in a
geometric theory.
An adaptative and collaborative blended-learning environment where the DGS
and GATP features could be fully explored would be, in our opinion a very rich
and challenging learning environment for teachers and students.
In this text we will describe the Web Geometry Laboratory a Web environment
incorporating a DGS and a repository of geometric problems, that can be used in
a synchronous and asynchronous fashion and with some adaptative and
collaborative features.
As future work we want to enhance the adaptative and collaborative aspects of
the environment and also to incorporate a GATP, constructing a dynamic and
individualised learning environment for geometry.Comment: In Proceedings THedu'11, arXiv:1202.453
FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning
The human-like automatic deductive reasoning has always been one of the most
challenging open problems in the interdiscipline of mathematics and artificial
intelligence. This paper is the third in a series of our works. We built a
neural-symbolic system, called FGeoDRL, to automatically perform human-like
geometric deductive reasoning. The neural part is an AI agent based on
reinforcement learning, capable of autonomously learning problem-solving
methods from the feedback of a formalized environment, without the need for
human supervision. It leverages a pre-trained natural language model to
establish a policy network for theorem selection and employ Monte Carlo Tree
Search for heuristic exploration. The symbolic part is a reinforcement learning
environment based on geometry formalization theory and FormalGeo, which models
GPS as a Markov Decision Process. In this formal symbolic system, the known
conditions and objectives of the problem form the state space, while the set of
theorems forms the action space. Leveraging FGeoDRL, we have achieved readable
and verifiable automated solutions to geometric problems. Experiments conducted
on the formalgeo7k dataset have achieved a problem-solving success rate of
86.40%. The project is available at https://github.com/PersonNoName/FGeoDRL.Comment: 15 page
Automating the Generation of High School Geometry Proofs using Prolog in an Educational Context
When working on intelligent tutor systems designed for mathematics education
and its specificities, an interesting objective is to provide relevant help to
the students by anticipating their next steps. This can only be done by
knowing, beforehand, the possible ways to solve a problem. Hence the need for
an automated theorem prover that provide proofs as they would be written by a
student. To achieve this objective, logic programming is a natural tool due to
the similarity of its reasoning with a mathematical proof by inference. In this
paper, we present the core ideas we used to implement such a prover, from its
encoding in Prolog to the generation of the complete set of proofs. However,
when dealing with educational aspects, there are many challenges to overcome.
We also present the main issues we encountered, as well as the chosen
solutions.Comment: In Proceedings ThEdu'19, arXiv:2002.1189
O método do ângulo completo no sistema OpenGeoProver
Dissertação de Mestrado em Matemática apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraO método do ângulo completo para geometria euclideana construtiva foi proposto por Chou, Gao e Zhang no início dos anos 1990. Este método, uma extensão do método da área proposto pelos mesmos autores, produz demonstrações legíveis e de um modo eficiente demonstra muitos teoremas não triviais. Pode ser considerado como um dos métodos mais interessante e de maior sucesso na demonstração de teoremas em geometria e, possivelmente, o mais bem sucedido na produção de demonstrações automáticas legíveis. Nesta dissertação de mestrado faz-se a apresentação do mêtodo do ângulo completo e demonstram-se muitos dos seus lemas. Descreve-se ainda a planificação da implementação, em código livre, do método do ângulo completo.The full-angle method for euclidean constructive geometry was proposed by Chou, Gao, Zhang in early 1990’s. The method, an extension of the area method proposed by the same authors, produces humanreadable proofs and can efficiently prove many non-trivial theorems. It can be considered as one of the most interesting and most successful methods in geometry theorem proving and probably the most successful in the domain of automated production of readable proofs. In this master thesis a presentation of the full-angle method is made and several of its lemmas are proved. A plannification of the implementation, in open source code, of the full-angle method is also described
Applied Formal Methods in Wireless Sensor Networks
This work covers the application of formal methods to the world of wireless sensor networks. Mainly two different perspectives are analyzed through mathematical models which can be distinct for example into qualitative statements like "Is the system error free?" From the perspective of quantitative propositions we investigate protocol optimal parameter settings for an energy efficient operation
Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
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