13,965 research outputs found
Improving QED-Tutrix by Automating the Generation of Proofs
The idea of assisting teachers with technological tools is not new.
Mathematics in general, and geometry in particular, provide interesting
challenges when developing educative softwares, both in the education and
computer science aspects. QED-Tutrix is an intelligent tutor for geometry
offering an interface to help high school students in the resolution of
demonstration problems. It focuses on specific goals: 1) to allow the student
to freely explore the problem and its figure, 2) to accept proofs elements in
any order, 3) to handle a variety of proofs, which can be customized by the
teacher, and 4) to be able to help the student at any step of the resolution of
the problem, if the need arises. The software is also independent from the
intervention of the teacher. QED-Tutrix offers an interesting approach to
geometry education, but is currently crippled by the lengthiness of the process
of implementing new problems, a task that must still be done manually.
Therefore, one of the main focuses of the QED-Tutrix' research team is to ease
the implementation of new problems, by automating the tedious step of finding
all possible proofs for a given problem. This automation must follow
fundamental constraints in order to create problems compatible with QED-Tutrix:
1) readability of the proofs, 2) accessibility at a high school level, and 3)
possibility for the teacher to modify the parameters defining the
"acceptability" of a proof. We present in this paper the result of our
preliminary exploration of possible avenues for this task. Automated theorem
proving in geometry is a widely studied subject, and various provers exist.
However, our constraints are quite specific and some adaptation would be
required to use an existing prover. We have therefore implemented a prototype
of automated prover to suit our needs. The future goal is to compare
performances and usability in our specific use-case between the existing
provers and our implementation.Comment: In Proceedings ThEdu'17, arXiv:1803.0072
A framework for proof certificates in finite state exploration
Model checkers use automated state exploration in order to prove various
properties such as reachability, non-reachability, and bisimulation over state
transition systems. While model checkers have proved valuable for locating
errors in computer models and specifications, they can also be used to prove
properties that might be consumed by other computational logic systems, such as
theorem provers. In such a situation, a prover must be able to trust that the
model checker is correct. Instead of attempting to prove the correctness of a
model checker, we ask that it outputs its "proof evidence" as a formally
defined document--a proof certificate--and that this document is checked by a
trusted proof checker. We describe a framework for defining and checking proof
certificates for a range of model checking problems. The core of this framework
is a (focused) proof system that is augmented with premises that involve "clerk
and expert" predicates. This framework is designed so that soundness can be
guaranteed independently of any concerns for the correctness of the clerk and
expert specifications. To illustrate the flexibility of this framework, we
define and formally check proof certificates for reachability and
non-reachability in graphs, as well as bisimulation and non-bisimulation for
labeled transition systems. Finally, we describe briefly a reference checker
that we have implemented for this framework.Comment: In Proceedings PxTP 2015, arXiv:1507.0837
Robust Computer Algebra, Theorem Proving, and Oracle AI
In the context of superintelligent AI systems, the term "oracle" has two
meanings. One refers to modular systems queried for domain-specific tasks.
Another usage, referring to a class of systems which may be useful for
addressing the value alignment and AI control problems, is a superintelligent
AI system that only answers questions. The aim of this manuscript is to survey
contemporary research problems related to oracles which align with long-term
research goals of AI safety. We examine existing question answering systems and
argue that their high degree of architectural heterogeneity makes them poor
candidates for rigorous analysis as oracles. On the other hand, we identify
computer algebra systems (CASs) as being primitive examples of domain-specific
oracles for mathematics and argue that efforts to integrate computer algebra
systems with theorem provers, systems which have largely been developed
independent of one another, provide a concrete set of problems related to the
notion of provable safety that has emerged in the AI safety community. We
review approaches to interfacing CASs with theorem provers, describe
well-defined architectural deficiencies that have been identified with CASs,
and suggest possible lines of research and practical software projects for
scientists interested in AI safety.Comment: 15 pages, 3 figure
A theorem prover-based analysis tool for object-oriented databases
We present a theorem-prover based analysis tool for object-oriented database systems with integrity constraints. Object-oriented database specifications are mapped to higher-order logic (HOL). This allows us to reason about the semantics of database operations using a mechanical theorem prover such as Isabelle or PVS. The tool can be used to verify various semantics requirements of the schema (such as transaction safety, compensation, and commutativity) to support the advanced transaction models used in workflow and cooperative work. We give an example of method safety analysis for the generic structure editing operations of a cooperative authoring system
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
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