https://web4.cs.ucl.ac.uk/staff/j.gow/papers/clase05.pdf

Constructing Induction Rules for Deductive Synthesis Proofs

This is a tutorial paper on the tool Uppaal. Its goal is to be a short introduction on the flavor of timed automata implemented in the tool, to present its interface, and to explain how to use the tool. The contribution of the paper is to provide reference examples and modeling patterns.

A Tutorial on UPPAAL

This article proposes a taxonomy of model transformation, based on the discussions of a working group on model transformation of the Dagstuhl seminar on Language Engineering for Model-Driven Software Development. This taxonomy can be used, among others, to help developers in deciding which model transformation language or tool is best suited to carry out a particular model transformation activity.

/pdf/a-taxonomy-of-model-transformation-3cyk0bxs24.pdf

A Taxonomy of Model Transformation

Minimal recursion semantics (MRS) is a framework for computational semantics that is suitable for parsing and generation and that can be implemented in typed feature structure formalisms. We discuss why, in general, a semantic representation with minimal structure is desirable and illustrate how a descriptively adequate representation with a nonrecursive structure may be achieved. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. We have integrated MRS with a broad-coverage HPSG grammar.

/pdf/minimal-recursion-semantics-an-introduction-4hdjxn0v8q.pdf

Minimal Recursion Semantics An Introduction

Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to path-bounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.

/pdf/pex-white-box-test-generation-for-net-1kpnw99ssd.pdf

Pex: white box test generation for .NET

Neurosymbolic AI is an increasingly active area of research which aims to combine symbolic reasoning methods with deep learning to generate models with both high predictive performance and some degree of human-level comprehensibility. As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy in ways that facilitate interpretability, maintain performance, and integrate expert knowledge. Within this article, we survey a breadth of methods that perform neurosymbolic reasoning tasks on graph structures. To better compare the various methods, we propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: (1) logically-informed embedding approaches, (2) embedding approaches with logical constraints, and (3) rule-learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the applications on which these methods were primarily used and propose several prospective directions toward which this new field of research could evolve.

Neurosymbolic AI for Reasoning on Graph Structures: A Survey

We present a logic-based system for process specification and composition named WorkflowFM. It relies on an embedding of Classical Linear Logic and the so-called proofs-as-processes paradigm within the proof assistant HOL Light. This enables the specification of abstract processes as logical sequents and their composition via formal proof. The result is systematically translated to an executable workflow with formally verified consistency, rigorous resource accounting, and deadlock freedom. The 3-tiered server/client architecture of WorkflowFM allows multiple concurrent users to interact with the system through a purely diagrammatic interface, while the proof is performed automatically on the server.

/pdf/workflowfm-a-logic-based-framework-for-formal-process-3eoe8yzctr.pdf

WorkflowFM: A Logic-Based Framework for Formal Process Specification and Composition

In this paper, we investigate the potential of the Boyer-Moore waterfall model for the automation of inductive proofs within a modern proof assistant. We analyze the basic concepts and methodology underlying this 30-year-old model and implement a new, fully integrated tool in the theorem prover HOL Light that can be invoked as a tactic. We also describe several extensions and enhancements to the model. These include the integration of existing HOL Light proof procedures and the addition of state-of-the-art generalization techniques into the waterfall. Various features, such as proof feedback and heuristics dealing with non-termination, that are needed to make this automated tool useful within our interactive setting are also discussed. Finally, we present a thorough evaluation of the approach using a set of 150 theorems, and discuss the effectiveness of our additions and relevance of the model in light of our results.

The Boyer-Moore Waterfall Model Revisited.

The need for rigorous process composition is encountered in many situations pertaining to the development and analysis of complex systems. We discuss the use of Classical Linear Logic (CLL) for correct-by-construction resource-based process composition, with guaranteed deadlock freedom, systematic resource accounting, and concurrent execution. We introduce algorithms to automate the necessary inference steps for binary compositions of processes in parallel, conditionally, and in sequence. We combine decision procedures and heuristics to achieve intuitive and practically useful compositions in an applied setting.

A Pragmatic, Scalable Approach to Correct-by-Construction Process Composition Using Classical Linear Logic Inference

The goal of the ProofPeer project is to make collaborative theorem proving a reality. An important part of our plan to make this happen is ProofScript, a language designed to be the main user interface of ProofPeer. Of foremost importance in the design of ProofScript is its fit within a collaborative theorem proving environment. By this we mean that it needs to fit into an environment where peers who are not necessarily part of the current theorem proving and programming language communities work independently from but collaboratively with each other to produce formal definitions and proofs. All aspects of ProofScript are shaped by this design principle. In this paper we will discuss ProofScript’s most important aspect of being an integrated language both for interactive proof and for proof scripting.

/pdf/proofscript-proof-scripting-for-the-masses-4gh4w6w6rh.pdf

Jacques Fleuriot

Papers

Neurosymbolic AI for Reasoning on Graph Structures: A Survey

WorkflowFM: A Logic-Based Framework for Formal Process Specification and Composition

The Boyer-Moore Waterfall Model Revisited.

A Pragmatic, Scalable Approach to Correct-by-Construction Process Composition Using Classical Linear Logic Inference

ProofScript: Proof Scripting for the Masses