Showing papers by "Jacques Fleuriot published in 2018"

Machine Learning for Inductive Theorem Proving.

Abstract: Over the past few years, machine learning has been successfully combined with automated theorem provers to prove conjectures from proof assistants. However, such approaches do not usually focus on inductive proofs. In this work, we explore a combination of machine learning, a simple Boyer-Moore model and ATPs as a means of improving the automation of inductive proofs in the proof assistant HOL Light. We evaluate the framework using a number of inductive proof corpora. In each case, our approach achieves a higher success rate than running ATPs or the Boyer-Moore tool individually.

The Boyer-Moore Waterfall Model Revisited.

Abstract: 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.

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

Abstract: 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

Abstract: 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.

Correct-by-construction Process Composition Using Classical Linear Logic Inference

Abstract: 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.

Correct by Construction Resource-based Process Composition

Abstract: 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. Abstract processes are specified as CLL sequents describing the types of input and output resources. The proofs-as-processes paradigm and its recent evolutions enable the composition of such processes via logical inference, with mathematical guarantees when it comes to concurrent execution, deadlock freedom, and systematic resource accounting. 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. Our work is implemented within the formal setting of the HOL Light proof assistant and incorporated in the WorkflowFM prototype to facilitate diagrammatic, correct-by-construction process workflows.