• Corpus ID: 15323764

Learning from an expert's proof: AI4FM

@inproceedings{Freitas2010LearningFA,
  title={Learning from an expert's proof: AI4FM},
  author={Leo Freitas and Cliff B. Jones},
  year={2010}
}
This position paper outlines the background and current approaches taken within AI4FM, a 4-year research project aimed at combining AI methodologies to aid proof discovery of certain families of interest. Namely, those repeated proofs often appearing in the application of verification to industrial applications. 
ai4fm.org
5 Citations
Methods Citations
3

5 Citations

Can a system learn from interactive proofs?

The on-going research in a project which is investigating how to learn from one interactive proof so that other similar proofs can be completed automatically is set out.

Capturing proof process

This thesis presents an architecture to capture the expert’s proof ideas as a highlevel proof process, which accommodates branching to represent the actual proof structure as well as layers of abstraction to accommodate different granularities.

Ours Is to Reason Why

The central idea is to "learn" new ways of discharging families of proof obligations by tracking one interactive proof performed by an expert, which will facilitate the discharge of significant "families" of recalcitrant proof tasks.

Formalising Workflows Partitioning over Federated Clouds: Multi-level Security and Costs

We present an abstract formalisation of federated cloud workflows using the Z notation. Various properties of interest are observed in the possible deployments symbolically calculated by the Z/EVES…

Formalizing workflows partitioning over federated clouds: multi-level security and costs

An formalization of federated cloud workflows using the Z notation with properties of interest being observed by the possible deployments, which are symbolically calculated by the Z/EVES theorem prover is presented.

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