4 research outputs found
Capturing proof process
PhD ThesisProof automation is a common bottleneck for industrial adoption of formal methods.
Heuristic search techniques fail to discharge every proof obligation (PO), and
significant effort is spent on proving the remaining ones interactively. Luckily,
they usually fall into several proof families, where a single idea is required to discharge
all similar POs. However, interactive formal proof requires expertise and
is expensive: repeating the ideas over multiple proofs adds up to significant costs.
The AI4FM research project aims to alleviate the repetitive effort by “learning”
from an expert doing interactive proof. The expert’s proof attempts can give rise
to reusable strategies, which capture the ideas necessary to discharge similar POs.
Automatic replay of these strategies would complete the remaining proof tasks
within the same family, enabling the expert to focus on novel proof ideas.
This thesis presents an architecture to capture the expert’s proof ideas as a highlevel
proof process. Expert insight is not reflected in low-level proof scripts, therefore
a generic ProofProcess framework is developed to capture high-level proof information,
such as proof intent and important proof features of the proof steps taken.
The framework accommodates branching to represent the actual proof structure
as well as layers of abstraction to accommodate different granularities. The full
history of how the proof was discovered is recorded, including multiple attempts
to capture alternative, failed or unfinished versions.
A prototype implementation of the ProofProcess framework is available, including
integrations with Isabelle and Z/EVES theorem provers. Two case studies illustrate
how the ProofProcess systems are used to capture high-level proof processes
in examples from industrial-style formal developments. Reuse of the captured
information to discharge similar proofs within the examples is also explored.
The captured high-level information facilitates extraction of reusable proof
strategies. Furthermore, the data could be used for proof maintenance, training,
proof metrics, and other use cases
A Model for Capturing and Replaying Proof Strategies
Peer-to-peer (P2P) computing has shown an unexpected growth and development during the recent years. P2P networking is being applied from B2B enterprise solutions to more simple, every-day file-sharing applications like Gnutella clients. In this paper we are investigating the use of the Gnutella P2P protocol for Information Retrieval by means of building and evaluating a general-purpose Web meta-search engine