Crypto-Verifying Protocol Implementations in ML

We intend to narrow the gap between concrete implementations and verified models of cryptographic protocols. We consider protocols implemented in F#, a variant of ML, and verified using CryptoVerif, Blanchet's protocol verifier for computational cryptography. We experiment with compilers from F# code to CryptoVerif processes, and from CryptoVerif declarations to F# code. We present two case studies: an implementation of the Otway-Rees protocol, and an implementation of a simplified password-based authentication protocol. In both cases, we obtain concrete security guarantees for a computational model closely related to executable code

The role of computational logic as a hinge paradigm among deduction, problem solving, programming, and parallelism

This paper presents some brief considerations on the role of Computational Logic in the construction of Artificial Intelligence systems and in programming in general. It does not address how the many problems in AI can be solved but, rather more modestly, tries to point out some advantages of Computational Logic as a tool for the AI scientist in his quest. It addresses the interaction between declarative and procedural views of programs (deduction and action), the impact of the intrinsic limitations of logic, the relationship with other apparently competing computational paradigms, and finally discusses implementation-related issues, such as the efficiency of current implementations and their capability for efficiently exploiting existing and future sequential and parallel hardware. The purpose of the discussion is in no way to present Computational Logic as the unique overall vehicle for the development of intelligent systems (in the firm belief that such a panacea is yet to be found) but rather to stress its strengths in providing reasonable solutions to several aspects of the task

Abstract State Machines 1988-1998: Commented ASM Bibliography

An annotated bibliography of papers which deal with or use Abstract State Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm

Extending and Relating Semantic Models of Compensating CSP

Business transactions involve multiple partners coordinating and interacting with each other. These transactions have hierarchies of activities which need to be orchestrated. Usual database approaches (e.g.,checkpoint, rollback) are not applicable to handle faults in a long running transaction due to interaction with multiple partners. The compensation mechanism handles faults that can arise in a long running transaction. Based on the framework of Hoare's CSP process algebra, Butler et al introduced Compensating CSP (cCSP), a language to model long-running transactions. The language introduces a method to declare a transaction as a process and it has constructs for orchestration of compensation. Butler et al also defines a trace semantics for cCSP. In this thesis, the semantic models of compensating CSP are extended by defining an operational semantics, describing how the state of a program changes during its execution. The semantics is encoded into Prolog to animate the specification. The semantic models are further extended to define the synchronisation of processes. The notion of partial behaviour is defined to model the behaviour of deadlock that arises during process synchronisation. A correspondence relationship is then defined between the semantic models and proved by using structural induction. Proving the correspondence means that any of the presentation can be accepted as a primary definition of the meaning of the language and each definition can be used correctly at different times, and for different purposes. The semantic models and their relationships are mechanised by using the theorem prover PVS. The semantic models are embedded in PVS by using Shallow embedding. The relationships between semantic models are proved by mutual structural induction. The mechanisation overcomes the problems in hand proofs and improves the scalability of the approach

AutoBayes: A System for Generating Data Analysis Programs from Statistical Models

Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis, which use methods from probability theory and numerical analysis, are well-founded but difficult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe AutoBayes, a program synthesis system for the generation of data analysis programs from statistical models. A statistical model specifies the properties for each problem variable (i.e., observation or parameter) and its dependencies in the form of a probability distribution. It is a fully declarative problem description, similar in spirit to a set of differential equations. From such a model, AutoBayes generates optimized and fully commented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Code is produced by a schema-guided deductive synthesis process. A schema consists of a code template and applicability constraints which are checked against the model during synthesis using theorem proving technology. AutoBayes augments schema-guided synthesis by symbolic-algebraic computation and can thus derive closed-form solutions for many problems. It is well-suited for tasks like estimating best-fitting model parameters for the given data. Here, we describe AutoBayes's system architecture, in particular the schema-guided synthesis kernel. Its capabilities are illustrated by a number of advanced textbook examples and benchmarks

A comparison of languages which operationalise and formalise {KADS} models of expertise

In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area