A formalization of multi-tape Turing machines

We discuss the formalization, in the Matita Theorem Prover, of basic results on multi-tapes Turing machines, up to the existence of a (certified) Universal Machine, and propose it as a natural benchmark for comparing different interactive provers and assessing the state of the art in the mechanization of formal reasoning. The work is meant to be a preliminary step towards the creation of a formal repository in Complexity Theory, and is a small piece in our long-term Reverse Complexity program, aiming to a comfortable, machine independent axiomatization of the field

Using SMT Solving for the Lookup of Infeasible Paths in Binary Programs

International audienceWorst-Case Execution Time (WCET) is a key component to check temporal constraints of realtime systems. WCET by static analysis provides a safe upper bound. While hardware modelling is now efficient, loss of precision stems mainly in the inclusion of infeasible execution paths in the WCET calculation. This paper proposes a new method to detect such paths based on static analysis of machine code and the feasibility test of conditions using Satisfiability Modulo Theory (SMT) solvers. The experimentation shows promising results although the expected precision was slightly lowered due to clamping operations needed to cope with complexity explosion. An important point is that the implementation has been performed in the OTAWA framework and is independent of any instruction set thanks to its semantic instructions

Causal inference using the algorithmic Markov condition

Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when only single observations are present. We develop a theory how to generate causal graphs explaining similarities between single objects. To this end, we replace the notion of conditional stochastic independence in the causal Markov condition with the vanishing of conditional algorithmic mutual information and describe the corresponding causal inference rules. We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs. This insight provides a theoretical foundation of a heuristic principle proposed in earlier work. We also discuss how to replace Kolmogorov complexity with decidable complexity criteria. This can be seen as an algorithmic analog of replacing the empirically undecidable question of statistical independence with practical independence tests that are based on implicit or explicit assumptions on the underlying distribution.Comment: 16 figure

Quantum Kolmogorov Complexity and Quantum Key Distribution

We discuss the Bennett-Brassard 1984 (BB84) quantum key distribution protocol in the light of quantum algorithmic information. While Shannon's information theory needs a probability to define a notion of information, algorithmic information theory does not need it and can assign a notion of information to an individual object. The program length necessary to describe an object, Kolmogorov complexity, plays the most fundamental role in the theory. In the context of algorithmic information theory, we formulate a security criterion for the quantum key distribution by using the quantum Kolmogorov complexity that was recently defined by Vit\'anyi. We show that a simple BB84 protocol indeed distribute a binary sequence between Alice and Bob that looks almost random for Eve with a probability exponentially close to 1.Comment: typos correcte