We study the problem of applying statistical methods for approximate model checking of probabilistic systems against properties encoded as PCTL formulas. Such approximate methods have been proposed primarily to deal with state-space explosion that makes the exact model checking by numerical methods practically infeasible for large systems. However, the existing statistical methods either consid...

We prove that Kreisel’s Conjecture is true, if Peano arithmetic is axiomatised using minimality principle and axioms of identity (theory PAM ). The result is independent on the choice of language of PAM . We also show that if infinitely many instances of A(x) are provable in a bounded number of steps in PAM then there exists k ∈ ω s.t. PAM ` ∀x > k A(x). The results imply that PAM does not prov...

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to...

The aim of this paper is to present a signature scheme in which the ability to sign messages of a signer is limited to a fixed number k of signatures. It is an identity-based signature schemein which each signature can be usedonly once. We called suchschemes “bounded life-span”. It is based on mental games and it uses zero-knowledge tools. A validation center is needed to initialize this identi...

We prove that the class of context-free languages with polynomially bounded ambiguity (PCFL) is the closure of the class of unambiguous languages (UCFL) under projections which deletes Parikh bounded symbols only. A symbol a is Parikh bounded in a language L if there is a constant c such that no word of L contains more than c occurrences of a. Furthermore PCFL is closed under the formally stron...

We consider L1-isotonic regression and L∞ isotonic and unimodal regression. For L1isotonic regression, we present a linear time algorithm when the number of outputs are bounded. We extend the algorithm to construct an approximate isotonic regression in linear time when the output range is bounded. We present linear time algorithms for L∞ isotonic and unimodal regression.

An online adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem for continuous-time uncertain nonlinear systems. A novel actor–critic–identifier (ACI) is proposed to approximate the Hamilton–Jacobi–Bellman equation using three neural network (NN) structures—actor and critic NNs approximate the optimal control and the optimal value function,...

The behavioral theory of dynamical system is used to address a deterministic system identification problem with a newly defined measure of misfit between data and linear time-invariant systems. An approximate model identification problem is formalized using this misfit criterium. In particular, Pareto optimal models are defined as feasible trade-offs between low complexity and low misfit models...