Adaptive Homing is in P

Homing preset and adaptive experiments with Finite State Machines (FSMs) are widely used when a non-initialized discrete event system is given for testing and thus, has to be set to the known state at the first step. The length of a shortest homing sequence is known to be exponential with respect to the number of states for a complete observable nondeterministic FSM while the problem of checking the existence of such sequence (Homing problem) is PSPACE-complete. In order to decrease the complexity of related problems, one can consider adaptive experiments when a next input to be applied to a system under experiment depends on the output responses to the previous inputs. In this paper, we study the problem of the existence of an adaptive homing experiment for complete observable nondeterministic machines. We show that if such experiment exists then it can be constructed with the use of a polynomial-time algorithm with respect to the number of FSM states.Comment: In Proceedings MBT 2015, arXiv:1504.0192

Advanced flight control system study

A fly by wire flight control system architecture designed for high reliability includes spare sensor and computer elements to permit safe dispatch with failed elements, thereby reducing unscheduled maintenance. A methodology capable of demonstrating that the architecture does achieve the predicted performance characteristics consists of a hierarchy of activities ranging from analytical calculations of system reliability and formal methods of software verification to iron bird testing followed by flight evaluation. Interfacing this architecture to the Lockheed S-3A aircraft for flight test is discussed. This testbed vehicle can be expanded to support flight experiments in advanced aerodynamics, electromechanical actuators, secondary power systems, flight management, new displays, and air traffic control concepts

Coding-theorem Like Behaviour and Emergence of the Universal Distribution from Resource-bounded Algorithmic Probability

Previously referred to as `miraculous' in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.Comment: 27 pages main text, 39 pages including supplement. Online complexity calculator: http://complexitycalculator.com

On the invertibility of finite state machines

Structural properties of finite state machines invertible with delay

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Automata & Sequential Machines, A Survey

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryContract DA-36-039-TR US AMC 02208(E

Experiments with finite state machines

The purpose of this study is to introduce and illustrate the various types of experiments with finite state machines. A finite state machine is an abstract object composed of a finite number of input, output and state symbols. The behavior of the machine is described by a functional relationship between input, output and state. In designing a finite state machine it often happens that two states represent the same internal condition. Therefore it is desirable to develop a technique for transforming one machine into another which has no redundant states, so that both have the same behavior. The definition of k-equivalent and k-distinguishable are useful in an algorithm which is developed to determine which states of the machine are equivalent. The machine with no two equivalent states is a reduced machine. An experiment on a reduced state machine consists of applying an input sequence and observing the output. The classification of experiments is (1) simple and multiple experiments; (2) preset and adaptive experiments; (3) distinguishing and homing experiments. The successor tree is a useful device in designing the experiment. The successor tree is terminated by specific rules in each experiment. Homing and distinguishing experiments can be either preset and adaptive. All reduced machines have a homing sequence. A distinguishing sequence is sometimes possible in both preset and adaptive form. However, some reduced machines have only an adaptive experiment and some do not have any simple distinguishing sequence at all