Search results for "algoritmiikka"

Progress Checking for Dummies

2018

Verification of progress properties is both conceptually and technically significantly more difficult than verification of safety and deadlock properties. In this study we focus on the conceptual side. We make a simple modification to a well-known model to demonstrate that it passes progress verification although the resulting model is intuitively badly incorrect. Then we point out that the error can be caught easily by adding a termination branch to the system. We compare the use of termination branches to the established method of addressing the same need, that is, weak fairness. Then we discuss another problem that may cause failure of catching progress errors even with weak fairness. Fi…

Stubborn sets, frozen actions, and fair testing

2021

Many partial order methods use some special condition for ensuring that the analysis is not terminated prematurely. In the case of stubborn set methods for safety properties, implementation of the condition is usually based on recognizing the terminal strong components of the reduced state space and, if necessary, expanding the stubborn sets used in their roots. In an earlier study it was pointed out that if the system may execute a cycle consisting of only invisible actions and that cycle is concurrent with the rest of the system in a non-obvious way, then the method may be fooled to construct all states of the full parallel composition. This problem is solved in this study by a method tha…

Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms : An experimental analysis

2022

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are use…

A Detailed Account of The Inconsistent Labelling Problem of Stutter-Preserving Partial-Order Reduction

2021

One of the most popular state-space reduction techniques for model checking is partial-order reduction (POR). Of the many different POR implementations, stubborn sets are a very versatile variant and have thus seen many different applications over the past 32 years. One of the early stubborn sets works shows how the basic conditions for reduction can be augmented to preserve stutter-trace equivalence, making stubborn sets suitable for model checking of linear-time properties. In this paper, we identify a flaw in the reasoning and show with a counter-example that stutter-trace equivalence is not necessarily preserved. We propose a stronger reduction condition and provide extensive new correc…

Kvanttitietokone voi laskea minuutissa laskun, jota tavallinen tietokone laskee vuosisadan

2023

World as Numbers : Living in an Algorithmic Culture

2016

There is a long tradition of trying to grasp the world around us in mathematical terms. From early man perceiving the motion of celestial bodies, to Pythagoras’ ‘celestial harmony’ and to Kepler’s and Newton’s laws of motion, calculations have provided ways to reduce the messy world of instances to a handful of mathematical formulae. Einstein’s Relativity Theory, and even more the quantum physics, complicated the situation, but still, even with random elements involved, the statistics could provide a model to understand the processes of the universe. When calculations grew ever more complex, and computers became necessary tools to deal with them, this lead to the idea of seeing the whole of…

Explainable Fuzzy AI Challenge 2022 : Winner’s Approach to a Computationally Efficient and Explainable Solution

2022

An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based …

Improving Scalable K-Means++

2021

Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces produced by the random projection method for the initialization. The proposed methods are scalable and can be run in parallel, which make them suitable for initializing large-scale problems. In the experiments, comparison of the proposed methods to the K-means++ and K-means‖ methods is conducted using an extensive set of reference and synthetic large-scale datasets. Concerning the latter, a novel high-dimensional clustering data generation …