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Sometimes in practice it is necessary to calculate the probability of an uncertain cause, taking into account some observed evidence. For example, we would like to know the probability of a particular disease when we observe the patient’s symptoms. Such problems are often complex with many interrelated variables. There may be many symptoms and even more potential causes. In practice, it is usually possible to obtain only the inverse conditional probability, the probability of evidence giving the cause, the probability of observing the symptoms if the patient has the disease.Intelligent systems must think about their environment. For example, a robot needs to know about the possible outcomes of its actions, and the system of medical experts needs to know what causes what consequences. Intelligent systems began to use probabilistic methods to deal with the uncertainty of the real world. Instead of building a special system of probabilistic reasoning for each new program, we would like a common framework that would allow probabilistic reasoning in any new program without restoring everything from scratch. This justifies the relevance of the developed genetic algorithm. Bayesian networks, which first appeared in the work of Judas Pearl and his colleagues in the late 1980s, offer just such an independent basis for plausible reasoning.This article presents the genetic algorithm for learning the structure of the Bayesian network that searches the space of the graph, uses mutation and crossover operators. The algorithm can be used as a quick way to learn the structure of a Bayesian network with as few constraints as possible.learn the structure of a Bayesian network with as few constraints as possible., Розроблено генетичний алгоритм для навчання структури баєсівської мережі з повністю спостережуваного набору даних, який здійснює пошук по простору графа і використовує оператори мутації та кросовера. Цей алгоритм здійснює пошук по простору спрямованих ациклічних графів.

Design fixation is an effect that prevents designers from producing creative solutions. This document presents an experiment carried out at a studio course which demonstrates the existence of high rates of fixation among Architecture students, where decision making while designing is characterized by the transposition of typified components from referenced cases. As a consequence a Case-Based Design Aid (CBDA) is formulated to support design processes by enabling students to visualize similar problems to those presented in studio courses which have been solved by professionals in real practices. The strategy is to relate the technical data of cases to their real contexts and to the situations that produced them into acyclic graphs. The final objective is to increase students´ analysis, design, and creativity capacities by referencing precedent practices.

Sometimes in practice it is necessary to calculate the probability of an uncertain cause, taking into account some observed evidence. For example, we would like to know the probability of a particular disease when we observe the patient’s symptoms. Such problems are often complex with many interrelated variables. There may be many symptoms and even more potential causes. In practice, it is usually possible to obtain only the inverse conditional probability, the probability of evidence giving the cause, the probability of observing the symptoms if the patient has the disease. Intelligent systems must think about their environment. For example, a robot needs to know about the possible outcomes of its actions, and the system of medical experts needs to know what causes what consequences. Intelligent systems began to use probabilistic methods to deal with the uncertainty of the real world. Instead of building a special system of probabilistic reasoning for each new program, we would like a common framework that would allow probabilistic reasoning in any new program without restoring everything from scratch. This justifies the relevance of the developed genetic algorithm. Bayesian networks, which first appeared in the work of Judas Pearl and his colleagues in the late 1980s, offer just such an independent basis for plausible reasoning. This article presents the genetic algorithm for learning the structure of the Bayesian network that searches the space of the graph, uses mutation and crossover operators. The algorithm can be used as a quick way to learn the structure of a Bayesian network with as few constraints as possible. learn the structure of a Bayesian network with as few constraints as possible.

Sometimes in practice it is necessary to calculate the probability of an uncertain cause, taking into account some observed evidence. For example, we would like to know the probability of a particular disease when we observe the patient’s symptoms. Such problems are often complex with many interrelated variables. There may be many symptoms and even more potential causes. In practice, it is usually possible to obtain only the inverse conditional probability, the probability of evidence giving the cause, the probability of observing the symptoms if the patient has the disease. Intelligent systems must think about their environment. For example, a robot needs to know about the possible outcomes of its actions, and the system of medical experts needs to know what causes what consequences. Intelligent systems began to use probabilistic methods to deal with the uncertainty of the real world. Instead of building a special system of probabilistic reasoning for each new program, we would like a common framework that would allow probabilistic reasoning in any new program without restoring everything from scratch. This justifies the relevance of the developed genetic algorithm. Bayesian networks, which first appeared in the work of Judas Pearl and his colleagues in the late 1980s, offer just such an independent basis for plausible reasoning. This article presents the genetic algorithm for learning the structure of the Bayesian network that searches the space of the graph, uses mutation and crossover operators. The algorithm can be used as a quick way to learn the structure of a Bayesian network with as few constraints as possible. learn the structure of a Bayesian network with as few constraints as possible.

Sometimes in practice it is necessary to calculate the probability of an uncertain cause, taking into account some observed evidence. For example, we would like to know the probability of a particular disease when we observe the patient’s symptoms. Such problems are often complex with many interrelated variables. There may be many symptoms and even more potential causes. In practice, it is usually possible to obtain only the inverse conditional probability, the probability of evidence giving the cause, the probability of observing the symptoms if the patient has the disease. Intelligent systems must think about their environment. For example, a robot needs to know about the possible outcomes of its actions, and the system of medical experts needs to know what causes what consequences. Intelligent systems began to use probabilistic methods to deal with the uncertainty of the real world. Instead of building a special system of probabilistic reasoning for each new program, we would like a common framework that would allow probabilistic reasoning in any new program without restoring everything from scratch. This justifies the relevance of the developed genetic algorithm. Bayesian networks, which first appeared in the work of Judas Pearl and his colleagues in the late 1980s, offer just such an independent basis for plausible reasoning. This article presents the genetic algorithm for learning the structure of the Bayesian network that searches the space of the graph, uses mutation and crossover operators. The algorithm can be used as a quick way to learn the structure of a Bayesian network with as few constraints as possible. learn the structure of a Bayesian network with as few constraints as possible.

Blockchain technology appears to be ready to revolutionise a broad number of industries. However, the blockchain itself contains a number of inefficiencies and areas for improvement, namely: transaction fees and transaction speeds. Directed acyclic graphs (DAGs) address, and improve on these inefficiencies and a number of digital currencies utilising this technology have already begun to appear. This paper provides an explanation of the technology behind DAG-based assets, while identifying and highlighting strategic advantages that DAGs possess over traditional blockchains. We conduct an EGARCH volatility analysis of a range of blockchain-based and DAGbased cryptocurrencies in the aftermath of a range of market shocks, taking the form of regulatory announcements such as bans and broad restrictions for cryptocurrencies. We find that DAG-based assets become increasingly responsive to market shocks as they mature. Such behaviour mirrors that of established cryptocurrencies such as Bitcoin, Ethereum and Litecoin, providing evidence that DAG-based cryptocurrencies now share similar characteristics to traditional blockchain-chain based products.

The binary Constraint Satisfaction Problem (CSP) is to decide whether there exists an assignment to a set of variables which satisfies specified constraints between pairs of variables. A CSP instance can be presented as a labelled graph (called the microstructure) encoding both the forms of the constraints and where they are imposed. We consider subproblems defined by restricting the allowed form of the microstructure. One form of restriction that has previously been considered is to forbid certain specified substructures (patterns). This captures some tractable classes of the CSP, but does not capture the well-known property of acyclicity. In this paper we introduce the notion of a topological minor of a binary CSP instance. By forbidding certain patterns as topological minors we obtain a compact mechanism for expressing several novel tractable classes, including new generalisations of the class of acyclic instances.

In this paper comparison of the first and second normalized Zagreb eccentricity indices is analyzed.

This paper presents a new direct acyclic graph based corrective control strategy to alleviate overloads of transmission lines, with the help of a local optimization concept. In the present study, a direct acyclic graph (DA G) technique for selection of participating generators and buses with respect to a contingency is presented. Particle swarm optimization (PSO) technique has been employed for generator rescheduling and/or load shedding problem locally, to restore the system from abnormal to normal operating state. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 14-bus and 30-bus systems. The result shows that the proposed approach is computationally fast, reliable and efficient, in restoring the system to normal state after a contingency with minimal control actions.

A new generation rescheduling approach for preventive control of power system is presented which can reallocate power generation for multiple unstable contingencies based on graph theory. The objective of preventive control is to prepare the system when it is still in normal operation, so that the system is able to face future uncertain events in a satisfactory way. The generator rescheduling is based on the outage of links from one generator area to the other without violating line overloads. The quantity of generation shifting from one generator area to other is calculated by the proportionality assumption. When rescheduling of generators fails to provide a feasible solution for secure operation, optimal load shedding/switching off disturbed area, called islanding formation, is resorted to. The suitability of the proposed method is demonstrated with the help of six bus three machine power system and IEEE 30-bus power system. � 2009 Elsevier Ltd. All rights reserved.

Η παρούσα διπλωματική έχει ως σκοπό να συνδέσει την λειτουργία υπάρχοντος ορθογράφου με συγκεκριμένη δομή αποθήκευσης και αναζήτησης λέξεων. Κριτήρια του ορθογράφου είναι να είναι ανοιχτού κώδικα και σκοπός είναι να του προστεθεί η δυνατότητα να λειτουργεί με ακυκλικούς γράφους, ντετερμινιστικούς ή μη. Τα πλεονεκτήματα της νέας δομής και λειτουργίας του ορθογράφου είναι πολλαπλά και παρουσιάζονται στη πορεία της διπλωματικής. Αν και οι δοκιμές που έγιναν, δεν αναδεικνύουν τα πλεονεκτήματα αυτά, σε θεωρητικό επίπεδο η εξέλιξη μπορεί να είναι μεγάλη, και σε ταχύτητα αλλά και σε απόδοση. Target of this project is to connect the proccess of an existing speller to a spesific structure of storing and searching words. Basic criteria of this speller are: open source, capability to work with acyclic graphs, deteministic or not. The advantages of this new structure and proccess of the speller are presented in this project.

Model selection in graphical Markov models has been a challenging problem. The number of possible directed acyclic graphs (DAGs) grows super-exponentially as the number of variables increases and many of these DAGs encode the same conditional independence relations. Searching through DAG equivalence classes reduces the search space, hence making the search more efficient. The DAG equivalence class search (DECS) algorithm extends to DAG equivalence classes a search over undirected graph for the most simple graph consistent with the data. I provide a graphical criterion for the submodel relation for graphs with four or fewer nodes. I also provide insights into the graphical criterion needed for the submodel relations for graphs over more than four nodes. I use this criterion in the DECS algorithm to move across DAG equivalence classes. Finally. I also demonstrate the DECS algorithm on both real and simulated data sets.

The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning the structure of the risk. Furthermore, the combination of a rich information encoding procedure with a generalized model of dynamical recurrent networks permit us, as we shall demonstrate, a sophisticated processing of information that we believe as being a first step for building future advanced intersection safety systems.

Labelled acyclic graphs with some restrictions on the labelling function can be used to describe concurrent processes. In this paper, we show how the restrictions on the labelling functions can be related with the assumptions made on the properties of the dependence and independence relations between actions. Furthermore, we compare two different recognizing devices for a particular class of labelled acyclic graphs, i.e. finite state automata on a free partially commutative monoid and finite state asynchronous automata, and give some results on the existence of minimal automata recognizing a given language.