In this paper, an expert system that performs route planning using dynamic traffic data is introduced. Also an algorithmic approach is introduced to find the shortest path in a three-dimensional. Using both implementations, a comparison is made between the expert system approach and the algorithmic approach. It is concluded that the expert system shows great potential. The expert system indeed finds the best routes, and it outperforms the algorithm approach in computation time, too.
The contribution focuses on the design of a control algorithm aimed at the operative control of runoff water from a reservoir during flood situations. Management is based on the stochastically specified forecast of water inflow into the reservoir. From a mathematical perspective, the solved task presents the control of a dynamic system whose predicted hydrological input (water inflow) is characterised by significant uncertainty. The algorithm uses a combination of simulation model data, in which the position of the bottom outlets is sought via nonlinear optimisation methods, and artificial intelligence methods (adaptation and fuzzy model). The task is written in the technical computing language MATLAB using the Fuzzy Logic Toolbox.
Abstract: By action model, we understand any logic-based representation of effects and executability preconditions of individual actions within a certain domain. In the context of artificial intelligence, such models are necessary for planning and goal-oriented automated behaviour. Currently, action models are commonly hand-written by domain experts in advance. However, since this process is often difficult, time-consuming, and error-prone, it makes sense to let agents learn the effects and conditions of actions from their own observations. Even though the research in the area of action learning, as a certain kind of inductive reasoning, is relatively young, there already exist several distinctive action learning methods. We will try to identify the collection of the most important properties of these methods, or challenges that they are trying to overcome, and briefly outline their impact on practical applications., Abstrakt: Podle akčního modelu chápeme logickou reprezentaci efektů a předpokladů vykonatelnosti jednotlivých akcí v rámci určité domény. V kontextu umělé inteligence jsou tyto modely nezbytné pro plánování a cílené automatizované chování. V současné době jsou akční modely běžně ručně psány odborníky domény předem. Vzhledem k tomu, že tento proces je často obtížný, časově náročný a náchylný k chybám, má smysl nechat agenty seznámit se s účinky a podmínkami akcí z vlastních pozorování. I když je výzkum v oblasti akčního učení, jako určitý druh indukčního uvažování, relativně mladý, existuje již několik výrazných metod učení. Pokusíme se identifikovat sbírku nejdůležitějších vlastností těchto metod., and Michal Čertický
The design and evaluation of algorithms for adaptive stochastic control of the reservoir function of a water reservoir using an artificial intelligence method (learned fuzzy model) are described in this article. This procedure was tested on the Vranov reservoir (Czech Republic). Stochastic model results were compared with the results of deterministic management obtained using the method of classical optimisation (differential evolution). The models used for controlling of reservoir outflow used single quantile from flow duration curve values or combinations of quantile values from flow duration curve for determination of controlled outflow. Both methods were also tested on forecast data from real series (100% forecast). Finally, the results of the dispatcher graph, adaptive deterministic control and adaptive stochastic control were compared. Achieved results of adaptive stochastic management were better than results provided by dispatcher graph and provide inspiration for continuing research in the field.
This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0.6893 to 0.9821 by 142% for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.
In the seventy years since AI became a field of study, the theoretical work of philosophers has played increasingly important roles in understanding many aspects of the AI project, from the metaphysics of mind and what kinds of systems can or cannot implement them, the epistemology of objectivity and algorithmic bias, the ethics of automation, drones, and specific implementations of AI, as well as analyses of AI embedded in social contexts (for example). Serious scholarship in AI ethics sometimes quotes Asimov's speculative laws of robotics as if they were genuine proposals, and yet Lem remains historically undervalued as a theorist who uses fiction as his vehicle. Here, I argue that Lem's fiction (in particular his fiction about robots) is overlooked but highly nuanced philosophy of AI, and that we should recognize the lessons he tried to offer us, which focused on the human and social failures rather than technological breakdowns. Stories like "How the World Was Saved" and "Upside Down Evolution" ask serious philosophical questions about AI metaphysics and ethics, and offer insightful answers that deserve more attention. Highlighting some of this work from The Cyberiad and the stories in Mortal Engines in particular, I argue that the time has never been more appropriate to attend to his philosophy in light of the widespread technological and social failures brought about by the quest for artificial intelligence. In service of this argument, I discuss some of the history and philosophical debates around AI in the last decades, as well as contemporary events that illustrate Lem's strongest claims in critique of the human side of AI.
After a brief discussion of Creative reasoning modelling significance
for transportation reliability modelling this páper continues by a discussion of-the known applicable techniques of creativity modelling. Because tlie most significant one seems to be analogical and associative reasoning, a unified model of analogical and associative reasoning is presented. Dne to its real-time capabilities, the model enables to model reasoning under the condition of a processing capacity limitation (and concluding the increase of producing mistaking reactions).
Analyses based on precipitation data may be limited by the quality of the data, the size of the available historical series and the efficiency of the adopted methodologies; these factors are especially limiting when conducting analyses at the daily scale. Thus, methodologies are sought to overcome these barriers. The objective of this work is to develop a hybrid model through the maximum overlap discrete wavelet transform (MODWT) to estimate daily rainfall in homogeneous regions of the Tocantins-Araguaia Hydrographic Region (TAHR) in the Amazon (Brazil). Data series from the Climate Prediction Center morphing (CMORPH) satellite products and rainfall data from the National Water Agency (ANA) were divided into seasonal periods (dry and rainy), which were adopted to train the model and for model forecasting. The results show that the hybrid model had a good performance when forecasting daily rainfall using both databases, indicated by the Nash–Sutcliffe efficiency coefficients (0.81–0.95), thus, the hybrid model is considered to be potentially useful for modelling daily rainfall.
One of the problems during a great disaster is the breakdown of communication infra structure. One of the solutions is the use of mobile ad-hoc networks (MANET). In this paper, we consider the situation in a building after a big fire, explosion or earthquake. Rescue workers equipped with PDAs, which are wireless connected, explore the dynamically changing world. Each individual builds up a local world map based on their local exploration and observation. The local maps are fused via the MANET structure and provide an up to date map of the dynamically changing world. Such maps can be used for mitigation, escape or rescue work.
In the future, speech unquestionably will become the primary means of communication between humans and machines. New applications of artificial neural networks are capable of recognizing human speech and analyze the meaning of the recognized text. The condition of the effectiveness of two-way human-machine voice communication is to apply the mechanisms of command verification and correctness. In this paper there is a review of the selected issues on recognition and safety estimations of voice commands in natural language given by the operator of the technological device. A view is offered of the complexity of the recognition process of the operator‘s words and commands using neural networks made of a few layers of neurons. There is also an intelligent system of two-way voice communication between the technological device and the operator presented, which consists of the intelligent mechanism of operator identification, word and command recognition, command syntax and result analysis, command safety assessment, technological process supervision as well as operator reaction assessment. The paper presents research results of speech recognition and automatic command recognition as well as command safety estimation with artificial neural networks. and Obsahuje seznam literatury