Using an improved rule match algorithm in an expert system to detect broken driving rules for an energy-efficiency and safety relevant driving system

Vehicles have been so far improved in terms of energy-efficiency and safety mainly by optimising the engine and the power train. However, there are opportunities to increase energy-efficiency and safety by adapting the individual driving behaviour in the given driving situation. In this paper, an improved rule match algorithm is introduced, which is used in the expert system of a human-centred driving system. The goal of the driving system is to optimise the driving behaviour in terms of energy-efficiency and safety by giving recommendations to the driver. The improved rule match algorithm checks the incoming information against the driving rules to recognise any breakings of a driving rule. The needed information is obtained by monitoring the driver, the current driving situation as well as the car, using in-vehicle sensors and serial-bus systems. On the basis of the detected broken driving rules, the expert system will create individual recommendations in terms of energy-efficiency and safety, which will allow eliminating bad driving habits, while considering the driver needs

Production system shell for mobile Devices

The CLIPS shell extension was proposed to enable the creation of applied production systems which can be used on mobile devices. The innovation was substantiated in terms of saving money resources to purchase specialized equipment for using for the resource-intensive production systems. The following studies were conducted: market research tablet devices, the most widely represented in Ukraine; production systems shells that can be used on mobile devices; freely distributable production systems shells were considered, important characteristics were identified and compared. The basic classes of match algorithms were overviewed. The results of studies about Rete and Treat match algorithms advisability were described for the general case of the applied problems. The proposed modeling environment for production systems for mobile devices will reduce development time and increase system efficiency by choosing the optimal match algorithm for minimal memory usag

Вибір оптимального алгоритму співставлення зі зразком при проектуванні продукційної системи

The garment industry quickly becomes a highly developed branch due to the rapid development of technologies that contribute to high-quality design, cutting, manufacture. However, some design stages have not yet been formalized. For solving unformalized tasks, the expert systems are used. The research deals with developing the expert system prototype for rapid reorientation of women’s outerwear production. To form a subject environment, the textual method is used. Factor and cluster analyses are used to structure the subject environment. Thus, the main objective of the study is achieved by forming twelve individual tasks according to the number of individual groups, allocated in the subject environment of rapid reorientation of women’s outerwear production. Selection rules of transformation chain and values of additions at the level of chest, waist and hips are formed in tables. In each table, results are obtained at the intersection of several conditions.The expert system prototype for flexible reorientation of women’s outerwear production is designed by using the empty expert system “Rapana”. The expert system prototype implements a dialogue with the user as a series of questions and answers of the user. Some answers can have a degree of confidence. The user can revise the way of decision-making after obtaining the results. Thus, necessary conditions for further development of artificial intelligence methods in the garment production design training management and for reducing risks of wrong decision-making in conditions of rapid change in project situations are createdПредложена совокупность характеристик продукционной системы, которая зависит от формализованного представления прикладной задачи и определяют  эффективность логического вывода. На основании данных характеристик предложено схему определения оптимального по быстродействию и затратам памяти алгоритма сопоставления с образцом на этапе проектирования продукционной системы. Приведен  пример выбора алгоритма для задачи диагностирования башенной градирни.Запропонована сукупність характеристик продукційної системи, які залежать від формалізованого представлення прикладної задачі та впливають на ефективність логічного виведення. На основі даних характеристик запропоновано схему визначення оптимального за швидкодією та затратами пам’яті алгоритму співставлення зі зразком на етапі проектування продукційної системи. Приведено приклад вибору алгоритму для задачі діагностування баштової градирні

Вибір оптимального алгоритму співставлення зі зразком при проектуванні продукційної системи

The garment industry quickly becomes a highly developed branch due to the rapid development of technologies that contribute to high-quality design, cutting, manufacture. However, some design stages have not yet been formalized. For solving unformalized tasks, the expert systems are used. The research deals with developing the expert system prototype for rapid reorientation of women’s outerwear production. To form a subject environment, the textual method is used. Factor and cluster analyses are used to structure the subject environment. Thus, the main objective of the study is achieved by forming twelve individual tasks according to the number of individual groups, allocated in the subject environment of rapid reorientation of women’s outerwear production. Selection rules of transformation chain and values of additions at the level of chest, waist and hips are formed in tables. In each table, results are obtained at the intersection of several conditions.The expert system prototype for flexible reorientation of women’s outerwear production is designed by using the empty expert system “Rapana”. The expert system prototype implements a dialogue with the user as a series of questions and answers of the user. Some answers can have a degree of confidence. The user can revise the way of decision-making after obtaining the results. Thus, necessary conditions for further development of artificial intelligence methods in the garment production design training management and for reducing risks of wrong decision-making in conditions of rapid change in project situations are createdПредложена совокупность характеристик продукционной системы, которая зависит от формализованного представления прикладной задачи и определяют  эффективность логического вывода. На основании данных характеристик предложено схему определения оптимального по быстродействию и затратам памяти алгоритма сопоставления с образцом на этапе проектирования продукционной системы. Приведен  пример выбора алгоритма для задачи диагностирования башенной градирни.Запропонована сукупність характеристик продукційної системи, які залежать від формалізованого представлення прикладної задачі та впливають на ефективність логічного виведення. На основі даних характеристик запропоновано схему визначення оптимального за швидкодією та затратами пам’яті алгоритму співставлення зі зразком на етапі проектування продукційної системи. Приведено приклад вибору алгоритму для задачі діагностування баштової градирні

Detecting the adherence of driving rules in an energy-efficient, safe and adaptive driving system

An adaptive and rule-based driving system is being developed that tries to improve the driving behavior in terms of the energy-efficiency and safety by giving recommendations. Therefore, the driving system has to monitor the adherence of driving rules by matching the rules to the driving behavior. However, existing rule matching algorithms are not sufficient, as the data within a driving system is changing frequently. In this paper a rule matching algorithm is introduced that is able to handle frequently changing data within the context of the driving system. 15 journeys were used to evaluate the performance of the rule matching algorithms. The results showed that the introduced algorithm outperforms existing algorithms in the context of the driving system. Thus, the introduced algorithm is suited for matching frequently changing data against rules with a higher performance, why it will be used in the driving system for the detection of broken energy-efficiency or safety-relevant driving rules

Production system shell for mobile Devices

The CLIPS shell extension was proposed to enable the creation of applied production systems which can be used on mobile devices. The innovation was substantiated in terms of saving money resources to purchase specialized equipment for using for the resource-intensive production systems. The following studies were conducted: market research tablet devices, the most widely represented in Ukraine; production systems shells that can be used on mobile devices; freely distributable production systems shells were considered, important characteristics were identified and compared. The basic classes of match algorithms were overviewed. The results of studies about Rete and Treat match algorithms advisability were described for the general case of the applied problems. The proposed modeling environment for production systems for mobile devices will reduce development time and increase system efficiency by choosing the optimal match algorithm for minimal memory usage.В роботі запропоновано розширення програмної оболонки CLIPS для забезпечення можливості створення прикладних продукційних систем, використовуваних на мобільних пристроях. Обґрунтована інновація с точки зору економії грошових ресурсів на придбання спеціалізованих пристроїв для використання ресурсоємних продукційних систем. Проведено наступні дослідження: ринку планшетних пристроїв, найширше представлених в Україні; оболонок продукційних систем, які можуть застосовуватися на мобільних пристроях; більш детально, з виділенням значимих характеристик, розглянуто вільно поширювані обгортки продукційних систем. Описано основні класи алгоритмів співставлення зі зразком. Представлені результати досліджень щодо доцільності застосування Rete та Treat для загальних випадків в прикладних задач

Instance-based natural language generation

In recent years, ranking approaches to Natural Language Generation have become increasingly popular. They abandon the idea of generation as a deterministic decision¬ making process in favour of approaches that combine overgeneration with ranking at some stage in processing.In this thesis, we investigate the use of instance-based ranking methods for surface realization in Natural Language Generation. Our approach to instance-based Natural Language Generation employs two basic components: a rule system that generates a number of realization candidates from a meaning representation and an instance-based ranker that scores the candidates according to their similarity to examples taken from a training corpus. The instance-based ranker uses information retrieval methods to rank output candidates.Our approach is corpus-based in that it uses a treebank (a subset of the Penn Treebank II containing management succession texts) in combination with manual semantic markup to automatically produce a generation grammar. Furthermore, the corpus is also used by the instance-based ranker. The semantic annotation of a test portion of the compiled subcorpus serves as input to the generator.In this thesis, we develop an efficient search technique for identifying the optimal candidate based on the A*-algorithm, detail the annotation scheme and grammar con¬ struction algorithm and show how a Rete-based production system can be used for efficient candidate generation. Furthermore, we examine the output of the generator and discuss issues like input coverage (completeness), fluency and faithfulness that are relevant to surface generation in general

Developing and Measuring Parallel Rule-Based Systems in a Functional Programming Environment

This thesis investigates the suitability of using functional programming for building parallel rule-based systems. A functional version of the well known rule-based system OPS5 was implemented, and there is a discussion on the suitability of functional languages for both building compilers and manipulating state. Functional languages can be used to build compilers that reflect the structure of the original grammar of a language and are, therefore, very suitable. Particular attention is paid to the state requirements and the state manipulation structures of applications such as a rule-based system because, traditionally, functional languages have been considered unable to manipulate state. From the implementation work, issues have arisen that are important for functional programming as a whole. They are in the areas of algorithms and data structures and development environments. There is a more general discussion of state and state manipulation in functional programs and how theoretical work, such as monads, can be used. Techniques for how descriptions of graph algorithms may be interpreted more abstractly to build functional graph algorithms are presented. Beyond the scope of programming, there are issues relating both to the functional language interaction with the operating system and to tools, such as debugging and measurement tools, which help programmers write efficient programs. In both of these areas functional systems are lacking. To address the complete lack of measurement tools for functional languages, a profiling technique was designed which can accurately measure the number of calls to a function , the time spent in a function, and the amount of heap space used by a function. From this design, a profiler was developed for higher-order, lazy, functional languages which allows the programmer to measure and verify the behaviour of a program. This profiling technique is designed primarily for application programmers rather than functional language implementors, and the results presented by the profiler directly reflect the lexical scope of the original program rather than some run-time representation. Finally, there is a discussion of generally available techniques for parallelizing functional programs in order that they may execute on a parallel machine. The techniques which are easier for the parallel systems builder to implement are shown to be least suitable for large functional applications. Those techniques that best suit functional programmers are not yet generally available and usable

An analysis of the efficiency of ontology and symbolic learning algorithms in indigenous knowledge representation

It is without a doubt that machine learning has been the area of focus in early days of artificial intelligence, but the early neural networks approach suffered some shortcomings and this led to a temporary decline in research capacity. New symbolic learning techniques have emerged since then which have yielded promising results and have led to a revival in research in machine learning. This has seen many researchers focusing on these techniques and experimenting with them by comparing their performances for different applications. With that in mind, the research thus decided to make an analysis of the symbolic approach against other approaches such as the neural network (connectionist) to evaluate the power of the former approach. This was done by first generating an ontology that acted as a representation of some collected indigenous knowledge. It is from this ontology that a dataset was generated. The dataset was made ambiguous to see the learning power of classifiers in such data. Two experiments were done, one using WEKA and the other using Orange as tools. The reason why the two experiments were used is because there was not a single tool which contained all the required learning algorithms. The research wanted to make use of ID3 and CN2 symbolic algorithm. However, WEKA had ID3 and not CN2 while Orange had CN2 and not ID3. The most important attributes from the ontology regarding the indigenous knowledge were the name of the plant, the province it is found and the disease the plant treats. Therefore the dataset had two features which were disease and province and one label which was the name of the plant. The learning algorithm was to use the two features to generate rules used to predict the label. However, there was ambiguity on the dataset. The challenge was that two different labels would contain the same features, thus leading to wrongful classification. This was the core of the research. Even though the learning model concluded this situation as wrongful classification, in real time, a system using the same learning model would provide desired and correct results. The only flow which was there is that the learning model simply used one label to predict under and ignore the other label with similar features. This was identified as a flow for both symbolic and non-symbolic algorithms. There is no way of giving suggestions in the case a user wants a different plant but with similar features. Therefore for classification using an ambiguous dataset, both these approaches proved to have the fore mentions flow. The research then decided to use recall to analyze the power of these approaches. It was discovered that ID3 has better recall than Multilayer perceptron and Naïve Bayes algorithms when using a training set. ID3 managed to recall clearly and effectively three of its classes by a probability of 1 while Bayes Net had only one class with recall probability of 1. To further investigate the issue of recall, cross validation was used to contrast the competence of recall of the three algorithms to strengthen the assertion that indeed ID3 has a better recall as compared to the other two algorithms. Three stages of cross-validation were done, one stage using 10 fold, the other 20 fold, and the last using 50 fold. For all the different stages of crossvalidation, Bayes Net proved to perform better in terms of recall than the other two algorithms. In cross-validation, MLP could recall approximately above 88% of the instances available in contrast to when using training set where the algorithm recall only two out of 18 instances. In overall the symbolic approach proved to be a commendable approach for use over the nonsymbolic approach. The study of machine learning involves the building of learning algorithms, improving upon learning algorithms or making comparisons of machine learning algorithms. The research raised awareness on some improvements that need to be done on not only symbolic algorithms but non-symbolic ones as well. Some improvements include improving on or coming up with algorithms that suggest alternative predictions in cases of ambiguity instead of doing wrongful classification and not reflect on other possibilities

An adaptive and rule based driving system for energy-e cient and safe driving behaviour

Falta palabras claveSaving energy and protecting the environment became fundamental for society and politics, why several laws were enacted to increase the energye ciency. Furthermore, the growing number of vehicles and drivers leaded to more accidents and fatalities on the roads, why road safety became an important factor as well. Due to the increasing importance of energye ciency and safety, car manufacturers started to optimise the vehicle in terms of energy-e ciency and safety. However, energy-e ciency and road safety can be also increased by adapting the driving behaviour to the given driving situation. This thesis presents a concept of an adaptive and rule based driving system that tries to educate the driver in energy-e cient and safe driving by showing recommendations on time. Unlike existing driving systems, the presented driving system considers energy-e ciency and safety relevant driving rules, the individual driving behaviour and the driver condition. This allows to avoid the distraction of the driver and to increase the acceptance of the driving system, while improving the driving behaviour in terms of energy-e ciency and safety. A prototype of the driving system was developed and evaluated. The evaluation was done on a driving simulator using 42 test drivers, who tested the e_ect of the driving system on the driving behaviour and the e_ect of the adaptiveness of the driving system on the user acceptance. It has been proven during the evaluation that the energy-e ciency and safety can be increased, when the driving system was used. Furthermore, it has been proven that the user acceptance of the driving system increases when the adaptive feature was turned on. A high user acceptance of the driving system allows a steady usage of the driving system and, thus, a steady improvement of the driving behaviour in terms of energy-e ciency and safety