One way people enhance their learning is through a desirable difficulty that makes the learning phase more difficult. The present research was devised to further explore these results and test the hypothesis that desirable difficulties benefits inductive learning by helping people engage in deeper processing strategies. In this experiment, participants were instructed to process perceptual disfluency and study different butterfly species that were presented in a clear or blurry manner. All participants were exposed to the interleaved and blocked conditions (within subjects), there was also a between subjects condition of fluent vs. disfluent. I hypothesized that subjects would perform better when presented with disfluency (blurry picture) because people would be able to engage in deeper processing strategies. This supported my hypothesis that desirable difficulties benefits inductive learning by engaging the subject in deeper processing.

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No / Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.

Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptions as associations between the concept and the preconditions to this concept and employs a relational upgrade of association rule mining search heuristic, APRIORI rule, to effectively prune the search space. The proposed system is a hybrid predictive inductive logic system, which utilizes inverse resolution for generalization of concept instances in the presence of background knowledge and refines these general patterns into frequent and strong concept definitions with a modified APRIORI-based specialization operator. Two versions of the system are tested for three real-world learning problems: learning a linearly recursive relation, predicting carcinogenicity of molecules within Predictive Toxicology Evaluation (PTE) challenge and mesh design. Results of the experiments show that the proposed hybrid method is competitive with state-of-the-art systems.

Wire Electrical Discharge Machining (WEDM) is a nontraditional machining process used for machining intricate shapes in high strength and temperature resistive (HSTR) materials. WEDM provides high accuracy, repeatability, and a better surface finish; however the tradeoff is a very slow machining rate. Due to the slow machining rate in WEDM, machining tasks take many hours depending on the complexity of the job. Because of this, users of WEDM try to predict machining rate beforehand so that input parameter values can be pre-programmed to achieve automated machining. However, partial success with traditional methodologies such as thermal modeling, artificial neural networks, mathematical, statistical, and empirical models left this problem still open for further research and exploration of alternative methods. Also, earlier efforts in applying the decision tree rule induction algorithms for predicting the machining rate in WEDM had limitations such as use of coarse grained method of discretizing the target and exploration of only C4.5 as the learning algorithm. The goal of this dissertation was to address the limitations reported in literature in using decision tree rule induction algorithms for WEDM. In this study, the three decision tree inductive algorithms C5.0, CART and CHAID have been applied for predicting material removal rate when the target was discretized into varied number of classes (two, three, four, and five classes) by three discretization methods. There were a total of 36 distinct combinations when learning algorithms, discretization methods, and number of classes in the target are combined. All of these 36 models have been developed and evaluated based on the prediction accuracy. From this research, a total of 21 models found to be suitable for WEDM that have prediction accuracy ranging from 71.43% through 100%. The models indentified in the current study not only achieved better prediction accuracy compared to previous studies, but also allows the users to have much better control over WEDM than what was previously possible. Application of inductive learning and development of suitable predictive models for WEDM by incorporating varied number of classes in the target, different learning algorithms, and different discretization methods have been the major contribution of this research.

Programmering tog 2018 ett stort kliv in matematikundervisningen och det är upp till varje enskild lärare att besluta hur programmering ska integreras i matematikundervisningen. Det var med denna bakgrund som Tekniskt basår på KTH Campus önskade utveckla programmeringslaborationer till studenter på Tekniskt basår 2018/2019. Uppdraget formulerades sedan om till att vi, istället, skulle leda ett fristående introduktionsmoment i programmering för nuvarande Tekniskt basårsstudenter (VT2019). Syftet med detta examensarbete var att kartlägga svårigheter som tidigare Tekniskt basårsstudenter haft då de läste sin första programmeringskurs på ett ingenjörsprogram. De funna svårigheterna skulle sedan ligga till grund för ett introduktionsmoment för nuvarande studenter på Tekniskt basår. En enkätundersökning på tidigare studenter utfördes för att samla in data för att identifiera svårigheterna som sedan kategoriserades genom en tematisk analys. Introduktionsmomentet skapades med mindre uppgifter utifrån idéer från problembaserat och induktivt lärande. Resultatet pekar på sju teman av svårigheter, nämligen abstrakt, komplext, teori, arbetssättet, kursen, nytt och övriga svårigheter. Resultaten av svårigheterna liknar det som tidigare kartläggningar av programmeringssvårigheter har funnit. Ett förslag på ett induktivt introduktionsmoment influerat av problembaserat lärande presenteras och analyseras utifrån de funna svårigheterna. Vidare forskning skulle kunna följa upp om studenterna upplevde att svårigheterna blev avdramatiserade genom introduktionsmomentet samt utvärdera hur introduktionsmomentet kan tas vidare på Tekniskt basår. / In 2018, programming became an integral part of mathematics education in Sweden. However, the choice of how to integrate it with the curriculum remains a decision of the teacher. Consequently, teachers at KTH’s Technical Preparatory Year announced a master’s degree project aiming to design programming labs in mathematics for students attending the program during 2018/2019. The degree project was reformulated to focus on introducing programming without the mathematical context. The aim of this master thesis was to pinpoint earlier students’ difficulties during the introductory course in programming of their engineering program at KTH. These difficulties would later be the basis of the programming introduction for the current Technical Preparatory Year students. A review of the research literature on the learning of programming identified a number of common areas of concern. In order to collect data to identify our prior students’ difficulties a survey was designed. The data collected in the survey was then categorized through a thematic analysis. The results indicated seven themes of difficulties: abstract, complex, theory, work procedure, programming courses, new and miscellaneous. The results are similar to those found in prior categorizations of programming difficulties. Taking these themes into consideration, a suggestion of the content for the introduction was presented based on ideas from inductive learning and problem based learning. Future research should focus on determining whether the introduction dealt with the difficulties and what the effects were. Furthermore, future research could develop the material for the introduction, in particular how students are assessed.

A joining implication is a restricted form of an implication where it is explicitly specified which attributesmay occur in the premise and in the conclusion, respectively. A technique for sound and complete axiomatization of joining implications valid in a given formal context is provided. In particular, a canonical base for the joining implications valid in a given formal context is proposed, which enjoys the property of being of minimal cardinality among all such bases. Background knowledge in form of a set of valid joining implications can be incorporated. Furthermore, an application to inductive learning in a Horn description logic is proposed, that is, a procedure for sound and complete axiomatization of Horn-M concept inclusions from a given interpretation is developed. A complexity analysis shows that this procedure runs in deterministic exponential time.

Programmering tog 2018 ett stort kliv in matematikundervisningen och det är upp till varjeenskild lärare att besluta hur programmering ska integreras i matematikundervisningen. Detvar med denna bakgrund som Tekniskt basår på KTH Campus önskade utvecklaprogrammeringslaborationer till studenter på Tekniskt basår 2018/2019. Uppdragetformulerades sedan om till att vi, istället, skulle leda ett fristående introduktionsmoment iprogrammering för nuvarande Tekniskt basårsstudenter (VT2019).Syftet med detta examensarbete var att kartlägga svårigheter som tidigare Teknisktbasårsstudenter haft då de läste sin första programmeringskurs på ett ingenjörsprogram. Defunna svårigheterna skulle sedan ligga till grund för ett introduktionsmoment för nuvarandestudenter på Tekniskt basår. En enkätundersökning på tidigare studenter utfördes för attsamla in data för att identifiera svårigheterna som sedan kategoriserades genom en tematiskanalys. Introduktionsmomentet skapades med mindre uppgifter utifrån idéer frånproblembaserat och induktivt lärande.Resultatet pekar på sju teman av svårigheter, nämligen abstrakt, komplext, teori,arbetssättet, kursen, nytt och övriga svårigheter. Resultaten av svårigheterna liknar det somtidigare kartläggningar av programmeringssvårigheter har funnit. Ett förslag på ett induktivtintroduktionsmoment influerat av problembaserat lärande presenteras och analyseras utifrånde funna svårigheterna.Vidare forskning skulle kunna följa upp om studenterna upplevde att svårigheterna blevavdramatiserade genom introduktionsmomentet samt utvärdera hur introduktionsmomentetkan tas vidare på Tekniskt basår. / In 2018, programming became an integral part of mathematics education in Sweden. However, the choice of how to integrate it with the curriculum remains a decision of the teacher. Consequently, teachers at KTH’s Technical Preparatory Year announced a master’s degree project aiming to design programming labs in mathematics for students attending the program during 2018/2019. The degree project was reformulated to focus on introducing programming without the mathematical context. The aim of this master thesis was to pinpoint earlier students’ difficulties during theintroductory course in programming of their engineering program at KTH. These difficulties would later be the basis of the programming introduction for the current Technical Preparatory Year students. A review of the research literature on the learning of programming identified a number of common areas of concern. In order to collect data to identify our prior students’ difficulties a survey was designed. The data collected in the survey was then categorized through a thematic analysis.The results indicated seven themes of difficulties: abstract, complex, theory, work procedure, programming courses, new and miscellaneous. The results are similar to those found in prior categorizations of programming difficulties. Taking these themes into consideration, a suggestion of the content for the introduction was presented based on ideas from inductive learning and problem based learning. Future research should focus on determining whether the introduction dealt with the difficulties and what the effects were. Furthermore, future research could develop the material for the introduction, in particular how students are assessed.

The increasing complexity of software systems has led to the development of sophisticated formal Methodologies for verifying and correcting data and programs. In general, establishing whether a program behaves correctly w.r.t. the original programmer s intention or checking the consistency and the correctness of a large set of data are not trivial tasks as witnessed by many case studies which occur in the literature. In this dissertation, we face two challenging problems of verification and correction. Specifically, verification and correction of declarative programs, and the verification and correction of Web sites (i.e. large collections of semistructured data). Firstly, we propose a general correction scheme for automatically correcting declarative, rule-based programs which exploits a combination of bottom-up as well as topdown inductive learning techniques. Our hybrid hodology is able to infer program corrections that are hard, or even impossible, to obtain with a simpler,automatic top-down or bottom-up learner. Moreover, the scheme will be also particularized to some well-known declarative programming paradigm: that is, the functional logic and the functional programming paradigm. Secondly, we formalize a framework for the automated verification of Web sites which can be used to specify integrity conditions for a given Web site, and then automatically check whether these conditions are fulfilled. We provide a rule-based, formal specification language which allows us to define syntactic as well as semantic properties of the Web site. Then, we formalize a verification technique which detects both incorrect/forbidden patterns as well as lack of information, that is, incomplete/missing Web pages. Useful information is gathered during the verification process which can be used to repair the Web site. So, after a verification phase, one can also infer semi-automatically some possible corrections in order to fix theWeb site. The methodology is based on a novel rewrit / Ballis, D. (2005). Rule-Based Software Verification and Correction [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1948