Requirements acquisition is one of the most important and least well supported parts of the software development process. The Requirements Apprentice (RA) will assist a human analyst in the creation and modification of software requirements. Unlike current requirements analysis tools, which assume a formal description language, the focus of the RA is on the boundary between informal and formal specifications. The RA is intended to support the earliest phases of creating a requirement, in which incompleteness, ambiguity, and contradiction are inevitable features. From an artificial intelligence perspective, the central problem the RA faces is one of knowledge acquisition. It has to develop a coherent internal representation from an initial set of disorganized statements. To do so, the RA will rely on a variety of techniques, including dependency-directed reasoning, hybrid knowledge representation, and the reuse of common forms (clich鳩. The Requirements Apprentice is being developed in the context of the Programmer's Apprentice project, whose overall goal is the creation of an intelligent assistant for all aspects of software development.

The Listener is an automated system that unintrusively performs knowledge acquisition from informal input. The Listener develops a coherent internal representation of a description from an initial set of disorganized, imprecise, incomplete, ambiguous, and possibly inconsistent statements. The Listener can produce a summary document from its internal representation to facilitate communication, review, and validation. A special purpose Listener, called the Requirements Apprentice (RA), has been implemented in the software requirements acquisition domain. Unlike most other requirements analysis tools, which start from a formal description language, the focus of the RA is on the transition between informal and formal specifications.

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical.

Computer-based training systems have become a mainstay in military and private institutions for training people how to perform certain complex tasks. As these tasks expand in difficulty, intelligent agents will appear as virtual teammates or tutors assisting a trainee in performing and learning the task. For developing these agents, we must obtain the strategies from expert players and emulate their behavior within the agent. Past researchers have shown the challenges in acquiring this information from expert human players and translating it into the agent. A solution for this problem involves using computer systems that assist in the human expert knowledge elicitation process. In this thesis, we present an approach for developing an agent for the game Revised Space Fortress, a game representative of the complex tasks found in training systems. Using machine learning techniques, the agent learns the strategy for the game by observing how a human expert plays. We highlight the challenges encountered while designing and training the agent in this real-time game environment, and our solutions toward handling these problems. Afterward, we discuss our experiment that examines whether trainees experience a difference in performance when training with a human or virtual partner, and how expert agents that express distinctive behaviors affect the learning of a human trainee. We show from our results that a partner agent that learns its strategy from an expert player serves the same benefit as a training partner compared to a programmed expert-level agent and a human partner of equal intelligence to the trainee.

Rule induction is a popular technique for knowledge acquisition and data mining. Many techniques, such as ID3, C4.5, CART (tree induction tecniques) and Artificial Neural Networks have been developed and widely used. However, most techniques are either based on categorical or numerical mechanisms to assess the importance of different input variables, which may not produce the optimal rule when a mixture of variables exists. In 1992, Liang proposed a composite approach called CRIS that use different method to analyze different types of data in inducing rules for binary classification. Yang conducted a follow-up research to extend the original algorithm to multiple categories. However, both methods do not take variable interaction into consideration. The purpose of this research is to extend previous approach and extend by including second-order interaction. We also take into consideration the kurtosis and skewness of data for numerical variables. For categorical data, we also adopt ID3 algorithm to handle classes with low representation in the sample. In order to evaluate this technique, we develop a prototype CRIS 3.0 and compare with existing techniques, including multi-category-CRIS, CART and C4.5 as benchmark. The results show that CRIS 3.0 has the highest probability of producing the highest prediction accuracy.

Background Exploring the literature stream of the knowledge perspective as well as that of start-ups andincubation, and subsequently bringing the two together. Aim To construct propositions regarding the process of developing managerial capability in incubatedstart-ups. Methodology The study entails 3 start-ups that provide a high technology product. The development ofmanagerial capability was explored through a comparative case study in which founders, businesscoaches and externally recruited employees where interviewed. Findings The process of managerial capability development in incubated start-ups can be deconstructedinto two processes, knowledge acquisition and knowledge integration, where each process presentdistinct attributes in different stages of development of the start-up. This managerial capabilityformation is an incremental process that drives growth.

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In this thesis the Semantic Web and its core technology---Web Ontology Language (OWL)---were studied. Considering the features of the different units involved in the dairy industry, OWL, in its capacity as an ontology description language, can be used to encode and thus exchange ontology among the units in the dairy industry. After creation of OWL file using Protege, an OWL parser was programmed to decode the ontology and data contained in the OWL file. Based on these investigations, it was determined that OWL can be used to encode, exchange, and decode data between farms and the units that interact with them, although large volumes of data among the service agencies pose certain challenges in terms of transfer size. A structure of the Semantic Web services in the dairy industry is proposed for Semantic Web Service registration, search and usage related to certain farm-management tasks. With the help of the Semantic Web and OWL, one can expect a more efficient data processing in the future dairy industry.

The high dropout rate of first year students is a major source of concern for the Department of Higher Education and Training and for Higher Education Institutions (HEI’s). Research indicated that students’ Self-Regulated Learning (SRL) skills and strategies play a significant role in achieving academic success at universities. Thus, the main aim of this study was to develop strategies for the development of SRL skills of first year university students. In order to achieve the research aim and objectives an extensive literature review was conducted on SRL and the relationship between SRL skills and the academic achievement of students at HEI’s. For the purposes of the empirical investigation, a mixed-method approach was followed. In the quantitative part of the investigation, the results of the Learning and Study Strategies Inventory (LASSI), which was administered to the 2007 cohort of first year students (n=2421) at the Potchefstroom Campus of the North-West University, were analysed to determine whether the subscales in the LASSI significantly predicted academic success and to identify variables that related to the first year students’ learning and study skills and academic achievement. In the qualitative part of the research, interviews were conducted during 2010, with a selected group of participants from the 2007 cohort of first year students who were then in their fourth year of study. The questions in the interviews were based on questions in the Self-Regulated Learning Inventory Schedule (SRLIS), and the aims were to explore the participants’ experiences with their studies and to determine which SRL skills, in addition to the skills assessed by the LASSI, influenced their studies and academic achievement. The quantitative analysis of the LASSI results revealed that: • Motivation, Time management and Information processing were the best LASSI predictors of the first year students’ academic success. • The independent biographical variables Grade 12 marks, age and gender correlated better with the first year students’ academic achievement than the LASSI subscales did. The qualitative investigation revealed that: • Successful students realised at the onset of their studies that they had to adapt their study methods to meet the challenges that studying at a university requires. • Successful students could differentiate between the different types of study material and could adapt their study methods accordingly. They could also adapt their study methods when the volume of the study material differed. • Successful students applied a repertoire of study methods in a flexible manner, and managed their time well. • Successful students conveyed knowledge of themselves as students, as well as of the different requirements that study at a university implicates. • Most of the successful students received information from parents, lecturers or principals about different study methods and could describe their learning styles and preferences clearly. • Some of the successful students could accurately infer which questions could be expected in the exam papers, and knew how and why these questions were asked. • Successful students set realistic academic goals for themselves. • Unsuccessful students did not consider their own study preferences or the academic requirements of the university. • Unsuccessful students did not manage their time well and were not motivated. On the basis of the findings, strategies were proposed for the development of SRL skills of first year students at universities. The strategies are presented as a compulsory programme that first year students have to complete in the first semester. / Thesis (PhD (Teaching and Learning))--North-West University, Potchefstroom Campus, 2012