Estimating the quality of arguments in argument-based machine learning

Argument-based machine learning (ABML) knowledge refinement loop enables an interaction between a machine learning algorithm and a domain expert. It represents a powerful knowledge elicitation tool, suitable for obtaining expert knowledge in difficult domains. The loop enables the expert to focus on the most critical parts of the current knowledge base, and helps him or her to argue about automatically chosen relevant examples. The expert only needs to explain a single example at the time, which facilitates articulating arguments. It also helps the expert to improve the explanations by providing (automatically chosen) relevant counter examples. It has been shown recently that ABML knowledge refinement loop also enables design of argumentation-based interactive teaching tool. However, so far the machine was not able to provide neither the teachers (that designed such a tool) nor the students (that used it for learning) with concrete estimations about the quality of their arguments. In this thesis, we have designed three approaches for giving immediate feedback about the quality of arguments used in the ABML knowledge refinement loop. The chosen experimental domain was financial statement analysis, more concretely estimating credit scores of companies (enterprises). Our goal was twofold: to obtain a successful classification model for predicting the credit scores, and to enable the students to learn about this rather difficult domain. In the experimental sessions, both the teacher and the students were involved in the process of knowledge elicitation with the ABML knowledge refinement loop, receiving the feedback about their arguments. The goal of the learning session with the teacher was in particular to obtain advanced concepts (attributes) that describe the domain well, are suitable for teaching, and also enable successful predictions. This was done with the help of a financial expert. In the “tutoring" sessions, the students learned about the intricacies of the domain and strived for the best predictive model as possible, also by using the teacher's advanced concepts in their arguments. The main contributions of this work are: - the design of three approaches for estimating the quality of arguments used in the argument-based machine learning (ABML) knowledge refinement loop, - implementation of argumentation-based interactive teaching tool for estimating credit scores of companies (enterprises), using real data, - a detailed description of the learning session, where the student received three types of feedback about the arguments used

Light-Weight Structural Optimization Through Biomimicry, Machine Learning, and Inverse Design

In load-bearing lightweight architectures, cellular materials were frequently utilized. While octahedron, tetrahedron, and octet truss lattice truss were built for lightweight architectures with stretching and flexural dominance, it can be believed that new cells could easily be designed that might perform much better than the present ones in terms of mechanical and architectural characteristics. Machine learning-based structure scouting and design improvisation for better mechanical performance is a growing field of study. Additionally, biomimicry—the science of imitating nature’s elements—offers people a wealth of resources from which to draw motivation as they work to create a better quality of life. Here, utilizing machine learning approaches, novel lattice truss unit cellular architectures with enhanced architectural characteristics were designed. An inverse design methodology employing generative adversarial networks is suggested to investigate and improvise the lightweight lattice truss unit cellular architectures. The proposed framework was utilized to identify various lattice truss unit cellular architectures with load carrying capacities 40–120% greater than those of octet unit cells. A further 130–160% raise in buckling load bearing capacity was made possible by substituting porous biomimicry columns for the solid trusses in the light-weight lattice truss unit cellular architectures. This dissertation\u27s main goal is to investigate various improvisation strategies for creating lightweight architectures, particularly when using data analysis and machine learning methods. Lightweight cellular architectures with thin-walls and lattice truss unit cellular architectures with improved shape memory capabilities were created using the knowledge gleaned from numerous of the research projects mentioned in the preceding paragraphs load-bearing architectures and devices, lightweight architecture with shape memory and with better strength, better stretchability, and better elastic stress recovery are widely desired. As compared to the bulk shape memory polymeric cylinders, the cellular architectures with thin walls show 200% betterer elastic stress recovery that is normalized with respect to base designs. The architectural improvisation of many other additional designs and practical implementation can be accomplished using the inverse design framework

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Multimedia and Knowledge-based Computer-aided Architectural Design

It appears by now fairly accepted to many researchers in the field of the Computer Aided Architectural Design that the way to realize support tools for these aims is by means of the realization of Knowledge Based Assistants. This kind of computer programs, based on the knowledge engineering, finds their power and efficaciousness by their knowledge bases. Nowdays this kind of tools is leaving the research world and it appears evident that the common graphic interfaces and the modalities of dialogue between the architect and the computer, are inadequate to support the exchange of information that the use of these tools requires. The use of the knowledge bases furthermore, presupposes that the conceptual model of the building realized by others, must be made entirely understandable to the architect . The CAAD Laboratory has carried out a system software prototype based on Knowledge Engineering in the field of hospital buildings. In order to overcame the limit of software systems based on usual Knowledge Engineering, by improving architect-computer interaction, at CAAD Lab it is refining building model introducing into the knowledge base two complementary each other methodologies: the conceptual clustering and multimedia technics. This research will make it possible for architects navigate consciously through the domain of the knowledge base already implemented