Artificial Neural Networks excel at identifying individual components in an image.However, out-of-the-box, they do not manage to correctly integrate and interpret these components as a whole.One way to alleviate this weakness is to expand the network with explicit knowledge and a separate reasoning component.In this paper, we evaluate an approach to this end, applied to the solving of the popular board game Rummikub.We demonstrate that, for this particular example, the added background knowledge is equally valuable as two-thirds of the data set, and allows to bring down the training time to half the original time.* This research received funding from the Flemish

Ishikawa diagrams, also known as fishbone or cause-and-effect diagrams, are a widely known visual tool for performing root cause analysis (RCA). Although Ishikawa diagrams originated in the manufacturing sector, the tool is also actively used in other areas such as healthcare or business due to its simple structure, which requires little or no training beforehand. Though Ishikawa diagrams are valuable sources of knowledge, they lack rich semantics to effectively process them. As a result, knowledge engineers tend to ignore Ishikawa diagrams and choose other means to collect knowledge, although domain experts are familiar with the RCA tool and it is highly accepted. This paper presents the Ishikawa diagram ontology which enables the explicit modeling of Ishikawa diagrams as visual artifacts, their encoded knowledge and the process of their creation by reusing and extending existing ontologies. The ontology was developed using the LOT methodology. We have created a dataset of Ishikawa diagrams and describe a fictional use case to illustrate the intended use of the presented ontology.

Abstract As the popularity of adhesive joints in industry increases, so does the need for tools to support the process of selecting a suitable adhesive. While some such tools already exist, they are either too limited in scope or offer too little flexibility in use. This work presents a more advanced tool, that was developed together with a team of adhesive experts. We first extract the experts’ knowledge about this domain and formalize it in a Knowledge Base (KB). The IDP-Z3 reasoning system can then be used to derive the necessary functionality from this KB. Together with a user-friendly interactive interface, this creates an easy-to-use tool capable of assisting the adhesive experts. To validate our approach, we performed user testing in the form of qualitative interviews. The experts are very positive about the tool, stating that, among others, it will help save time and find more suitable adhesives.

First-order logic (FO) is an important foundation of many domains, including computer science and artificial intelligence. In recent efforts to teach basic CS and AI concepts to children, FO has so far remained absent. In this paper, we examine whether it is possible to design a learning environment that both motivates and enables children to learn the basics of FO. The key components of the learning environment are a syntax-free blocks-based notation for FO, graphics-based puzzles to solve, and a tactile environment which uses computer vision to allow the children to work with wooden blocks. The resulting FOLL-E system is intended to sharpen childrens' reasoning skills, encourage critical thinking and make them aware of the ambiguities of natural language. During preliminary testing with children, they reported that they found the notation intuitive and inviting, and that they enjoyed interacting with the application.

Abstract Knowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.