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.

We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition. Contrary to existing datasets, it focuses on complex scenes depicting multiple people in various naturalistic, social settings, with images being annotated as a whole, thereby going beyond the traditional focus on faces or single individuals. Annotated dimensions include Valence, Arousal and Emotion label, with annotations gathered using Prolific. Together with the annotations, we release the list of URLs pointing to the original images, as well as all associated source code.

Abstract Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors, and countries. Moreover, the rules are often complex and change regularly. Therefore, payroll management systems must be flexible in design. In this paper, we suggest an approach based on a flexible answer set programming (ASP) model and an easy-to-read tabular representation based on the decision model and notation standard. It allows HR consultants to represent complex rules without the need for a software engineer and to ultimately design payroll systems for a variety of different scenarios. We show how the multi-shot solving capabilities of the clingo ASP system can be used to reach the performance that is necessary to handle real-world instances.

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.

Recent research in human-agent interaction focuses on humanagent collaboration where teams of humans and intelligent agents are formed to achieve a shared goal.The young term Hybrid Intelligence [3] addresses human-agent teams that achieve a superior goal leveraging human creative power and artificial computation power.An important aspect for the success of human-agent teams is co-learning [4, 5] which describes the goal to gain experience while performing activities as a team.Well-established systems that share knowledge with human users are expert systems which make use of extracted expert knowledge to solve real world problems and are a subclass of knowledgebased systems which store explicit domain knowledge [6, p. 18].The conventional approach to build expert systems is described by the knowledge engineering cycle [6, p. 5] where a knowledge engineer applies different knowledge elicitation techniques with domain experts to formalize strategies and domain rules the expert system must adhere to.A comprehensive overview of elicitation techniques was published in 1994 [2].The addressed technique families observations, interviews, and task analysis are still relevant today

Abstract Many practical problems can be understood as the search for a state of affairs that extends a fixed partial state of affairs, the environment, while satisfying certain conditions that are formally specified. Such problems are found in, for example, engineering, law or economics. We study this class of problems in a context where some of the relevant information about the environment is not known by the user at the start of the search. During the search, the user may consider tentative solutions that make implicit hypotheses about these unknowns. To ensure that the solution is appropriate, these hypotheses must be verified by observing the environment. Furthermore, we assume that, in addition to knowledge of what constitutes a solution, knowledge of general laws of the environment is also present. We formally define partial solutions with enough verified facts to guarantee the existence of complete and appropriate solutions. Additionally, we propose an interactive system to assist the user in their search by determining (1) which hypotheses implicit in a tentative solution must be verified in the environment, and (2) which observations can bring useful information for the search. We present an efficient method to over-approximate the set of relevant information, and evaluate our implementation.

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.

We developed a self-assessment tool for computing students in higher education to measure their Creative Problem Solving skills. Our survey encompasses 7 dimensions of creativity, based on existing validated scales and conducted focus groups. These are: technical knowledge, communication, constraints, critical thinking, curiosity, creative state of mind, and creative techniques. Principal axis factor analysis groups the dimensions into three overarching constructs: ability, mindset, and interaction. The results of a pilot study (n = 269) provide evidence for its psychometric qualities, making it a useful instrument for educational researchers to investigate students' creative skills.

Previous studies investigating and identifying non-technical skills of computing show that creative skills play an important role in tackling difficult programming problems. In the field of cognitive psychology, creativity has been extensively researched, and many of these methods can be adapted for application in computing education. We conducted a systematic literature review to support research on creativity in computing education by summarizing relevant theories, instruments, and other prior work. The review encompasses all SIGCSE venues and major journals in the field, providing a perspective of the current landscape on teaching and assessing creativity, in the context of computing in higher education. We identify which papers explore creativity as a central theoretical basis, revealing eight major themes. We also found seven commonly used measurement instruments. Creativity theories that are notably absent are also discussed, as are pedagogical implications of the approaches to fostering creativity in computing education. This paper serves to support the community by highlighting the interdisciplinary aspects of creativity research applicable in computing contexts. Additionally, it provides practical guidance and implications for educators in leveraging creativity in the classroom.

As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of software engineering: devoting oneself to continuous learning , being creative by approaching a problem from different angles , and thinking in a solution-oriented way by favoring outcome over ego . Eight follow-up interviews were conducted to further explore how the industry perceives these skill gaps, yielding 26 sub-themes grouped into six bigger themes: stimulating continuous learning , stimulating creativity , creative techniques , addressing the gap in education , skill requirements in industry , and the industry selection process . With this work, we hope to inspire educators to give the necessary attention to the uncovered skills, further mitigating the gap between the industry and the academic world.

This paper presents an application that we developed to assist users with the creation of an investment profile for the selection of financial assets. It consists of a natural language interface, an automatic translation to a declarative FO(.) knowledge base, and the IDP reasoning engine with multiple forms of logical inference. The application speeds up the investment profile creation process, and reduces the considerable inherent operational risk linked to the creation of investment profiles

Abstract Technological progress in Answer Set Programming (ASP) has been stimulated by the use of common standards, such as the ASP-Core-2 language. While ASP has its roots in nonmonotonic reasoning, efforts have also been made to reconcile ASP with classical first-order (FO) logic. This has resulted in the development of FO(·), an expressive extension of FO, which allows ASP-like problem solving in a purely classical setting. This language may be more accessible to domain experts already familiar with FO and may be easier to combine with other formalisms that are based on classical logic. It is supported by the IDP inference system, which has successfully competed in a number of ASP competitions. Here, however, technological progress has been hampered by the limited number of systems that are available for FO(·). In this paper, we aim to address this gap by means of a translation tool that transforms an FO(·) specification into ASP-Core-2, thereby allowing ASP-Core-2 solvers to be used as solvers for FO(·) as well. We present experimental results to show that the resulting combination of our translation with an off-the-shelf ASP solver is competitive with the IDP system as a way of solving problems formulated in FO(·).

Industry expectations of graduates are higher than ever. Not only are they required to be skilled in several technologies, but also need to be equipped with non-technical skills - often called soft skills or professional skills. This puts pressure on computing programs, as educators try to integrate these requirements into already full curricula. Although incorporating such skills into programs is seemingly common practice, little is known about what skills are being taught and why, outside of isolated case studies. In this work we ask: What non-technical skills are expected of undergraduate students according to computing programs? To answer this we manually curated 278 non-technical syllabi from 110 universities in 30 European countries. The most frequently identified skills are teamwork, ethics, written/oral communication, and presentation skills, while the development of one's own values, motivating others, creativity, and empathy feature least frequently. By providing a detailed analysis and an interactive website visualizing this data, we hope to aid the community in reviewing which non-technical skills are taught with an aim to teaching the right skills to the right students. This work sheds new light on what is expected of undergraduate computing students in terms of non-technical skills and identifies areas where more coverage might be needed.

In the past academic year, we conducted an experiment at using service-learning in order to integrate learning of empathy and creativity into an undergraduate course on Web Technology. This was a small scale pilot project, conducted in collaboration with the service-learning team at our institute. In the project, students collaborated with WAI-NOT, a non-profit organization that develops an online platform for children with various kinds of (physical/mental) disabilities. The students developed new "games" for this platform, to teach the children basic computer skills (e.g., clicking, moving the mouse). Key in this project was the interaction between the students, the non-profit and the target audience. Due to the small size of the class, we did not conduct a quantitative evaluation of the project, but we do discuss the experiences and feedback from teachers, students and community.

Important building blocks of software engineering concepts are without a doubt technical. During the last decade, research and practical interest for non-technicalities has grown, revealing the building blocks to be various skills and abilities beside pure technical knowledge. Multiple attempts to categorise these blocks have been made, but so far little international studies have been performed that identify skills by asking experts from both the industrial and academic world: which abilities are needed for a developer to excel in the software engineering industry? To answer this question, we performed a Delphi study, inviting 36 experts from 11 different countries world-wide, affiliated with 21 internationally renowned institutions. This study presents the 55 identified and ranked skills as classified in four major areas: communicative skills (empathy, actively listening, etc.), collaborative skills (sharing responsibility, learning from each other, etc.), problem solving skills (verifying assumptions, solution-oriented thinking, etc.), and personal skills (curiosity, being open to ideas, etc.), of which a comparison has been made between opinions of technical experts, business experts, and academics. We hope this work inspires educators and practitioners to adjust their training programs, mitigating the gap between the industry and the academic world.

The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.

In this paper we present a framework that is able to reliably and completely autonomously detect abnormal behavior in surveillance images. As input, we rely solely on a long-wave infrared (LWIR) image sensor. Our abnormal behavior detection pipeline consists of two consecutive stages. In a first stage, we perform efficient and fast pedestrian detection and tracking. In a second step, the detected paths are fed into a semi-supervised classifier that detects abnormal behavior. As test-case we recorded a unique real-life LWIR train station dataset — which will be made publicly available — containing natural occurrences of both normal and abnormal behavior. Our experiments indicate that our proposed framework achieves excellent accuracy results at real-time processing speeds.

In many knowledge representation formalisms, a constructive semantics is defined based on sequential applications of rules or of a semantic operator. These constructions often share the property that rule applications must be delayed until it is safe to do so: until it is known that the condition that triggers the rule will remain to hold. This intuition occurs for instance in the well-founded semantics of logic programs and in autoepistemic logic. In this paper, we formally define the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. For the latter, we show that safe inductions manage to capture the intended meaning of a class of theories on which all classical constructive semantics fail.

Hiring a professional camera crew to cover an event such as a lecture, sports game or musical performance may be prohibitively expensive. The CAMETRON project aims at drastically reducing this cost by developing an (almost) fully automated system that can produce video recordings of such events with a quality similar to that of a professional crew. This system consists of different components, including intelligent Pan-Tilt-Zoom cameras and UAVs that act as “virtual camera men”. To combine the footage of these different cameras into a single coherent and pleasant-to-watch video, a “virtual editor” is needed. This paper describes the development of such a component. We adopt a declarative approach, in which we build a model of the decision process that a human editor might follow to edit a video. To achieve a montage that obeys various cinematographic rules while at the same time retaining a natural, non-mechanical feel, we construct this model in a Probabilistic Logic Programming language. We demonstrate that the resulting system can be run in real-time and that it delivers montages that are almost indistinguishable from those made by a professional editor.

In the past, compelling arguments in favour of the well-founded semantics for autoepistemic logic have been presented. In this article, we show that for certain classes of theories, this semantics fails to identify the unique intended model. We solve this problem by refining the well-founded semantics. We develop our work in approximation fixpoint theory, an abstract algebraical study of semantics of nonmonotonic logics. As such, our results also apply to logic programming, default logic, Dung’s argumentation frameworks, and abstract dialectical frameworks.

Recently, FO(C), the integration of C-LOG with classical logic, was introduced as a knowledge representation language. Up to this point, no systems exist that perform inference on FO(C), and very little is known about properties of inference in FO(C). In this paper, we study both of the above problems. We define normal forms for FO(C), one of which corresponds to FO(ID). We define transformations between these normal forms, and show that, using these transformations, several inference tasks for FO(C) can be reduced to inference tasks for FO(ID), for which solvers exist. We implemented this transformation and hence, created the first system that performs inference in FO(C). We also provide results about the complexity of reasoning in FO(C).

Abstract Dynamic systems play a central role in fields such as planning, verification, and databases. Fragmented throughout these fields, we find a multitude of languages to formally specify dynamic systems and a multitude of systems to reason on such specifications. Often, such systems are bound to one specific language and one specific inference task. It is troublesome that performing several inference tasks on the same knowledge requires translations of your specification to other languages. In this paper we study whether it is possible to perform a broad set of well-studied inference tasks on one specification. More concretely, we extend IDP3with several inferences from fields concerned with dynamic specifications.

This paper considers the fragment ∃∀SO of second-order logic. Many interesting problems, such as conformant planning, can be naturally expressed as finite domain satisfiability problems of this logic. Such satisfiability problems are computationally hard (ΣP2) and many of these problems are often solved approximately. In this paper, we develop a general approximative method, i.e., a sound but incomplete method, for solving ∃∀SO satisfiability problems. We use a syntactic representation of a constraint propagation method for first-order logic to transform such an ∃∀SO satisfiability problem to an ∃SO(ID) satisfiability problem (second-order logic, extended with inductive definitions). The finite domain satisfiability problem for the latter language is in NP and can be handled by several existing solvers. Inductive definitions are a powerful knowledge representation tool, and this moti- vates us to also approximate ∃∀SO(ID) problems. In order to do this, we first show how to perform propagation on such inductive definitions. Next, we use this to approximate ∃∀SO(ID) satisfiability problems. All this provides a general theoretical framework for a number of approximative methods in the literature. Moreover, we also show how we can use this framework for solving practical useful problems, such as conformant planning, in an effective way.

Abstract Given a causal model of some domain and a particular story that has taken place in this domain, the problem of actual causation is deciding which of the possible causes for some effect actually caused it. One of the most influential approaches to this problem has been developed by Halpern and Pearl (Halpern, J. and Pearl, J. 2005. Causes and explanations: A structural-model approach. Part I: Causes.British Journal for the Philosophy of Science56(4), 843–887) in the context of structural models. In this paper, I argue that this is actually not the best setting for studying this problem. As an alternative, I offer the probabilistic logic programming language of CP-logic. Unlike structural models, CP-logic incorporates the deviant/default distinction that is generally considered an important aspect of actual causation, and it has an explicitly dynamic semantics, which helps to formalize the stories that serve as input to an actual causation problem.

We introduce First Order ProbLog, an extension of first order logic with soft constraints where formulas are guarded by probabilistic facts. The paper defines a semantics for FOProbLog, develops a translation into ProbLog, a system that allows a user to compute the probability of a query in a similar setting restricted to Horn clauses, and reports on initial experience with inference.

Abstract PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules. In this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of “chance rules”. The underlying PRISM system can then be used for several probabilistic inference tasks, including probability computation and parameter learning. We define the CHRiSM language in terms of syntax and operational semantics, and illustrate it with examples. We define the notion of ambiguous programs and define a distribution semantics for unambiguous programs. Next, we describe an implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between CHRiSM and other probabilistic logic programming languages, in particular PCHR. Finally, we identify potential application domains.

There are many reasons why software can be hard to implement. For important classes of applications, the main source of complexity is the domain knowledge that is involved. One such class is that of configuration software, which serves to assist a user in making choices in accordance with certain constraints. For instance, consider an application that helps students compose a study program that complies with all relevant university regulations. The reason why this may be difficult to implement is that these regulations can get quite complicated, making them hard to handle, at least for imperative programming methods. A better approach might be to follow the paradigm of a knowledge base system: explicitly represent the domain knowledge in a declarative way, and implement the behavior of the application by performing various logical inference methods on it. Doing this well, however, requires that a number of different components be got right. Most importantly, we need an expressive and purely declarative knowledge representation language, together with a set of useful inference methods. In this paper, we present a framework for implementing this kind of software, based on a rich extension of first-order logic.

Abstract This paper develops a logical language for representing probabilistic causal laws. Our interest in such a language is two-fold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.

status: Published

It is well known that, under certain conditions, it is possible to split logic programs under stable model semantics, that is, to divide such a program into a number of different “levels”, such that the models of the entire program can be constructed by incrementally constructing models for each level. Similar results exist for other nonmonotonic formalisms, such as auto-epistemic logic and default logic. In this work, we present a general, algebraic splitting theory for logics with a fixpoint semantics. Together with the framework of approximation theory , a general fixpoint theory for arbitrary operators, this gives us a uniform and powerful way of deriving splitting results for each logic with a fixpoint semantics. We demonstrate the usefulness of these results, by generalizing existing results for logic programming, auto-epistemic logic and default logic.