Modeling skills are essential during the process of learning programming. ITS systems for modeling are typically hard to build due to the ill-definedness of most modeling tasks. This paper presents a system that can teach UML skills to novice programmers. The system is “simple and cheap” in the sense that it only requires an expert solution against which the student solutions are compared, but still flexible enough to accommodate certain degrees of solution flexibility and variability that are characteristic of modeling tasks. An empirical evaluation via a controlled lab study showed that the system worked fine and, while not leading to significant learning gains as compared to a control condition, still revealed some promising results.

Intelligent tutoring systems (ITS) is a multidisciplinary field of study that draws upon artificial intelligence, computer science, and cognitive science to create computerized tutoring systems that offer immediate feedback and individualized instruction. Broadly construed, most intelligent tutoring systems can be characterized as having two loops: an outer loop and an inner loop. In general, the goal of the track is to bring together an international group of scientists to present current research, design, and empirical evaluations of their tutoring systems. is track is meant to inform researchers on the recent developments in both the design of tutoring systems, as well as their evaluation. Topics included game-based, narrative-based and virtual learning environments; NLP and dialogue in tutoring systems; modeling and shaping affective state; metacognition; gaming the system; ill-defined domains; educational data mining; authoring tools for nonexperts; adaptive educational hypermedia; collaborative and group learning; open learner modeling; ontology engineering for educational purposes; novel interfaces; human computer interaction in educational settings; design decisions to increase engagement; and assistive technologies for learners with special needs.

Artifact capabilities can play an important role in understanding human cognition. Over time humans learn to use artifacts, evolve the knowledge and combine acquired capabilities with others to form complex capabilities. In this study we present a hybrid ontology of artifacts to facilitate learning artifact capabilities. We develop a framework where agents simultaneously exploit a centralized artifact ontology in the environment and a distributed artifact ontology local to each agent. We demonstrate how both ontologies can be used by agents both in the artifact selection process and in learning artifact use. The local ontology serves as domain knowledge gained by the agent as it learns. We illustrate an example to show how an acquired artifact capability can be stored in an agent's local ontology for future use.

Event Management is one of the most lucrative and growing professions today. At present event management is done by humans. With the growing demand for managing large events, there is a rising demand for building intelligent systems to manage events. The so called event management systems today are only data processing systems that are unable to carry out decision making task on their own. Event management systems today do not consider emergencies and risk assessment as part of their execution. In this paper, we present an approach for representing events and monitor their execution. In particular, discuss the exceptions that can occur during an event execution and how they can be managed using event management rules. We present strategies for writing management rules that are used to handle problematic events and to build a DAG based programming system for event management. Our simulation results show how the performance of our event management system performs with the exception management rules.

In this paper, we suggest to increase the quality and the precision of a document description using publication’s context description. Today, a lot of linguistic resources are both available on line and described by specific metadata. We first integrate them into an ontology which describes how linguists consider their primary data and tools. Then, we add to this ontology an inference system based on the information flow theory in order to establish causal relations between heterogeneous data. The result of the inference is characterized by a small set of properties which are embedded into three sequences of metadata enhancing the usual metadata describing publications.

Propositional attitudes in noncompositional logic are analysed from the view point of integration of their epistemic and deontic components. A new logical calculus for propositional attitudes inspired by possibility theory, a noncompositional version of fuzzy logic is proposed.

In this paper we present a new method for ontology selection in a reuse context. The novel feature of this method is the iterative selection of the reused ontologies. Ontology selection is guided by the user according to his requirements and his perception to the target domain. Starting from a first selected ontology, the concepts with the weakest density are identified then the ontology developer is enabled to choose among them the ones to be refined in order to cover a specific scope of the domain.

Entity coreference is the process to decide which identifiers (e.g., person names, locations, ontology instances, etc.) refer to the same real world entity. In the Semantic Web, entity coreference can be used to detect equivalence relationships between heterogeneous Semantic Web datasets to explicitly link coreferent ontology instances via the owl:sameAs property. Due to the large scale of Semantic Web data today, we propose two pruning techniques for scalably detecting owl:sameAs links between ontology instances by comparing the similarity of their context graphs. First, a sampling based technique is designed to estimate the potential contribution of each RDF node in the context graph and prune insignificant context. Furthermore, a utility function is defined to reduce the cost of performing such estimations. We evaluate our pruning techniques on three Semantic Web instance categories. We show that the pruning techniques enable the entity coreference system to run 10 to 35 times faster than without them while still maintaining comparably good F1-scores.

According to a 2008 OECD review of national policies for education in South Africa, typically only 15 percent to 18 percent of secondary school students who sit for their final year exams every year qualify automatically for university-level education; and this number seems to be decreasing as more students choose to complete subjects on so-called standard grade instead of higher grade, a trend that is especially apparent for mathematics and science, the two fields with critical skills shortages in the country. The South African tertiary education sector is quite small for a country with a population of around 50 million, with 11 "traditional" universities, 6 technical universities, and 6 comprehensive universities. The latter university types focus on more technical or vocational education. The public sector also funds 16 research institutions. In spite of these obstacles, South African universities participate in world-class research activities in many fields and range among the best on the African continent.

This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.