The OWL 2 profile OWL 2 QL, based on the DL-Lite family of description logics, is emerging as a major language for developing new ontologies and approximating the existing ones. Its main application is ontology-based data access, where ontologies are used to provide background knowledge for answering queries over data. We investigate the corresponding notion of query inseparability (or equivalence) for OWL 2 QL ontologies and show that deciding query inseparability is PSPACE-hard and in EXPTIME. We give polynomial time (incomplete) algorithms and demonstrate by experiments that they can be used for practical module extraction.

Understanding the interaction between emotion and cognitive processes is important for developing architectures for general intelligence, and vital for the fields of human social and behavioral modeling, game intelligence, and human-computer interaction. However, relatively little work in AI has been done on emotion in intelligent architectures, particularly on the effect of emotions on cognitive processes such as inference, planning and learning, despite research showing that emotion is a crucial and often beneficial factor in human decision-making. My work will provide a new emotional-cognitive architecture, focusing on a small set of theories, mechanisms and algorithms for the modeling of a wide array of emotional effects on human cognitive processes. The work and its results will be evaluated against current computational models of cognition and emotion, and validated by results from human cognitive science, neuroscience, and psychology.

In applications including chemoinformatics, bioinfor- matics, information retrieval, text classification, com- puter vision and others, a variety of common issues have been identified involving frequency of occurrence, variation and similarities of instances, and lack of pre- cise class labels. These issues continue to be important hurdles in machine intelligence and my doctoral thesis focuses on developing robust machine learning models that address the same.

HAPTIC is individuals - agents or humans - within them to establish grounded in game theory and probabilistic modeling. It has successful relationships with their partners. In Supply been proved that HAPTIC agents learn other agents' behaviors Chain Management (SCM), establishing trust improves the reliably using direct observations. One shortcoming of chances of a successful supply chain relationship, and increases HAPTIC is that it does not support reported observations.

Ensemble classification methods have been shown to produce more accurate predictions than the base component models. Due to their effectiveness, ensemble approaches have been applied in a wide range of domains to improve classification. The expected prediction error of classification models can be decomposed into bias and variance. Ensemble methods that independently construct component models (e.g., bagging) can improve performance by reducing the error due to variance, while methods that dependently construct component models (e.g., boosting) can improve performance by reducing the error due to bias and variance. Although ensemble methods were initially developed for classification of independent and identically distributed (i.i.d.) data, they can be directly applied for relational data by using a relational classifier as the base component model. This straightforward approach can improve classification for network data, but suffers from a number of limitations. First, relational data characteristics will only be exploited by the base relational classifier, and not by the ensemble algorithm itself. We note that explicitly accounting for the structured nature of relational data by the ensemble mechanism can significantly improve ensemble classification. Second, ensemble learning methods that assume i.i.d. data can fail to preserve the relational structure of non-i.i.d. data, which will (1) prevent the relational base classifiers from exploiting these structures, and (2) fail to accurately capture properties of the dataset, which can lead to inaccurate models and classifications. Third, ensemble mechanisms that assume i.i.d. data are limited to reducing errors associated with i.i.d. models and fail to reduce additional sources of error associated with more powerful (e.g., collective classification models. Our key observation is that collective classification methods have error due to variance in inference. This has been overlooked by current ensemble methods that assume exact inference methods and only focus on the typical goal of reducing errors due to learning, even if the methods explicitly consider relational data. Here we study the problem of ensemble classification for relational domains by focusing on the reduction of error due to variance. We propose a relational ensemble framework that explicitly accounts for the structured nature of relational data during both learning and inference. Our proposed framework consists of two components. (1) A method for learning accurate ensembles from relational data, focusing on the reduction of error due to variance in learning, while preserving the relational characteristics in the data. (2) A method for applying ensembles in collective classification contexts, focusing on further reduction of the error due to variance in inference, which has not been considered in state of the art ensemble methods.

The goal of my research is to develop and evaluate long-term declarative memory mechanisms that are effective and efficient across a variety of tasks.

Cluedo) serves as a fun when it comes to designing an optimal (or even practicable) focus problem for this introduction to propositional knowledge solution. The potential solutions also touch on many representation and reasoning. After covering fundamentals areas of AI, so the students can be creative in applying and of propositional logic, students first solve basic synthesizing what they've learned to a new problem. The logic problems with and without the aid of a satisfiability three challenges give the students the opportunity to choose solver (e.g.

Integrating games into the computer science curriculum has been gaining acceptance in recent years, particularly when used to improve student engagement in introductory courses. This paper argues that games can also be useful in upper level courses, such as general artificial intelligence and machine learning. We provide a case study of using a Mario game in a machine learning class to provide one successful data point where both content-specific and general learning outcomes were successfully achieved.

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

The undergraduate computer science curriculum is generally focused on skills and tools;  most students are not exposed to much  research in the field, and do not learn how to navigate the research literature.  We describe how science fiction reviews were used as a gateway to research reviews.  Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related their chosen science fiction book, movie, or TV show.