In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.

We discuss the problems of modeling, control, and decision support in complex dynamic systems from a general system theoretic point of view. The main characteristics of complex systems and of system approach to complex system study are considered. We provide an overview and analysis of known existing paradigms and methods of mathematical modeling and simulation of complex systems, which support the processes of control and decision making. Then we continue with the general dynamic modeling and simulation technique for complex hierarchical systems functioning in control loop. Architectural and structural models of computer information system intended for simulation and decision support in complex systems are presented.

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

The Synthesis of ACT-R and Leabra (SAL) hybrid cognitive architecture is the integration of two theories of cognitive functioning, each itself a highly integrative theory of cognition, ACT-R being predominantly a symbolic production-rule based architecture and Leabra a neural modeling architecture. The combination of the two architectures allows for richer dynamics that take advantage of neural and symbolic aspects and provides mutual constraints that promote convergence towards models that are both neurophysiologically and psychologically valid. We present a hybrid model that makes use of multi-level and multi-system integration to allow an instructed assembly task to be carried out in way that is noise and error robust. Specifically, the model shows how higher-level error recovery routines can interface with lower-level sensory, motor, and error detection processes and result in a robustness to noise and noise-induced errors. Multiple systems and processes operating at multiple levels are recruited to provide a way around the limitations of simpler systems composed of isolated modules that do not allow information to be propagated as easily. The benefits of this approach provide motivation for the adoption of a generally integrated approach to cognitive systems.

MetaShare is a knowledge-based system that supports the creation of data management plans and provides the functionality to support researchers as they implement those plans. MetaShare is a community-based, user-driven system that is being designed around the parallels of the scientific data life cycle and the development cycle of knowledge-based systems. MetaShare will provide recommendations and guidance to researchers based on the practices and decisions of similar projects. Using formal knowledge representation in the form of ontologies and rules, the system will be able to generate data collection, dissemination, and management tools to facilitate tasks with respect to using and sharing scientific data. MetaShare, which is initially targeting the research community at the University of Texas at El Paso, is being developed on a Web platform, using Semantic Web technologies. This paper presents a roadmap for the development of MetaShare, justifying the functionality and implementation decisions. In addition, the paper presents an argument concerning the return on investment for researchers and the planned evaluation for the system.

Monte Carlo Tree Search (MCTS) has produced many recent breakthroughs in game AI research, particularly in computer Go. In this paper we consider how MCTS can be applied to create engaging AI for a popular commercial mobile phone game: Spades by AI Factory, which has been downloaded more than 2.5 million times. In particular, we show how MCTS can be integrated with knowledge-based methods to create an interesting, fun and strong player which makes far fewer plays that could be perceived by human observers as blunders than MCTS without the injection of knowledge. These blunders are particularly noticeable for Spades, where a human player must co-operate with an AI partner. MCTS gives objectively stronger play than the knowledge-based approach used in previous versions of the game and offers the flexibility to customise behaviour whilst maintaining a reusable core, with a reduced development cycle compared to purely knowledge-based techniques.

Inquire Biology is a prototype of a new kind of intelligent textbook — one that answers students’ questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students’ questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.

Learner assessment is nontrivial even in its most basic incarnation, namely evaluating a learner's understanding of a set of domain-dependent skills from ad hoc test items (for example, Desmarais [2011]). The assessment challenges increase with the complexity of the learner's traits to be captured, because how a student behaves during an instructional activity generally provides partial and ambiguous information on the student's underlying states, and the gap between what can be observed and what a learner actually thinks and feels increases as these states go from cognitive to metacognitive and affective. In ITSs, the research field concerned with addressing these challenges is known as student modeling, and a student model is the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs. Student modeling research has made the problem solution from the tutor et al. [2010]), given extensive evidence substantial progress in providing reliable (for instance by repeatedly asking for in education research showing that learner assessment during problem help) without trying to solve the problem affective factors play an important role solving or question-answering on their own (Baker et al. 2008), in learning. Educational technology At the cognitive level, knowledge can foster understanding at different however, continues to produce novel assessment, that is, evaluating the student's stages of the learning process or for environments often consisting of knowledge of relevant concepts learners with different preferences and activities not as structured and well and skills at specific points of the interaction abilities.

Interestingness measures provide information that can be used to prune or select association rules. A given value of an interestingness measure is often interpreted relative to the overall range of the values that the interestingness measure can take. However, properties of individual association rules restrict the values an interestingness measure can achieve. An interesting measure can be standardized to take this into account, but this has only been done for one interestingness measure to date, i.e., the lift. Standardization provides greater insight than the raw value and may even alter researchers' perception of the data. We derive standardized analogues of three interestingness measures and use real and simulated data to compare them to their raw versions, each other, and the standardized lift.