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Künstliche Intelligenz 29(2) - Mai 2015

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  • Zeitschriftenartikel
    Facilitating Evolution during Design and Implementation
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) McClatchey, Richard
    The volumes and complexity of data that companies need to handle are increasing at an accelerating rate. In order to compete effectively and ensure their commercial sustainability, it is becoming crucial for them to achieve robust traceability in both their data and the evolving designs of their systems. This is addressed by the CRISTAL software which was originally developed at CERN by UWE, Bristol, for one of the particle detectors at the Large Hadron Collider, which has been subsequently transferred into the commercial world. Companies have been able to demonstrate increased agility, generate additional revenue, and improve the efficiency and cost-effectiveness with which they develop and implement systems in various areas, including business process management (BPM), healthcare and accounting applications. CRISTAL’s ability to manage data and its semantic provenance at the terabyte scale, with full traceability over extended timescales, together with its description-driven approach, has provided the flexible adaptability required to future proof dynamically evolving software for these businesses.
  • Zeitschriftenartikel
    Revolution in Health and Wellbeing
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Lőrincz, András
    We argue that recent technology developments hold great promises for health and wellbeing. In our view, recent advances of (1) smart tools and wearable sensors of diverse kinds, (2) data collection and data mining methods, (3) 3D visual recording and visual processing methods, (4) 3D models of the environment with robust physics engine, and last but not least, (5) new applications of human computing and crowdsourcing started the revolution. We are neither claiming nor excluding that human intelligence will be reached in some years from now, but make the above claim, which is both weaker and stronger. We believe that fast developments for health and wellbeing are the question of active collaboration between health and wellbeing experts and motivated engineers.
  • Zeitschriftenartikel
    Cognitive Endurance for Brain Health: Challenges of Creating an Intelligent Warning System
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Hedman, Anders; Hallberg, Josef
    During the past few years, the market for apps monitoring traditional health and wellbeing parameters such as heart rate, levels of physical activity and sleep patterns has rapidly expanded. In this paper, we articulate how we are currently engineering an early warning system designed to support long-term brain health, termed cognitive endurance, based on such monitoring. It can be thought of as a rudimentary expert system. It will monitor physical and social activity, stress and sleep patterns and signal when these parameters are such that a person’s cognitive endurance might be at risk. The aim of the system is to guide the user to adopt sustainable behavioral patterns from a cognitive endurance perspective. This paper articulates (1) what we mean by cognitive endurance, (2) how cognitive endurance may be enhanced, (3) our cognitive endurance monitoring platform, (4) our approach to calculating cognitive endurance risk, (5) specific challenges related to our approach and (6) what the long term benefits might be of successively monitoring cognitive endurance.
  • Zeitschriftenartikel
    KI Fachbereichspolitik und Künstliche Kognitive Systeme
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Visser, Ubbo
  • Zeitschriftenartikel
    Technology Roadmap Development for Big Data Healthcare Applications
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Zillner, Sonja; Neururer, Sabrina
    Big data applications indicate a wide range of opportunities to improve the overall quality and efficiency of healthcare delivery. The highest impact of big data applications is expected when data from various healthcare areas, such as clinical, administrative, financial, or outcome data, can be integrated. However, as of today, the realization of big data healthcare applications aggregating various kinds of data sources is still lacking behind. In order to foster the implementation of comprehensive big data applications, a clear understanding of short-term and long-term goals of envisioned big data scenarios is needed to forecast which emerging big data technologies are needed at what point in time. The contribution of this paper is to introduce the development of a technology roadmap for big data technologies in the healthcare domain. Beside the description of user needs and the technologies needed in order to satisfy those needs, the technology roadmap provides a basis to forecast technology developments and, thus, guidance in planning and coordinating technology developments accordingly.
  • Zeitschriftenartikel
    An Interactive Narrative Format for Clinical Guidelines
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Cavazza, Marc; Charles, Fred; Lindsay, Alan; Siddle, Jonathan; Georg, Gersende
    Clinical guidelines are standardised documents, which summarise best practice in complex medical situations. Their target audience comprises health professionals, or in some cases patient groups, for whom they constitute important sources of patient education. These documents are characterised by a rich knowledge content, which also relies on a complex, largely implicit background. At the heart of guidelines is a set of recommendations describing expected behaviour throughout specific, evolving contexts. Such complex documents can be challenging to assimilate, in particular their patient education versions. The need to contextualise information and visualise behaviours and their consequences suggests the use of virtual environments, as in serious gaming. However, knowledge representation in serious games are often limited and the overall implementation mainly empirical. On the other hand, interactive narratives technologies have demonstrated their ability to embed complex behavioural knowledge and support principled behaviour responding to dynamic contexts. This is why they support the exploration of complex situations, their rehearsal, and the understanding of expected behaviour through what-if interaction. The narrative perspective also provides better user guidance than a pure simulation system, allowing mixed-initiative access to information. The translation of medical protocols as interactive narratives is faced with a number of knowledge representation challenges, in particular for the representation of non-compli-ance and the consequences of incorrect behaviour. Another technical issue is the need to represent both common sense and domain knowledge, and articulate their representation with the Planning domain that forms the backbone of the interactive narrative. As part of the Open FET project MUSE (FP7-296703), we are developing a proof-of-concept prototype exploring the above aspects, and embedding the logical structure of guidelines into a real-time interactive narrative, which provides a principled simulation of the situations faced by patients, which preserves causal and deontic constraints. This paper describes the knowledge engineering process supporting the development of this prototype, from the analysis of patient guidelines to the use of planning representations supporting the interactive narrative.
  • Zeitschriftenartikel
    Now All Together: Overview of Virtual Health Assistants Emulating Face-to-Face Health Interview Experience
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Lisetti, Christine; Amini, Reza; Yasavur, Ugan
    We discuss a large research project aimed at building socially expressive virtual health agents or assistants (VHA) that can deliver brief motivational interventions (BMI) for behavior change, in a communication style that individuals and patients not only accept, but also find emotionally supportive and socially appropriate. Because of their well-defined sequential structure, BMIs lend themselves well to automation, and are adaptable to address a variety of target behaviors, from obesity, to alcohol and drug use, to lack treatment adherence, among others. We discuss the advantages that VHAs provide for the delivery of health interventions. We describe components of our intelligent agent architecture that enables our virtual health agents to dialogue with users in realtime while delivering the appropriate intervention based on the patient’s specific needs at the time. We conclude by identifying open research challenges in developing virtual health agents.
  • Zeitschriftenartikel
    Sensemaking in Intelligent Health Data Analytics
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Boman, Magnus; Sanches, Pedro
    A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.
  • Zeitschriftenartikel
    A Paradigm Shift in Healthcare Provision
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Sonntag, Daniel; Gelissen, Jean
  • Zeitschriftenartikel
    Towards Configuration Planning with Partially Ordered Preferences: Representation and Results
    (KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Silva-Lopez, Lia Susana d. C.; Broxvall, Mathias; Loutfi, Amy; Karlsson, Lars
    Configuration planning for a distributed robotic system is the problem of how to configure the system over time in order to achieve some causal and/or information goals. A configuration plan specifies what components (sensor, actuator and computational devices), should be active at different times and how they should exchange information. However, not all plans that solve a given problem need to be equally good, and for that purpose it may be important to take preferences into account. In this paper we present an algorithm for configuration planning that incorporates general partially ordered preferences. The planner supports multiple preference categories, and hence it solves a multiple-objective optimization problem: for a given problem, it finds all possible valid, non-dominated configuration plans. The planner has been able to successfully cope with partial ordering relations between quantitative preferences in practically acceptable times, as shown in the empirical results. Preferences here are represented as c-semirings, and are used for establishing dominance of a solution over another in order to obtain a set of configuration plans that will constitute the solution of a configuration planning problem with partially ordered preferences. The dominance operators tested in this paper are Pareto and Lorenz dominance. Our solver considers one guiding heuristic for obtaining the first solution, and then switches to a dominance based monotonically decreasing heuristic used for pruning dominated partial configuration plans. In our empirical results, we perform a statistical study in the space of problem instances and establish families of problems for which our approach is computationally feasible.