PoK

Author:Klaus Dieter Althoff, Ralph Bergmann, Stefan Wess, Michel Manago, Eric Auriol, Oleg. I. Larichev, Alexander Bolotov, Yurii I. Zhuravlev, Serge I. Gurov

Description:
We describe an approach for developing knowledge based medical decision support systems based on the rather new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca project which particularly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis, as it was available among the partners, and ‘scale-up’ to more general non-technical decision support tasks as typically given in medical domains. Inreca technology is used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases that are caused by psychotropes

Author:Yan Zhai, Peng Ning, Purush Iyer, Douglas S. Reeves

Description:
This paper presents techniques to integrate and reason about complementary intrusion evidence such as alerts generated by intrusion detection systems (IDSs) and reports by system monitoring or vulnerability scanning tools. To facilitate the modeling of intrusion evidence, this paper classifies intrusion evidence into either event-based evidence or state-based evidence. Event-based evidence refers to observations (or detections) of intrusive actions (e.g., IDS alerts),while state-based evidence refers to observations of the effects of intrusions on system states. Based on the interdependency between event-based and state-based evidence, this paper develops techniques to automatically integrate complementary evidence into Bayesian networks, and reasonabout uncertain or unknown intrusion evidence based on verified evidence. The experimental results in this paper demonstrate the potential of the proposed techniques. In particular, additional observations by system monitoring or vulnerability scanning tools can potentially reduce the false alert rate and increase the confidence in alerts corresponding to successful attacks.

Authors: Sanja Petrovic, Gareth Beddoe, Greet Vanden Berghe

Description:
The inherent diffculties in eliciting domain knowledge from experts are often encountered when applying artificial intelligence techniques to real-world problems characterised by multiple conflicting constraints. Definitions of optimal solutions are often subjective and highly dependent on the opinions and work practices of individual experts. We developed a case-based reasoning approach to capture concepts of optimality through the storage, reuse, and adaptation of previous repairs of constraint violations. The technique is applied to the problem of rostering nurses at the Queens Medical Centre, Nottingham. An iterative roster repair system is presented that learns repair techniques from nurses with rostering experience.

Authors: Syed Sibte Raza Abidi

Description:
We present a Personalized Health Information Generation and Delivery System that leverages case based reasoning techniques to dynamically author a Personalized Health Information Package based on an individual’s current health profile. The work features a compositional adaptation approach, whereby relevant health information elements from the solution component of multiple similar past cases are carefully selected and systematically combined to yield a new personalized health information package. We have implemented a generic Java-based case based reasoning engine that applies a novel compositional adaptation algorithm to author a HTML-based personalized health information package that can be emailed to users.

Authors: Matt Healy, Sarah Jane Delany, and Anton Zamolotskikh

Description:
With an increased emphasis on problem solving and problem-based learning in the instructional design field, new methods for task analysis and models for designing instruction are needed. An important methodology for both entails the elicitation, analysis, and inclusion of stories as a primary form of instructional support while learning to solve problems. Stories are the most natural and powerful formalism for storing and describing experiential knowledge that is essential to problem solving. The rationale and means for analyzing, organizing, and presenting stories to support problem solving are defined by case-based reasoning. Problems are solved by retrieving similar past experiences in the form of stories and applying the lessons learned from those stories to the new problems. In this paper, after justifying the use of stories as instructional supports, we describe methods for eliciting, indexing, and making stories available as instructional support for learning to solve problems.