Exploiting domain knowledge for approximate diagnosis

The AI literature contains many definitions of diagnostic reasoning most of which are defined in terms of the logical entailment relation. We use existing work on approximate entailment to define notions of approximation in diagnosis. We show how such a notion of approximate diagnosis can be exploited in various diagnostic strategies. We illustrate these strategies by performing diagnosis in a small car domain example

Validation and verification of conceptual models of diagnosis

Traditional approaches to validation and verification of KBS aim at investigating properties of a KBS which are independent of the particular task of the KBS, and are phrased in terms of the implementation language of the final system. In contrast to this, we propose an approach to validation and verification of KBS which exploits task-specific properties of a KBS, and which is based on an implementation-independent conceptual model of the system

Using domain knowledge to select solutions in abductive diagnosis

This paper presents a novel extension to abductive reasoning in causal nets, namely the use of domain knowledge to select among alternative diagnoses. We describe how preferences among multiple causes of a given state can be expressed in terms of causal nets, and how these preferences can be used to select among alternative diagnoses. We investigate this new extension by proving a number of properties, and show how our preference scheme interacts with conventional ways of choosing among competing diagnoses. Our extension increases the expressive power of causal nets, enjoys a number of desirable properties, and compares favourably with existing proposals for expressing preferential knowledge in causal nets

An extended spectrum of logical definitions for diagnostic sytems

The goal of this work is to develop a single uniform theory, which enables us to describe many different diagnostic systems. We will give a general definition of diagnostic systems. Our claim is that a large number of very different diagnostic systems can be described by this definition by choosing the right values for six parameters in this definition. Our work is an extension of the spectrum of logical definitions of Console and Torasso

Approximations in diagnosis: motivations and techniques

We argue that diagnosis should not be seen as solving a problem with a unique definition, but rather that there exists a whole space of reasonable notions of diagnosis. These notions can be seen as mutual approximations. We present a number of reasons for choosing among different notions of diagnosis. We also present an exhaustive categorisation of techniques that can be employed to obtain approximations, as well as a number of specific example techniques for each category. We also show that it is possible to characterise the relations between the approximations obtained by these techniques

Childhood emotional maltreatment : impact on cognition and the brain

When a child is often scolded or threatened by his parents (emotional abuse) and /or when a child is structurally ignored or isolated by his parents (emotional neglect) we call this childhood emotional maltreatment (CEM). CEM is the most common form of child abuse, however, CEM is also the most hidden, underreported and least studied form of child abuse. An important reason for this may be because that the consequences of CEM are underestimated (e.g. __Sticks and Stones may break bones, but words will never hurt me__). However, this thesis shows that CEM is related with a persistent negative impact on cognition and the brain. We discovered that individuals that report CEM show differential structure and function of a brain area (the medial prefrontal cortex) that is crucial for role in responding to stress and thinking about yourself. Individuals with CEM also showed more activity in an area that signals threat (the amygdala) which may represent a persistent vigilance towards the detection of threat from others. These brain changes may underlie our other findings that individuals with CEM think more negatively about themselves and others. Negative thoughts can evoke negative thoughts and in new situations, which reinforces more negative memories. Due to this process, emotionally abused individuals may be more vulnerable to develop a depressive and/or anxiety disorder. Our findings warrant scientific and policital investments to increase societal awareness about the detrimental impact of CEM on cognition and the brain. Increased societal knowledge will hopefully lead to better awareness, reports, and subsequent interventions for individuals with CEM.FSW - Self-regulation models for health behavior and psychopathology - ou

Streaming the Web: Reasoning over dynamic data.

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved