Honorary Research Fellow, School of Computer Science,
I am currently an independent researcher working on artificial metacognitive systems. These are systems that monitor their own information processing so that they can respond to potential problems or opportunities. (Alternative terms are meta-reasoning or meta-management). My specialisation is in non-hierarchical metacognitive systems where multiple components critically evaluate each other (with no central control).
I have recently started investigating relations between computational models of metacognition and models of behaviour change in health psychology. Control is an important aspect of metacognitive systems (for instance, deciding to resist unhealthy food).
My recent research has also involved decision support and other kinds of assistance in e-Health (with University of Manchester). I have a special interest in ensuring that automated decision-making and semantic interpretation of data are consistent with the concerns of people affected by decisions (value-aware computing). One project involves mental health service users: with OpenClinical.
In addition to e-Health, I have also developed concepts for value-aware computing in e-Democracy (in collaboration with MIT and UNCC ) on a consultancy basis. I recently spent a year at MIT CSAIL, working on metacognition in social systems.
Earlier I worked with Georgios Theodoropoulos on decision support systems in the social sciences. Our primary collaborators were in the School of Public Policy. We completed a study entitled "Adaptive Intelligent Modelling for the Social Sciences (AIMSS)". Details are here. This was a small grant project funded by the Economic and Social Research Council and was coordinated by the National Centre for e-Social Science (NCeSS) in Manchester. I also spent six months at NCeSS as a visiting researcher. As a result of that visit, and the experience of the AIMSS project, I started to investigate participatory determination of semantics for information systems.
Distributed Metareasoning: My PhD thesis was Distributed Reflective Architectures for Anomaly Detection and Autonomous Recovery and is available in the Cognition and Affect Directory. The aim of the research was to explore architectures which allow an autonomous system to detect and recover from anomalies without user intervention. An anomaly is any event that deviates from the model-predicted state of the world and may also occur in the system's own software or hardware. This means that the system must have a model of its own operation (reflection). Recently I have been extending this work to include some aspects of human-like metacognition.
The thesis was inspired by various branches of philosophy and biology, in particular by autopoiesis theory, immune system models and Minsky's Society of Mind concept. Some consultation with dependability researchers also took place. The SimAgent package was used as a simulation tool for conceptual exploration and rapid prototyping.
The research addressed the problem of ensuring that critical requirements are met in anomalous situations (e.g. software failure due to intrusions or design flaws). Moreover, the system should focus attention on the most critical anomalies and ignore others. E.g. some software anomalies may only occasionally cause delays or minor inaccuracies, while others can cause the whole system to fail, in particular if they are due to deliberate intrusion.
This file is maintained by Catriona Kennedy
Last updated June 2019.