Recent Funded Projects
- Adaptive Intelligent Decision Systems.
The purpose of this project is to develop adaptive intelligent systems
for decision support for time-critical applications in dynamic and
uncertain environments. We have completed a comprehensive literature
survey and have developed systems architectures, decision models and
computational algorithms for adaptive reasoning and decision making in
two domains, namely (1) adaptive manufacturing job-shop systems and
(2) adaptive multi-agent systems. These are domains with significant
applications in the real world and in the industry. The proposed
architectures and algorithms were evaluated and tested in a virtual
environment using discrete events simulations developed specially for
the two domains.
In the adaptive manufacturing decision support systems, the dynamic
job-shop scheduling and rescheduling under uncertainty in a
manufacturing environment was studied. Dynamic Bayesian Networks and
Influence Diagrams construction approaches have been adopted as the
main formalism in the system. This work advances the current state of
the art in industrial jobs-shop scheduling and will enable the
building of more effective decision support systems for industrial
applications such as in the electronics and semi-conductor
manufacturing sectors.
In the adaptive multi-agent decision systems, the rocks forging
problem has been selected as the application problem. The system was
tested for the effectiveness of the various agents' decision making
rules, how information are communicated and shared among the
cooperative agents, as well as the agent's learning schemes. This
work advances the current state of the art in the architecting of
decision support systems that has to deal with multiple players.
These are applicable to the building of command & control systems in
many domains such as military, maritime security, air-traffic control
etc.
- Advanced Planning & Decision Systems.
This project is funded by the Defense Sciecne and Technology Agency's
(DSTA) Defense Innovative Research Program and is in collaboration
with Decision Support Solutions Center of DSTA. The fundamental goal of this project
is to develop a set of advanced computational techniques and
algorithms that facilitate the building of advanced planning systems
for allocation of resources and intelligent decision systems that are
capable of responding to sensory inputs and providing optimal course
of action to decision makers in an uncertain and dynamic
environment. The project will focus on the optimization of large-scale
rosters that combines column generation with constraint programming,
and the development of intelligent normative decision systems for
automated reasoning and decision making in an uncertain and dynamic
environment.
- Intelligent Prognostic Analysis in Medicine.
This Agency for
Science, Technology & Research (A*STAR) funded-project is in
collaboration with the Department of Computer
Science,
Department of Medicine, the National Neuroscience Institute of
Singapore, Johns Hopkins
Hospital of USA, and ReasonEdge Technologies. This work
aims to develop a set of advanced decision engineering techniques to
support effective prognostic analysis in medicine; the resulting
techniques will be incorporated into a set of prototype applications
that automate clinical practice guideline generation in significant
and time-critical health care domains. Prognostic analysis is a
critical part of evidence-based medicine that emphasizes the effective
use of information to improve quality, reduce variation, and manage
resources in health care procedures. Prognostic analysis illuminates
the natural, as well as the expected, post-intervention course and
outcome of disease processes. It plays an important role in care
management tasks, including cost-effective diagnostic test and
treatment planning, prognostic prediction, pharmacoeconomic analysis,
health technology and policy assessment, and clinical practice
guideline generation.
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