Decision Support Systems Research Programme
History
The current Decision-Support Systems programme was started in May 2000, through
the appointment of L.C. van der Gaag as a full professor, upon being awarded the prestigious five-year pioneer-funding from the Netherlands Organization for Scientific Research (NWO Pionier) for her research
programme Practicable Decision Making Under Uncertainty. In 2007, professor Van der Gaag was honoured with an ECCAI Fellowship.
Aim
In many fields of our society, experts are called upon to solve complex decision
problems in their every-day problem-solving practice. Examples include the
medical field where specialists have to establish diagnoses and decide upon appropriate treatment alternatives, and the financial field where companies have
to forecast demand and decide upon investments. In many of these fields, the
problems to be solved are rapidly increasing in complexity, and highly tailored
support from computer-based systems for decision making is called for. The aim of the Decision-Support Systems research programme is to design a new
generation of decision-support systems that are capable of handling the increasing
complexity of the problems in their domains of application.
Approach
The Decision-Support Systems research programme is aimed at the design and
analysis of decision-support systems that build upon concepts and techniques
from statistics. Decision-support systems nowadays often call for the use of
such concepts and techniques, either because the problems to be solved in their
domain of application require reasoning with uncertainty for their solution or
because the complexity of these problems forestalls the use of exact algorithms.
Within the programme, the use of concepts and techniques from statistics for
decision support is being studied in two main research themes:
- decision making under uncertainty
- evolutionary computation
The research themes are being studied mainly from a fundamental
computer-science perspective, focusing on representation formalisms and associated
algorithms. Although firmly rooted in the field of statistics, statistics
itself is not the focus of research. The viability and practicability of the more
fundamental research results are being studied in experimental settings and in
real-life applications that are being developed with the help of domain experts.
Decision making under uncertainty
Decision making under uncertainty is the field of research that addresses
reasoning with uncertainty and reasoning about preferences for solving complex
decision problems. While traditionally building upon techniques from statistics,
the field is now showing an increasing impact from computer science. Research
efforts have resulted in powerful formalisms for capturing the probabilistic and
preferential relationships between a decision problem’s variables. These formalisms
typically build upon graphical representations, such as probabilistic
networks and decision-theoretic networks more in general. Algorithms have
been designed for computing probabilities of interest and optimal decisions from
such networks.
Within the research programme, the practicability of the above formalisms and their associated algorithms is the main focus of interest. The
design of methodologies and techniques for constructing and analysing probabilistic
networks is being pursued, as well as the design of efficient algorithms for
various types of probabilistic reasoning. To provide for studying the more fundamental research results in a realistic setting, a number of real-life probabilistic networks are being developed for complex medical (both human and veterinary) decision problems.
Evolutionary computation
Evolutionary computation is the field of research that studies algorithms for
search, optimisation, and adaptation that are inspired by the mechanisms of natural
evolution and genetics. Neo-darwinian principles can lead to efficient and
robust search mechanisms as exemplified by the evolution of species, the adaptive
recognition abilities of the human immune system, and the self-organising
development of neural pathways. In the field of evolutionary computation, the
general computational principles of these mechanisms are taken for the basis
of powerful algorithms for problem solving.
Within the research programme,
the characteristics of evolutionary search are being studied. In addition, the
development of methodological guidelines for designing evolutionary algorithms
as well as the design of new types of evolutionary algorithm based upon new
principles are being pursued. Research activities also include the use
of genetic algorithms and related stochastic search algorithms for constructing
probabilistic networks from data.
