Speaker:
Dr. Piero P. Bonissone
GE Global Research, Schenectady, New York
Title:
Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff, and Interaction Visualization
Date:
Friday, September 17, 2010
Time:
2:00 pm
Location:
RCH 309
Abstract:
We consider Multi Criteria Decision Making (MCDM) as the conjunction
of three components:
search,
preference tradeoffs,
and
interactive
visualization. The first MCDM component is the search process over the
space of possible solutions to identify the non-dominated solutions that
compose the Pareto set. The development of efficient search algorithms
has been the goal of Multi-Objective Optimization (MOO), from classical
mathematical programming to evolutionary approaches. However MOO's
emphasis has been on generating densely sampled, well-distributed Pareto
sets, without worrying about the solution selection phase. The second
component is the preference tradeoff process to select a single solution
(or a small subset of solutions) from the Pareto set. The development of
methods to capture and aggregate preferences has been the goal of Bayesian
and Fuzzy decision-making techniques. However, their emphasis has been on
the aggregation mechanisms to select a solution, rather than the solution
generation phase. The third component is the interactive visualization
process to embed the decision-maker in the solution refinement and
selection loop. We often need to embed the decision-maker in the solution
refinement and selection loop. To this end, we need to understand and
present the impacts that intermediate tradeoffs in one sub-space could
have in the other ones, while allowing him/her to retract or modify any
intermediate decision steps to strike appropriate tradeoff balances. We
focus on the intersection of these three components and we highlight some
research challenges, representing gaps in the intersection. We introduce
a requirement framework to compare most MCDM problems, their solutions,
and analyze their performances. We focus on two research challenges and
illustrate them with two case studies in electric power management and
financial portfolio rebalancing.
Biography:
Dr. Piero Bonissone is a Coolidge Fellow and a Chief Scientist at GE
Global Research. He has published over 150 articles in the area of soft
computing, computational intelligence, expert systems, approximate
reasoning, fuzzy sets, pattern recognition, decision analysis, and
prognostics and health management (PHM). He is a Fellow of AAAI, IEEE,
and IFSA. He was President of the IEEE Neural Networks Society (now
IEEE Computational Intelligence Society). He received 49 patents from
the U.S. Patent Office (and 50 more are pending).
Invited by:
Prof.
F. Karray
This talk is organized by the Kitchener Waterloo Chapter of the
IEEE Computational Intelligence Society and is supported by the IEEE
Computational Intelligence Society Distinguished Lecturers Program.
