Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff, and Interaction Visualizations

UPDATE: Free video of a two-hrs tutorials given at the 2009 IEEE Symposia Series on Computational Intelligence (SSCI) available at the IEEE CIS Education website:
http://sites.google.com/site/ciseducationsite/home/video-tutorials-produced-by-cis 

Part I:
http://ewh.ieee.org/cmte/cis/mtsc/ieeecis/ssci_09_video/Bonissone1_xvid.avi 

Part II:
http://ewh.ieee.org/cmte/cis/mtsc/ieeecis/ssci_09_video/Bonissone2_xvid.avi 

Part III:
http://ewh.ieee.org/cmte/cis/mtsc/ieeecis/ssci_09_video/Bonissone3_xvid.avi 

Computational Intelligence in Multi-Criteria Decision-Making:
The Intersection of Three Processes: Search, Preference Tradeoff, and Interaction Visualization (oct23_2009_MCDM_IEEE_Schenectady_LB_V3.pdf)

Multi Criteria Decision Making (MCDM): A Framework for Research and Applications (oct23_2009_2009GRC412_MCDM.pdf)

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, welldistributed 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.

ABOUT THE SPEAKER

A Chief Scientist at GE Global Research, Dr. Bonissone has been a pioneer in the field of fuzzy logic, AI, soft computing, and approximate reasoning systems applications since 1979. His current interests are the development of multi-criteria decision making systems applied to PHM issues, and the automation of intelligent systems lifecycle, i.e. the development of processes to create, deploy, and maintain smart SC-based systems that provide customized performance while adapting themselves to avoid obsolescence.

He is a Fellow of IEEE, of the Association for the Advancement of Artificial Intelligence (AAAI), of the International Fuzzy Systems Association (IFSA) and a Coolidge Fellow at GE Global Research. In 2008 he received the II Cajastur International Prize for Soft Computing from the European Centre of Soft Computing. In 2005 he received the Meritorious Service Award from the IEEE Computational Intelligence Society. He has received two Dushman Awards from GE Global Research. He served as Editor in Chief of the International Journal of Approximate Reasoning for 13 years. He is in the editorial board of five technical journals and is Editor-at-Large of the IEEE Computational Intelligence Magazine. He has co-edited six books and has over 150 publications in refereed journals, book chapters, and conference proceedings, with an H-Index of 22 (using Google Scholar). He received 45 patents issued from the US Patent Office (plus 47 pending patents). Since 1982, he has been an Adjunct Professor at Rensselaer Polytechnic Institute, in Troy N, where he has supervised 5 PhD theses and 33 Master theses. He has co-chaired 12 scientific conferences and symposia focused on Multi-Criteria Decision-Making, Fuzzy sets, Diagnostics, Prognostics, and Uncertainty Management in AI. Dr. Bonissone is very active in the IEEE, where is currently a member of the Fellow Evaluation Committee. In the past, while serving as President of the IEEE Neural Networks Society (now Computational Intelligence Society) he was also a member of the IEEE Technical Board Activities (TAB). He has been an Executive Committee member of NNC/NNS/CIS society for the past 16 years and an IEEE CIS Distinguished Lecturer since 2004.

DIRECTIONS: 

The church is located just west of the intersection of Rosendale/Vly Rd and Rt. 7, on the corner of Niskayuna Rd and Rt. 7. There is plenty of parking, accessible from either Rt. 7 or Niskayuna Rd. The meeting will be in the large white meeting hall next to the church. Enter through the large glass doors at the lower level. Please note that there is no smoking allowed anywhere on the church grounds.

Map to Niskayuna Reformed Church
NOTE: Valley Road = Vly Road

LUNCH: 
Lunch is provided.

RESERVATIONS:  
Please contact Lou Tomaino at schdyieee (at) yahoo.com by Thursday, October 22, 2009 at 9:00 AM to reserve. This event is free for IEEE members, $5.00 for non-members to help cover the cost of the room.

Please be courteous and cancel your reservation if you cannot attend so that we have a chance to adjust the food order and seating.  

Distinguished Lecturers Program Sponsored by the Computational Intelligence Society (CIS)