CIS Virtual Seminar: Interactive Knowledge Discovery for Multi-Criteria Decision Support

#Multi-criteria #Decision-making; #Evolutionary #Algorithms; #Multi-objective #Optimization; #Knowledge #discovery

IEEE Computational Intelligence Society, Australian Capital Territory (ACT) chapter invites you to: 

Seminar: 12 October 2022, 4-5 pm AEDT (Canberra time)

Speaker: A/Prof Sunith Bandaru, University of Skövde,Sweden.

Title: Interactive Knowledge Discovery for Multi-Criteria Decision Support

Venue: Online (via Zoom) - link will be sent to the registered participants prior to the event.

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Abstract:  

Most industrial processes and systems involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While solving such a multi-objective optimization problem can itself be a challenging task, equally difficult is the ability to make sense of the generated solutions. Data mining and machine learning techniques can be used in this regard to extract different types of knowledge from these solutions. Such knowledge can be expected to provide valuable insights into the problem to the decision makers. Additionally, when used judiciously, such knowledge can also assist the optimization process in converging towards the Pareto front. 

This talk will first introduce knowledge discovery in the context of multi-objective optimization, along with relevant forms of explicit knowledge and available methods to extract them from solution sets. This will be followed by a demonstration of Mimer - a recently developed multi-criteria decision support tool for interactive knowledge discovery and knowledge visualization. The talk will conclude with some recent developments in knowledge-driven optimization and future work towards multi-criteria group decision making.

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Speaker biography: 

Sunith Bandaru is an Associate Professor (Docent) with the Division of Intelligent Production Systems in the School of Engineering Science at University of Skövde, Sweden. He received his Bachelor’s degree (2006) in Mechanical Engineering from Jawaharlal Nehru Technological University, India, and Master’s (2008) and Ph.D. (2013) degrees from IIT Kanpur, India.  Sunith’s research interests lie at the intersection of evolutionary computation, machine learning and data mining, where he has been developing methods for knowledge discovery from simulation-based multi-objective engineering optimization problems. His research profile can be found at: https://scholar.google.com/citations?user=LH5v498AAAAJ.

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