Event Detail

Event Type: 
Applied Mathematics and Computation Seminar
Friday, May 9, 2014 - 05:00 to 06:00
GLK 115

Speaker Info

OSU School of Civil and Construction Enrgrg

Design of optimal plans for environmental planning and management applications should ideally consider the multiple quantitative and qualitative criteria relevant to the problem. For example, in ground water monitoring design problems, qualitative criteria such as acceptable spatial extent and shape of the contaminant plume predicted from the monitored locations can be equally important as the typical quantitative criteria such as economic costs and contaminant prediction accuracy. Incorporation of qualitative criteria in the problem-solving process is typically done in one of two ways: (a) quantifying approximate representations of the qualitative criteria, which are then used as additional criteria during the optimization process, or (b) post-optimization analysis of designs by decision makers to evaluate the overall performance of the optimized designs with respect to the qualitative criteria. These approaches, however, may not adequately represent all of the relevant qualitative information that affect a human involved in design (e.g., engineers, stakeholders, regulators, etc), and do not necessarily incorporate the effect of the human's own learning process on the suitability of the final design. In this presentation, I will present novel approaches for human-centered optimization approaches that addresses these limitations by using a collaborative human-computer search strategy to assist humans in designing optimized solutions to their applications, while also learning about their problem. The algorithm adaptively learns from the expert’s feedback, and explores multiple designs that meet her/his criteria using both the human expert and a simulated model of the expert’s responses in a collaborative fashion. The algorithm provides an introspection-based learning framework for the human and uses the human's subjective confidence measures to adjust the optimization search process to the transient learning process of the user. This paper presents the design and testing of this computational framework, and the benefits of using this approach for solving groundwater monitoring and watershed restoration design problems.