Explorations in joint human-machine cognitive systems
Cognition, computing, and cooperation
A co-operative computer based on the principles of human co-operation
International Journal of Man-Machine Studies - Special issue on knowledge-based co-operation
Meta-dialogue behaviors: improving the efficiency of human-machine dialogue
Meta-dialogue behaviors: improving the efficiency of human-machine dialogue
TRIPs: an integrated intelligent problem-solving assistant
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introspective multistrategy learning: on the construction of learning strategies
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Case-based introspective learning
ICCI '05 Proceedings of the Fourth IEEE International Conference on Cognitive Informatics
Interactive Evolutionary Multiobjective Optimization Using Robust Ordinal Regression
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Introspective learning to build case-based reasoning (CBR) knowledge containers
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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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 experts 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 expert involved in design (e.g. engineers, stakeholders, regulators, etc.), and do not necessarily incorporate the effect of the expert's own learning process on the suitability of the final design. The Interactive Genetic Algorithm with Mixed Initiative Interaction (IGAMII) is a novel approach that addresses these limitations by using a collaborative human-computer search strategy to assist users 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 expert 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 design problems.