In this paper, we present and compare two-stage type-2 fuzzy logic advisor (FLA) for subjective decision making in the domain of students' performance evaluation. We test our proposed model for evaluating students' performance in our Computer Science and Engineering Department at HBCC/KFUPM in two domains namely cooperating training and capstone/senior project assessment where we find these FLAs very useful and promising. In our proposed model, the assessment criteria for different components of cooperative training and senior project are transformed into linguistic labels and evaluation information is extracted into the form of IF-THEN rules from the experts. These rules are modeled using FLS, which then is used as a fuzzy logic advisor (FLA) to make decisions about students' grades. The evaluator's input for the system can be either singleton or non-singleton. Both type-1 and type-2 fuzzy logic based models are implemented and compared with individual expert's evaluation.
Case-based planning (CBP) is a knowledge-based planning technique which develops new plans by reusing its past experience instead of planning from scratch. The task of CBP becomes difficult when the knowledge needed for planning can not be expressed precisely. In this paper, we tackle this issue by modeling imprecise information using fuzzy predicates; and accordingly, we present a dynamic similarity metric for efficient and effective retrieval of relevant cases from a library of cases. We also present weight adaptation algorithm to allow improving the performance of the metric overtime. We use and compare the performance of Tabu search, simulated annealing, and exhaustive search algorithms in instantiating fuzzy predicates to achieve maximum similarity between a new problem and a case. Our experiments show that the proposed metric is sound. The metric along with the adaptation algorithm have been shown to be promising when compared to others. Experiments also show that simulated annealing is more efficient than Tabu search and exhaustive search in fuzzy predicates instantiation.
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