On the effectiveness of cognitive feedback from an interface agent

The objective of this study was to determine the impact of task information (TI) provided by an interface agent during the idea evaluation and integration step of the problem formulation stage of the problem solving process. The effectiveness assessment was based on solving diagnostic decision problems in the domain of complex industrial machinery. Ten domain experts participated in this study. Decision support was provided by a case-based reasoning system. Findings suggest that TI provided by the interface agent had no effect on the decision maker's performance, nor on the associated cognitive effort. However, a verbal protocol analysis revealed that the ten subjects used the interface agent to verify their decision processes. The results and their implications are discussed with respect to current findings in the area of decision support systems.decision support systems interface agents diagnostic case-based reasoning systems cognitive feedback

Rough set feature selection algorithms for textual case-based classification

Abstract. Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain) are computationally expensive. We previously showed that, on one dataset, a rough set feature selection algorithm can reduce computational complexity without sacrificing task performance. Here we test the generality of our findings on additional feature selection algorithms, add one data set, and improve our empirical methodology. We observed that features of textual cases vary in their contribution to task performance based on their part-of-speech, and adapted the algorithms to include a part-of-speech bias as background knowledge. Our evaluation shows that injecting this bias significantly increases task performance for rough set algorithms, and that one of these attained significantly higher classification accuracies than information gain. We also confirmed that, under some conditions, randomized training partitions can dramatically reduce training times for rough set algorithms without compromising task performance.