Recent controversy about the ease of constructing and reading graphical program representationsis of interest to us because of our work on graphical programming applications for training.We apply cognitive complexity analysis to graphical and textual programs, and confm the empirical findings of other researchers.We also apply cognitive complexity analysis to graphical programs from our own work. The analysis suggests that, when optimized for a speciilc task, both textual and graphical programs can carry the same information with similar cognitive complexity.The selection of graphical and textual representations for comparison in real-world training applications remains problematic.
Groups of robots are often able to to accomplish missions that no single robot can achieve by themselves. Teamwork is a very important factor in complex, dynamic domains. In heterogeneous teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. In such heterogeneous Multi-Robot Systems it is reasonable to explicitly take the robots' capabilities into account when determining which one is best suited for a task. In this paper I present a framework that formalizes robots' capabilities and provides a means to estimate their suitability for a task. In highly unpredictable domains, accurate predictions of the outcomes of a robot's actions are virtually impossible. Approximate models and algorithms are required which help to estimate the outcome with highest possible confidence. The proposed architecture can provide estimates of task solution qualities at three levels of confidence: the lowest level only taking the mere existence of capabilities into account, the middle level considering task-specific details with approximate parameters of the capabilities, and the highest confidence level considering more elaborate planning algorithms.
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