In a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded off. The GUI represents multiple objectives using the metaphor of an artist's palette. A distinct color is used to represent each objective, and tradeoffs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist's shade of color. We call the GUI an "Adverb Palette" where the word "Adverb" represents a specific type of objective for the path, such as the adverbs "quickly" and "safely" in the commands: "travel the path quickly", "make the journey safely". The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoff between different objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to define and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to find an acceptable solution even though they use very different representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a great deal of flexibility using the adverb palette.

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Query suggestions (QS) tailored specifically for children are slowly gaining research attention in response to the growth in Internet use by children. Even though QS offered by popular search engines adequately meet the information needs of the general public, they do not achieve equivalent effectiveness from a child’s perspective. This is because children’s search behaviors, interests, cognitive levels, and ability to read and understand complex content are different from adults. Given the ubiquitous nature of the Web, its importance in today’s society, and its increasing use in education, it is an urgent need to help children search the Web effectively. In this paper, we present a QS module, denoted CQS, which assists children in finding appropriate query keywords to capture their information needs by (i) analyzing content written for/by children, (ii) examining phrases and other metadata extracted from reputable (children’s) websites, and (iii) using a supervised learning approach to rank suggestions that are appealing to children. CQS offers suggestions with vocabulary that can be comprehended by children and with topics of interest to them. Empirical studies conducted using keyword queries initiated by children, in addition to feedback gathered through crowdsourcing, have verified not only the effectiveness of CQS, but also the fact that children favor CQSgenerated suggestions over the suggestions provided by Google, Yahoo!, and Bing