Measuring how well a potential solution to a problem matches the problem-holder's intent and detecting when a current solution no longer matches intent is important when designing resilient human-robot teams. This paper addresses intent-matching for a robot path-planning problem that includes multiple objectives and where human intent is represented as a vector in the multi-objective payoff space. The paper introduces a new metric called the intent threshold margin and shows that it can be used to rank paths by how close they match a specified intent. The rankings induced by the metric correlate with average human rankings (obtained in an MTurk study) of how closely different paths match a specified intent. The intuition of the intent threshold margin is that it represents how much the human's intent must be "relaxed" to match the payoffs for a specified path.

For goal-based robot navigation in a dynamic environment, human intent includes expectations about what performance objectives are satisfied by a planned path in terms of objectives to be met. If the planned path drifts from the human's intent as a result of environment changes, the path needs to be replanned. This paper presents a replanning framework with three elements: (a) the integration of fast online path-planning algorithms that generate trajectories conforming to the given intent; (b) a mathematical model that says when replanning must happen; and (c) an evaluation of events that trigger replanning. An interactive graphical user interface enables a human to accept or reject replanned paths when a trigger happens. A study of 50 MTurk participants is used to assess what replanning triggers best enable a human-robot collaboration to persistently satisfy intent?

An important part of expressing human intent is identifying acceptable tradeoffs among competing performance objectives. We present and evaluate a set of graphical user interfaces (GUIs), that are designed to allow a human to express intent by expressing desirable tradeoffs. The GUIs require an algorithm that identifies the set of Pareto optimal solutions to the multi-objective decision problem, which means that all the solutions are equally good in the sense that there are no other solutions better for every objective. Given the Pareto set, the GUIs provide different ways for a human to express intent by exploring tradeoffs between objectives; once a tradeoff is selected, the solution is chosen. The GUI designs are applied to interactive human-robot path-selection for a robot in an urban environment, but they can be applied to other tradeoff problems. A user study evaluates GUI designs by requiring users to select a tradeoff that satisfies a specified mission intent. Results of the user study suggest that GUIs designed to support an artist's palette-metaphor can be used to express intent without incurring unacceptable levels of human workload.

Many fields of study involve decision-making where trade-offs between different desirable outcomes are to be made. This paper introduces four designs for a graphical user interface (GUI) called the Adverb Palette (AP) to support interactive robot path-planning under multiple performance objectives. The GUI designs apply when the robot must go from an initial configuration to a goal configuration while honoring competing objectives. This novel human-robot interface helps the operator issue commands to the robot to take a specific path from the many available paths, thereby helping the user in decision making process. AP is a metaphor that symbolizes an artist's palette where an artist mixes different colors to get a desired shade of color. For the principal GUI designs, each objective is represented as a color. Since we assume that the human specifies the objectives using language, each objective is metaphorically called an adverb. Two of the approaches, the sliders and the waypoints indicator, have been explored by the researchers in the past, and the other two, palette and prism are novel and may be more useful and intuitive.

Commercial web search engines include a query suggestion module so that given a user's keyword query, alternative suggestions are offered and served as a guide to assist the user in formulating queries which capture his/her intended information need in a quick and simple manner. Majorityof these modules, however, perform an in-depth analysis oflarge query logs and thus (i) their suggestions are mostlybased on queries frequently posted by users and (ii) theirdesign methodologies cannot be applied to make suggestions oncustomized search applications for enterprises for which theirrespective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, aprobabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, sinceit solely relies on the availability of user-generated contentfreely accessible online, such as the Wikipedia.org documentcollection, and applies simple, yet effective, probabilistic-andinformation retrieval-based models, i.e., the Multinomial, BigramLanguage, and Vector Space Models, to provide usefuland diverse query suggestions. Empirical studies conductedusing a set of test queries and the feedbacks provided byMechanical Turk appraisers have verified that PQS makesmore useful suggestions than Yahoo! and is almost as goodas Google and Bing based on the relatively small difference inperformance measures achieved by Google and Bing over PQS.

Digital image processing in the field of time-lapse microscopy and biological research has been an interesting research area to provide statistical data of cellular dynamics to the field of cell biology. Digital image processing enables rapid and consistent quantification of qualitative observations. The image processing model examined here provides a tool for the biologists to identify structures called retraction fibers (RF) that are formed during epithelial-mesenchyme transition (EMT), an important developmental process which also occurs during cancer metastasis. Quantifying RF formation is an important tool for biologists studying cellular regulation of EMT. The dynamic EMT process is captured using time-lapse microscopy. We use computer vision algorithms to detect and track the RF in image sequences of cells undergoing EMT to generate statistical information such as the number of RF formed during a window, lifetime of the RF, and their geometric dimension. This information can in turn be used by the biologist to quantitatively measure the extent of EMT under different test conditions.