We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework ...

Collaborative filtering is a popular technique to infer users’ preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recommender system, and these loops iteratively influence the collaborative filter...

BACKGROUND Advance care planning is the process of establishing a patient's goals and preferences for future care. Previous research has demonstrated a need to improve patient-physician communication around advance care planning. A critical time for advance care planning conversations is the day of admission to the hospital. METHODS A survey of internal medicine residents was administered at ...

Interpersonal cognitive consistency is a driving force in group behavior. In this article, we propose a new model of interpersonal cognitive consistency in collective decision making. Building on ideas from the mutual enhancement model (Wittenbaum, Hubbell, & Zuckerman, 1999), we argue that group members evaluate one another more positively when they mention information confirming each other's ...

corrective feedback (cf) and its different types have long absorbed many scholars and practitioners. as ellis (2009) mentioned some experimental studies need to be carefully designed to discover the relative effectiveness of each of these cf techniques. the goal of this qualitative study was to discover whether the employment of different cf strategies could bring about an attitudinal shift. to...

In todays videogames user feedback is often provided through raw statistics and scoreboards. We envision that incorporating empathic feedback matching the player’s current mood will improve the overall gaming experience. In this paper we present Bro-cam, a novel system that provides empathic feedback to the player based on their body postures. Different body postures of the players are used as ...

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is often difficult to collect it is important to develop effective models that take...

Preference elicitation is important for any computerized system advising users about choices. Recommender systems aim to propose interesting material to users. Therefore, they must first gather user preferences. Negotiation support systems can only give meaningful bidding advice based on users’ preferences regarding negotiable issues and interests. In general, the more detail users are willing ...

We discuss the problem of learning to rank labels from a real valued feedback associated with each label. We cast the feedback as a preferences graph where the nodes of the graph are the labels and edges express preferences over labels. We tackle the learning problem by defining a loss function for comparing a predicted graph with a feedback graph. This loss is materialized by decomposing the f...

The present study examined the linkage between pre-adolescent children's depressive symptoms and their preferences for receiving positive vs. negative feedback subsequent to being faced with an experimentally manipulated peer evaluation outcome in real time. Participants (n = 142) ages 10 to 13, played a computer contest based on the television show Survivor and were randomized to either a peer...