Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multi-player nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from Draftsim.com. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots.

Human-robot interaction involving the failure of autonomous robots is not yet well understood. We conducted two online surveys with a total of 1200 participants in which people assessed situations where an autonomous robot experienced different kinds of failure. This information was used to construct a measurement scale of people's reaction to failure where positive values correspond with increasingly positive reactions and negative values with negative reactions. We then used this scale to compare different kinds of failure situations, including the severity of the failures, the context risk involved, and the effectiveness of different kinds of recovery strategies. We found evidence that the effectiveness of recovery strategies depends on the task, context, and severity of failure.

A significant amount of research has been conducted regarding the technical aspects of haptic feedback. However, the design of effective haptic feedback behaviors for controlling ground-based mobile robots is not yet well understood from a human-robot interaction perspective. Past research of haptic feedback behaviors for mobile robots has sometimes made use of control paradigms that do not appropriately map to teleoperation or supervision tasks. Furthermore, evaluation of haptic behaviors has not been systematic and often only demonstrates feasibility. As a result, comparing various techniques is difficult. In this article, we focus on how haptic control research could be improved in the domain of teleoperation and supervision of ground-based mobile robots through the introduction of a haptic evaluation toolkit.

This video shows a demonstration of a fully autonomous robot, an iRobot ATRV-JR, which can be given commands using natural language. Users type commands to the robot on a tablet computer, which are then parsed and processed using semantic analysis. This information is used to build a plan representing the high level autonomous behaviors the robot should perform [2][1]. The robot can be given commands to be executed immediately (e.g., "Search the floor for hostages.") as well as standing orders for use over the entire run (e.g., "Let me know if you see any bombs.").

No abstract available.