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As development of robots with the ability to self-assess their proficiency for accomplishing tasks continues to grow, metrics are needed to evaluate the characteristics and performance of these robot systems and their interactions with humans. This proficiency-based human-robot interaction (HRI) use case can occur before, during, or after the performance of a task. This article presents a set of metrics for this use case, driven by a four-stage cyclical interaction flow: (1) robot self-assessment of proficiency (RSA), (2) robot communication of proficiency to the human (RCP), (3) human understanding of proficiency (HUP), and (4) robot perception of the human’s intentions, values, and assessments (RPH). This effort leverages work from related fields including explainability, transparency, and introspection, by repurposing metrics under the context of proficiency self-assessment. Considerations for temporal level (a priori, in situ, and post hoc) on the metrics are reviewed, as are the connections between metrics within or across stages in the proficiency-based interaction flow. This article provides a common framework and language for metrics to enhance the development and measurement of HRI in the field of proficiency self-assessment.

Contemporary theories of metacognitive monitoring propose that beliefs play a critical role in monitoring of learning. Even so, recent evidence suggests that beliefs are not always sufficient to impact people's monitoring. In seven experiments, we explored people's beliefs about the impact of mood and item valence on memory and whether people use their beliefs about these cues when monitoring their learning. Participants expressed mood-congruent beliefs (Experiments 1, 6, and 7). That is, they believed people in a negative mood would remember more negative items than positive and neutral items. To evaluate whether they use this belief when monitoring their learning, participants studied emotional (positive and negative) and neutral pictures (Experiments 2 and 3) or words (Experiments 4, 5, and 7), made a judgment of learning (JOL) for each, and completed a free-recall test. In Experiments 2-5, participants completed the learning task while in a negative or neutral mood. The negative mood was induced with an established mood induction procedure. In contrast to the belief-based hypothesis, participants did not make mood-congruent JOLs; JOLs were not influenced by mood. By contrast, JOLs were consistently higher for emotional relative to neutral items. Thus, although participants demonstrated a mood-congruent belief, they did not use this belief when monitoring their learning. These outcomes demonstrate that simply having a belief about a person-centered cue (e.g., a belief about the impact of a person's mood on memory) is not sufficient for that belief to impact monitoring of learning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Current practice in Wilderness Search and Rescue (WiSAR) is analogous to an intelligent system designed to gather and analyze information to find missing persons in remote areas. The system consists of multiple parts - various tools for information management (maps, GPS, etc) distributed across personnel with different skills and responsibilities. Introducing a camera-equipped mini-UAV into this task requires autonomy and information technology that itself is an integrated intelligent system to be used by a sub-team that must be integrated into the overall intelligent system. In this paper, we identify key elements of the integration challenges along two dimensions: (a) attributes of intelligent system and (b) scale, meaning individual or group. We then present component technology that offload or supplement many responsibilities to autonomous systems, and finally describe how autonomy and information are integrated into user interfaces to better support distributed search across time and space. The integrated system was demoed for Utah County Search and Rescue personnel. A real searcher flew the UAV after minimal training and successfully located the simulated missing person in a wilderness area.

This paper defines a new three-dimensional (3D) Gaussian ray bundling model in geodetic coordinates: latitude, longitude, and altitude. Derivations are provided for 3D refraction, 3D interface reflection, 3D eigenray detection, and a 3D variant of the Comprehensive Acoustic System Simulation (CASS)/Gaussian Ray Bundling (GRAB) model. This approach allows environmental parameters and their derivatives are computed directly in latitude, longitude, and depth directions without reducing the problem to a series of [Formula: see text] Cartesian projections. Our model supports 3D effects such as great circle routes and horizontal refraction in sloped environments. Key test results are included for ray path refraction accuracy using a Munk profile, Gaussian beam projection into the shadow zone for an [Formula: see text] linear profile, and horizontal refraction from a 3D analytic wedge. Testing to date indicates that this approach has accuracy at least as good as CASS/GRAB, but with improved execution speed benefits for large numbers of targets, and 3D transmission loss effects.

In the 13th century, legislation dealing with sodomy and other moral crimes was strongly influences by the prevailing Catholic ideology. As a result, affenders were sought out by members of the lay confranternities, tried in the courts of the Inquisition, and generally punished with the death penalty.

Automated agents for electricity markets, social networks, and other distributed networks must repeatedly interact with other intelligent agents, often without observing associates' actions or payoffs (i.e., minimal information). Given this reality, our goal is to create algorithms that learn effectively in repeated games played with minimal information. As in other applications of machine learning, the success of a learning algorithm in repeated games depends on its learning bias. To better understand what learning biases are most successful, we analyze the learning biases of previously published multi-agent learning (MAL) algorithms. We then describe a new algorithm that adapts a successful learning bias from the literature to minimal information environments. Finally, we compare the performance of this algorithm with ten other algorithms in repeated games played with minimal information.

The recent interest in three dimensional graph drawing has been motivating studies on how to extend two dimensional techniques to higher dimensions. A common approach for computing a 2D orthogonal drawing ofa graph separates the task of defining the shape ofthe drawing from the task of computing its coordinates. First results towards finding a three-dimensional counterpart of this approach are presented in [8,9], where characterizations of orthogonal representations of paths and cycles are studied. In this note we show that the known characterization for cycles does not immediately extend to even seemingly simple graphs such as theta graphs. A sufficient condition for recognizing three-dimensional orthogonal representations oftheta graphs is also presented.