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This study explores volunteer learning in an online community science program. Findings indicate alignment with self-directed and experiential learning theory, with implications for learner feedback and engagement. 

While the development of proactive personal assistants has been a popular topic within AI research, most research in this direction tends to focus on a small subset of possible interaction settings. An important setting that is often overlooked is one where the users may have an incomplete or incorrect understanding of the task. This could lead to the user following incorrect plans with potentially disastrous consequences. Supporting such settings requires agents that are able to detect when the user's actions might be leading them to a possibly undesirable state and, if they are, intervene so the user can correct their course of actions. For the former problem, we introduce a novel planning compilation that transforms the task of estimating the likelihood of task failures into a probabilistic goal recognition problem. This allows us to leverage the existing goal recognition techniques to detect the likelihood of failure. For the intervention problem, we use model search algorithms to detect the set of minimal model updates that could help users identify valid plans. These identified model updates become the basis for agent intervention. We further extend the proposed approach by developing methods for pre-emptive interventions, to prevent the users from performing actions that might result in eventual plan failure. We show how we can identify such intervention points by using an efficient approximation of the true intervention problems, which are best represented as a Partially Observable Markov Decision-Process (POMDP). To substantiate our claims and demonstrate the applicability of our methodology, we have conducted exhaustive evaluations across a diverse range of planning benchmarks. These tests have consistently shown the robustness and adaptability of our approach, further solidifying its potential utility in real-world applications. 

While the question of misspecified objectives has gotten much attention in recent years, most works in this area primarily focus on the challenges related to the complexity of the objective specification mechanism (for example, the use of reward functions). However, the complexity of the objective specification mechanism is just one of many reasons why the user may have misspecified their objective. A foundational cause for misspecification that is being overlooked by these works is the inherent asymmetry in human expectations about the agent's behavior and the behavior generated by the agent for the specified objective. To address this, we propose a novel formulation for the objective misspecification problem that builds on the human-aware planning literature, which was originally introduced to support explanation and explicable behavioral generation. Additionally, we propose a first-of-its-kind interactive algorithm that is capable of using information generated under incorrect beliefs about the agent to determine the true underlying goal of the user. 

We describe our methodology for classifying ASL (American Sign Language) gestures. Rather than operate directly on raw images of hand gestures, we extract coor-dinates and render wireframes from individual images to construct a curated training dataset. This dataset is then used in a classifier that is memory efficient and provides effective performance (94% accuracy). Because we con-struct wireframes that contain information about several angles in the joints that comprise hands, our methodolo-gy is amenable to training those interested in learning ASL by identifying targeted errors in their hand gestures.