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Trigger-action programming (TAP) empowers a wide array of users to automate Internet of Things (IoT) devices. However, it can be challenging for users to create completely correct trigger-action programs (TAPs) on the first try, necessitating debugging. While TAP has received substantial research attention, TAP debugging has not. In this paper, we present the first empirical study of users’ end-to-end TAP debugging process, focusing on obstacles users face in debugging TAPs and how well users ultimately fix incorrect automations. To enable this study, we added TAP capabilities to an existing 3-D smart home simulator. Thirty remote participants spent a total of 84 hours debugging TAPs using this simulator. Without additional support, participants were often unable to fix buggy TAPs due to a series of obstacles we document. However, we also found that two novel tools we developed helped participants overcome many of these obstacles and more successfully debug TAPs. These tools collect either implicit or explicit feedback from users about automations that should or should not have happened in the past, using a SAT-solving-based algorithm we developed to automatically modify the TAPs to account for this feedback. 

In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objectives.Despite these advancements, there are fundamental limitations to how well this problem can be solved. Previous studies have alluded to this fact but have not examined it in depth.In this paper, we address the tractability of reinforcement learning for general LTL objectives from a theoretical perspective.We formalize the problem under the probably approximately correct learning in Markov decision processes (PAC-MDP) framework, a standard framework for measuring sample complexity in reinforcement learning.In this formalization, we prove that the optimal policy for any LTL formula is PAC-MDP-learnable if and only if the formula is in the most limited class in the LTL hierarchy, consisting of formulas that are decidable within a finite horizon.Practically, our result implies that it is impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee on the performance of its learned policy after finitely many interactions with an unconstrained environment for LTL objectives that are not decidable within a finite horizon.

 

Reinforcement learning (RL) can help agents learn complex tasks that would be hard to specify using standard imperative programming. However, end users may have trouble personalizing their technology using RL due to a lack of technical expertise. Prior work has explored means of supporting end users after a problem for the RL agent to solve has been defined. Little work, however, has explored how to support end users when defining this problem. We propose a tool to provide structured support for end users defining problems for RL agents. Through this tool, users can (i) directly and indirectly specify the problem as a Markov decision process (MDP); (ii) receive automatic suggestions on possible MDP changes that would enhance training time and accuracy; and (iii) revise the MDP after training the agent to solve it. We believe this work will help reduce barriers to using RL and contribute to the existing literature on designing human-in-the-loop systems. 

Experiences discovering attempts to subvert the peer-review process. 

Trigger-action programming (if-this-then-that rules) empowers non-technical users to automate services and smart devices. As a user's set of trigger-action programs evolves, the user must reason about behavior differences between similar programs, such as between an original program and several modification candidates, to select programs that meet their goals. To facilitate this process, we co-designed user interfaces and underlying algorithms to highlight differences between trigger-action programs. Our novel approaches leverage formal methods to efficiently identify and visualize differences in program outcomes or abstract properties. We also implemented a traditional interface that shows only syntax differences in the rules themselves. In a between-subjects online experiment with 107 participants, the novel interfaces better enabled participants to select trigger-action programs matching intended goals in complex, yet realistic, situations that proved very difficult when using traditional interfaces showing syntax differences. 

Modeling student knowledge is important for assessment design, adaptive testing, curriculum design, and pedagogical intervention. The assessment design community has primarily focused on continuous latent-skill models with strong conditional independence assumptions among knowledge items, while the prerequisite discovery community has developed many models that aim to exploit the interdependence of discrete knowledge items. This paper attempts to bridge the gap by asking, "When does modeling assessment item interdependence improve predictive accuracy?" A novel adaptive testing evaluation framework is introduced that is amenable to techniques from both communities, and an efficient algorithm, Directed Item-Dependence And Confidence Thresholds (DIDACT), is introduced and compared with an Item-Response-Theory based model on several real and synthetic datasets. Experiments suggest that assessments with closely related questions benefit significantly from modeling item interdependence. 

Two common approaches for automating IoT smart spaces are having users write rules using trigger-action programming (TAP) or training machine learning models based on observed actions. In this paper, we unite these approaches. We introduce and evaluate Trace2TAP, a novel method for automatically synthesizing TAP rules from traces (time-stamped logs of sensor readings and manual actuations of devices). We present a novel algorithm that uses symbolic reasoning and SAT-solving to synthesize TAP rules from traces. Compared to prior approaches, our algorithm synthesizes generalizable rules more comprehensively and fully handles nuances like out-of-order events. Trace2TAP also iteratively proposes modified TAP rules when users manually revert automations. We implemented our approach on Samsung SmartThings. Through formative deployments in ten offices, we developed a clustering/ranking system and visualization interface to intelligibly present the synthesized rules to users. We evaluated Trace2TAP through a field study in seven additional offices. Participants frequently selected rules ranked highly by our clustering/ranking system. Participants varied in their automation priorities, and they sometimes chose rules that would seem less desirable by traditional metrics like precision and recall. Trace2TAP supports these differing priorities by comprehensively synthesizing TAP rules and bringing humans into the loop during automation.