If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Moderators are believed to play a crucial role in ensuring the quality of discussion in online political debate forums. The line between moderation and illegitimate censorship, where certain views or individuals are unfairly suppressed, however, is often difficult to define. To better understand the relationship between moderation and censorship, we investigate whether users' perception of moderator bias is supported by how moderators act, using the Big Issues Debate (BID) group on Ravelry as our platform of study. We present our method for measuring bias while taking into account the posting behavior of a user, then apply our method to investigate whether moderators make decisions biased against viewpoints that they may have the incentive to suppress. We find evidence to suggest that while moderators may make decisions biased against individuals with unpopular viewpoints, the effect of this bias is small and often overblown by the users experiencing bias.We argue that the perception of bias by itself is an issue in online political discussions and suggest technological interventions to counteract the discrepancy between perceived and actual censorship in moderation.
Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search algorithms that generalizes LWA* and LazySP. We analyze the theoretic properties of LRA* and demonstrate empirically that, in many cases, to minimize the total planning time, the algorithm requires an intermediate lazy lookahead. Namely, using an intermediate lazy lookahead, our algorithm outperforms both LWA* and LazySP. These experiments span simulated random worlds in R^2 and R^4, and manipulation problems using a 7-DOF manipulator.
Haghtalab, Nika (Carnegie Mellon University) | Mackenzie, Simon (Carnegie Mellon University) | Procaccia, Ariel D. (Carnegie Mellon University) | Salzman, Oren (Carnegie Mellon University) | Srinivasa, Siddhartha S. (Carnegie Mellon University)
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along candidate shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case.
Raghunathan, Arvind U. (Mitsubishi Electric Research Laboratories) | Bergman, David (University of Connecticut) | Hooker, John (Carnegie Mellon University) | Serra, Thiago (Carnegie Mellon University) | Kobori, Shingo (Mitsubishi Electric Corporation)
Last-mile transportation (LMT) refers to any service that moves passengers from a hub of mass transportation (MT), such as air, boat, bus, or train, to destinations, such as a home or an office. In this paper, we introduce the problem of scheduling passengers jointly on MT and LMT services, with passengers sharing a car, van, or autonomous pod of limited capacity for LMT. Passenger itineraries are determined so as to minimize total transit time for all passengers, with each passenger arriving at the destination within a specified time window. The transit time includes the time spent traveling through both services and, possibly, waiting time for transferring between the services. We provide an integer linear programming (ILP) formulation for this problem. Since the ILMTP, is NP-hard and problem instances of practical size are often difficult to solve, we study a restricted version where MT trips are uniform, all passengers have time windows of a common size, and LMT vehicles visit one destination per trip. We prove that there is an optimal solution that sorts and groups passengers by their deadlines and, based on this result, we propose a constructive grouping heuristic and local search operators to generate high-quality solutions. The resulting groups are optimally scheduled in a few seconds using another ILP formulation. Numerical results indicate that the solutions obtained by this heuristic are often close to optimal %, even when multiple destinations are allowed per group, and that warm-starting the ILP solver with such solutions decreases the overall computational times significantly.
Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot’s feet—a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach.
We identify and classify users’ self-narration of racial discrimination and corresponding community support in social media. We developed natural language models first to distinguish self-narration of racial discrimination in Reddit threads, and then to identify which types of support are provided and valued in subsequent replies. Our classifiers can detect the self-narration of personally experienced racism in online textual accounts with 83% accuracy and can recognize four types of supportive actions in replies with up to 88% accuracy. Descriptively, our models identify types of racism experienced and the racist concepts (e.g., sexism, appearance or accent related) most experienced by people of different races. Finally, we show that commiseration is the most valued form of social support.
We present a mixed methods study of the online forum r/RoastMe, a comedy-focused subreddit of the parent site reddit.com, wherein members post photos of themselves to be ridiculed by other members; the site generally encourages harsh and offensive forms of humor in these interpersonal exchanges. We conducted semi-structured interviews with sixteen participants (both "roasters" and "roastees") in the online forum to understand their motivations for participating, their experiences in the subreddit, and their perceptions of their and other members' participation. To complement our qualitative analyses, we also analyzed a RoastMe data set of over 9,000 image posts and 230,000 comments from June-August of 2017. From our interviews, we found that, like other deviant online communities, RoastMe relies on a specific set of norms. In RoastMe, roasters rely heavily on perspective-taking rather than dissociation from their targets, roastees highly value the often scathing assessments offered by users on RoastMe, and, despite the salience of norms that enhance feelings of safety, there is lingering concern among participants about the potential for emotional or psychological harm. Our quantitative analyses confirm many of the statements made in our qualitative interviews and provide further insights into the specific nature of interactions on the subreddit. Our study directs us toward different vantage points from which to design online community spaces that account for or leverage users' predilections for baiting behaviors, harsh judgments, and caustic humor.
Yu, Tong (Carnegie Mellon University) | Pan, Shijia (Carnegie Mellon University) | Xu, Susu (Carnegie Mellon University) | Chen, Xinlei (Carnegie Mellon University) | Mirshekari, Mostafa (Carnegie Mellon University) | Fagert, Jonathon (Carnegie Mellon University) | Noh, Hae Young (Carnegie Mellon University) | Zhang, Pei (Carnegie Mellon University) | Mengshoel, Ole J. (Carnegie Mellon University)
In this paper, we present Iterative Learning using Physical Constraints (ILPC) method. ILPC is an iterative learning method targeting at model inaccuracy caused by a distribution change in training and test data. This change in distribution can be due to the complexity of many real-world physical systems. Although domain adaptation methods, which consider both training and test data distribution when building models, also target this distribution change, these methods can only handle a limited difference between training and test data. ILPC handles different distributions based on a key observation: gradual changes in physical conditions often cause gradual data distribution changes. Instead of treating test data as generated by an identical distribution, ILPC builds a model iteratively, guided by a system's physical measurements. In each iteration, the model is only extended with data that has similar physical measurements to the last iteration. This approach leads to higher accuracy. To evaluate ILPC, we apply it to two real-world datasets and achieve up to a 2.7x improvement in prediction accuracy compared to existing domain adaptation methods.
Stackelberg equilibria have become increasingly important as a solution concept in computational game theory, largely inspired by practical problems such as security settings. In practice, however, there is typically uncertainty regarding the model about the opponent. This paper is, to our knowledge, the first to investigate Stackelberg equilibria under uncertainty in extensive-form games, one of the broadest classes of game. We introduce robust Stackelberg equilibria, where the uncertainty is about the opponent’s payoffs, as well as ones where the opponent has limited lookahead and the uncertainty is about the opponent’s node evaluation function. We develop a new mixed-integer program for the deterministic limited-lookahead setting. We then extend the program to the robust setting for Stackelberg equilibrium under unlimited and under limited lookahead by the opponent. We show that for the specific case of interval uncertainty about the opponent’s payoffs (or about the opponent’s node evaluations in the case of limited lookahead), robust Stackelberg equilibria can be computed with a mixed-integer program that is of the same asymptotic size as that for the deterministic setting.
Consumers often rely on product reviews to make purchase decisions, but how consumers use review content in their decision making has remained a black box. In the past, extracting information from product reviews has been a labor-intensive process that has restricted studies on this topic to single product categories or those limited to summary statistics such as volume, valence, and ratings. This paper uses deep learning natural language processing techniques to overcome the limitations of manual information extraction and shed light into the black box of how consumers use review content. With the help of a comprehensive dataset that tracks individual-level review reading, search, as well as purchase behaviors on an e-commerce portal, we extract six quality and price content dimensions from over 500,000 reviews, covering nearly 600 product categories. The scale, scope, and precision of such a study would have been impractical using human coders or classical machine learning models. We achieve two objectives. First, we describe consumers’ review content reading behaviors. We find that although consumers do not read review content all the time, they do rely on review content for products that are expensive or of uncertain quality. Second, we quantify the causal impact of content information of read reviews on sales. We use a regression discontinuity in time design and leverage the variation in the review content seen by consumers due to newly added reviews. To extract content information, we develop two deep learning models: a full deep learning model that predicts conversion directly and a partial deep learning model that identifies content dimensions. Across both models, we find that aesthetics and price content in the reviews significantly affect conversion across almost all product categories. Review content information has a higher impact on sales when the average rating is higher and the variance of ratings is lower. Consumers depend more on review content when the market is more competitive or immature. A counterfactual simulation suggests that re-ordering reviews based on content can have the same effect as a 1.6% price cut for boosting conversion.