The rapid development of science and technology has been accompanied by an exponential growth in peer-reviewed scientific publications. At the same time, the review of each paper is a laborious process that must be carried out by subject matter experts. Thus, providing high-quality reviews of this growing number of papers is a significant challenge. In this work, we ask the question “can we automate scientific reviewing? ”, discussing the possibility of using natural language processing (NLP) models to generate peer reviews for scientific papers. Because it is non-trivial to define what a “good” review is in the first place, we first discuss possible evaluation metrics that could be used to judge success in this task. We then focus on the machine learning domain and collect a dataset of papers in the domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers as input and generate reviews as output. Comprehensive experimental results on the test set show that while system-generated reviews are comprehensive, touching upon more aspects of the paper than human-written reviews, the generated texts are less constructive and less factual than human-written reviews for all aspects except the explanation of the core ideas of the papers, which are largely factually correct. Given these results, we pose eight challenges in the pursuit of a good review generation system together with potential solutions, which, hopefully, will inspire more future research in this direction. We make relevant resource publicly available for use by future research: https://github. com/neulab/ReviewAdvisor. In addition, while our conclusion is that the technology is not yet ready for use in high-stakes review settings we provide a system demo, ReviewAdvisor (http://review.nlpedia.ai/), showing the current capabilities and failings of state-of-the-art NLP models at this task (see demo screenshot in A.2). A review of this paper written by the system proposed in this paper can be found in A.1.

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavily affected by the resolution at which the search space is discretized. Typically a fixed resolution is chosen for a given domain. While a finer resolution allows better maneuverability, it exponentially increases the size of the state space, and hence demands more search efforts. On the contrary, a coarser resolution gives a fast exploratory behavior but compromises on maneuverability and the completeness of the search. To effectively leverage the advantages of both high and low resolution discretizations, we propose Multi-Resolution A* (MRA*) algorithm, that runs multiple weighted-A*(WA*) searches with different resolution levels simultaneously and combines the strengths of all of them. In addition to these searches, MRA* uses one anchor search to control expansions of other searches. We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete. We performed experiments on several motion planning domains including 2D, 3D grid planning and 7 DOF manipulation planning and compared our approach with several search-based and sampling-based baselines.

The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a number of empirical investigations. We reassess Hassan et al.'s 2018 investigation into Chinese to English news translation, showing that the finding of human–machine parity was owed to weaknesses in the evaluation design—which is currently considered best practice in the field. We show that the professional human translations contained significantly fewer errors, and that perceived quality in human evaluation depends on the choice of raters, the availability of linguistic context, and the creation of reference translations. Our results call for revisiting current best practices to assess strong machine translation systems in general and human–machine parity in particular, for which we offer a set of recommendations based on our empirical findings.

This paper proposes a method for learning and utilizing potentially useful interaction patterns among neighborhood-based heuristics. It is built upon a previously proposed framework designed for facilitating the task of combining multiple neighborhood-based heuristics. Basically, an algorithm derived from this framework will operate by chaining the heuristics in a pipelined fashion. Conceptually, we can view this framework as an algorithmic template that contains two user-defined components: 1) the policy H for selecting heuristics, and 2) the policy L for choosing the length of the pipeline that chains the selected heuristics. In this paper, we will develop a method that automatically derives a policy H by analyzing the experience collected from running a baseline algorithm. This analysis will distill potentially useful patterns of interactions among heuristics, and give an estimate for the frequency of using each pattern. The empirical results on three problem domains show the effectiveness of the proposed approach.

Technical and fundamental analysis are traditional tools used to analyze individual stocks; however, the finance literature has shown that the price movement of each individual stock correlates heavily with other stocks, especially those within the same sector. In this paper we propose a general-purpose market representation that incorporates fundamental and technical indicators and relationships between individual stocks. We treat the daily stock market as a ‘market image’ where rows (grouped by market sector) represent individual stocks and columns represent indicators. We apply a convolutional neural network over this market image to build market features in a hierarchical way. We use a recurrent neural network, with an attention mechanism over the market feature maps, to model temporal dynamics in the market. We show that our proposed model outperforms strong baselines in both short-term and long-term stock return prediction tasks. We also show another use for our market image: to construct concise and dense market embeddings suitable for downstream prediction tasks.

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

The AAAI 2017 Fall Symposium Series was held Thursday through Saturday, November 9–11, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, DC. The titles of the six symposia were Artificial Intelligence for Human-Robot Interaction; Cognitive Assistance in Government and Public Sector Applications; Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; Human-Agent Groups: Studies, Algorithms and Challenges; Natural Communication for Human-Robot Collaboration; and A Standard Model of the Mind. The highlights of each symposium (except the Natural Communication for Human-Robot Collaboration symposium, whose organizers did not submit a report) are presented in this report.

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.

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.