This is the essence of a planning problem, and one instance of planning problems of particular import is wargaming, which is a simulated military exercise to test strategies and operational plans in a controlled environment. Within the context of planning problems, it is easy to envision a variety of applications for AI, ranging from decision support systems (DSS), intelligent opponents, scenario generation, and so on. This work will focus primarily on agents usable for the first two applications: DSS and intelligent opponents. As a result, we seek not only agents that can select a good quality action, but also agents that can analyze multi-player interactions, adapt rapidly to changing conditions, and provide interpretable insights. The U.S. Department of Defense introduced Battlespace [5] as a platform for wargaming [6].

Peer review is a widely accepted mechanism for research evaluation, playing a pivotal role in scholarly publishing. However, criticisms have long been leveled on this mechanism, mostly because of its inefficiency and subjectivity. Recent years have seen the application of artificial intelligence (AI) in assisting the peer review process. Nonetheless, with the involvement of humans, such limitations remain inevitable. In this review paper, we propose the concept of automated scholarly paper review (ASPR) and review the relevant literature and technologies to discuss the possibility of achieving a full-scale computerized review process. We further look into the challenges in ASPR with the existing technologies. On the basis of the review and discussion, we conclude that there are already corresponding research and technologies at each stage of ASPR. This verifies that ASPR can be realized in the long term as the relevant technologies continue to develop. The major difficulties in its realization lie in imperfect document parsing and representation, inadequate data, defected human-computer interaction and flawed deep logical reasoning. In the foreseeable future, ASPR and peer review will coexist in a reinforcing manner before ASPR is able to fully undertake the reviewing workload from humans.

ArXiv, an open-access digital repository of scholarly articles maintained by Cornell University in New York, made available all of its 1.7 million research articles on Kaggle, a public online platform for machine learning training datasets. For each article, the dataset includes information such as the author, article title, category, abstract, citations, as well as a link to the full-text PDF. Researchers can more easily use the data from arXiv articles to perform trend analysis, create algorithms that group scholarly papers by topic, and improve search engines for scholarly papers. Cassidy Chansirik is an intern at the Center for Data Innovation. Currently, she is a student at the University of California, Berkeley and is pursuing a B.A. in Legal Studies and a minor in Education.