Background: Science is experiencing a reproducibility crisis. Artificial intelligence research is not an exception. Objective: To give practical and pragmatic recommendations for how to document AI research so that the results are reproducible. Method: Our analysis of the literature shows that AI publications fall short of providing enough documentation to facilitate reproducibility. Our suggested best practices are based on a framework for reproducibility and recommendations given for other disciplines. Results: We have made an author checklist based on our investigation and provided examples for how every item in the checklist can be documented. Conclusion: We encourage reviewers to use the suggested best practices and author checklist when reviewing submissions for AAAI publications and future AAAI conferences.

In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list. Existing methods either seek a smooth convex surrogate for a non-smooth ranking metric or directly modify updating procedures to encourage top accuracy. In this work we point out that these methods do not scale well in a large-scale setting, and this is partly due to the inaccurate pointwise or pairwise rank estimation. We propose a new framework for personalized ranking. It uses batch-based rank estimators and smooth rank-sensitive loss functions. This new batch learning framework leads to more stable and accurate rank approximations compared to previous work. Moreover, it enables explicit use of parallel computation to speed up training. We conduct empirical evaluations on three item recommendation tasks, and our method shows a consistent accuracy improvement over current state-of-the-art methods. Additionally, we observe time efficiency advantages when data scale increases.

The goal of the Electric Elves project was to develop software agent technology to support human organizations. We developed a variety of applications of the Elves, including scheduling visitors, managing a research group (the Office Elves), and monitoring travel (the Travel Elves). The Travel Elves were eventually deployed at DARPA, where things did not go exactly as planned. In this article, we describe some of the things that went wrong and then present some of the lessons learned and new research that arose from our experience in building the Travel Elves.

In fact, DARPA, which funded the project, ways. Elves) (Scerri, Pynadath, and Tambe 2002; Finally, we will present some lessons Pynadath and Tambe 2003) and required learned and recent research that was motivated detailed information about the calendars by our experiences in deploying the of people using the system. Thus, we decided to deploy a new application of the Electric The Travel Elves introduced two major Elves, called the Travel Elves. This application advantages over traditional approaches to appeared to be ideal for wider deployment travel planning. First, the Travel Elves provided since it could be hosted entirely outside an interactive approach to making an organization and communication travel plans in which all of the data could be performed over wireless devices, required to make informed choices is such as cellular telephones. For example, when The mission of the Travel Elves (Ambite deciding whether to park at the airport or et al. 2002, Knoblock 2004) was to facilitate take a taxi, the system compares the cost planning a trip and to ensure that the of parking and the cost of a taxi given other resulting travel plan would execute selections, such as the airport, the specific smoothly. Initial deployment of the Travel parking lot, and the starting location Elves at DARPA went smoothly.