The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, presented the 2018 Spring Symposium Series, held Monday through Wednesday, March 26–28, 2018, on the campus of Stanford University. The seven symposia held were AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents; Artificial Intelligence for the Internet of Everything; Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-Being AI; Data Efficient Reinforcement Learning; The Design of the User Experience for Artificial Intelligence (the UX of AI); Integrated Representation, Reasoning, and Learning in Robotics; Learning, Inference, and Control of Multi-Agent Systems. This report, compiled from organizers of the symposia, summarizes the research of five of the symposia that took place.

The Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31) was held May 21-23, 2018, at the Crowne Plaza Oceanfront in Melbourne, Florida, USA. The conference events included invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Zdravko Markov from Central Connecticut State University. The program co-chairs were Vasile Rus from the University of Memphis and Keith Brawner from the Army Research Laboratory. The special tracks were coordinated by Roman Barták from Charles University in Prague.

The accompanying video captures the multi-modal data displays and speech dialogue of a human Commander (C) and a human Robot Navigator (RN) tele-operating a mobile robot (R) in a remote, previously unexplored area. We describe unique challenges for automation of turn-taking and coordination processes observed in the data.

We seek to develop a robot that will be capable of teaming with humans to accomplish physical exploration tasks that would not otherwise be possible in dynamic, dangerous environments. For such tasks, a human commander needs to be able to communicate with a robot that moves out of sight and relays information back to the commander. What is the best way to determine how a human commander would interact in a multi-modal spoken dialog with such a robot to accomplish tasks? In this paper, we describe our initial approach to discovering a principled basis for coordinating turn-taking, perception, and navigational behavior of a robot in communication with a commander, by identifying decision phases in dialogs collected in a WoZ framework. We present two types of utterance annotation with examples applied to task-oriented dialog between a human commander and a human ``robot navigator'' who controls the physical robot in a realistic environment similar to expected actual conditions. We discuss core robot capabilities that bear on the robot navigator's ability to take turns while performing a ``find the building doors'' task at hand. The paper concludes with a brief overview of ongoing work to implement these decision phases within an open-source dialog management framework, constructing a task tree specification and dialog control logic for our application domain.