Previous STRIPS domain model acquisition approaches that learn from state traces start with the names and parameters of the actions to be learned. Therefore their only task is to deduce the preconditions and effects of the given actions. In this work, we explore learning in situations when the parameters of learned actions are not provided. We define two levels of trace quality based on which information is provided and present an algorithm for each. In one level (L1), the states in the traces are labeled with action names, so we can deduce the number and names of the actions, but we still need to work out the number and types of parameters. In the other level (L2), the states are additionally labeled with objects that constitute the parameters of the corresponding grounded actions. Here we still need to deduce the types of the parameters in the learned actions. We experimentally evaluate the proposed algorithms and compare them with the state-of-the-art learning tool FAMA on a large collection of IPC benchmarks. The evaluation shows that our new algorithms are faster, can handle larger inputs and provide better results in terms of learning action models more similar to reference models.

Multi-Agent Path Finding (MAPF) deals with the problem of finding collision-free paths for a set of agents, where each agent wants to move from its start location to its goal location on a shared graph. The paper addresses the question of how to model MAPF as a classical planning problem, specifically, how to encode various collision constraints. Several models in the PDDL modeling language are proposed and empirically compared.

The FLAIRS poster track is designed to promote discussion of emerging ideas and work in order to encourage and help guide researchers — especially new researchers — who are able to present a full poster in the conference poster session and receive that critical work-shaping feedback that helps guide good work into great work. Abstracts of those posters appear here, which we hope to see fully developed into future FLAIRS papers..

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.

Reports of the Workshops of the Thirty-First AAAI Conference on Artificial Intelligence

The AAAI-17 workshop program included 17 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 4-5, 2017 at the Hilton San Francisco Union Square in San Francisco, California, USA. This report contains summaries of 12 of the workshops, and brief abstracts of the remaining 5

The aim of the special track is to bring researchers from related but still separated areas of robotics and artificial intelligence together to study how high-level AI techniques such as action planning and knowledge representation may help in increasing capabilities of robotic systems.