Personality is essential to creating socially interactive robots. Prior work in Human-Robot Interaction (HRI) has focused on endowing robots with personality for the purpose of increasing the appeal of the robot and interaction to the user. In this paper, we propose the application of personality towards producing consistent and predictable behavior for robots. We provide an overview of personality and personality research in HRI, and describe a new research direction for using the Big Five personality types to enable robotic behavior that is consistent and easily predictable by human users.

Multi-agent pathfinding is a challenging combinatorial problem that involves multiple agents moving on a graph from a set of initial nodes to a set of desired goals without inter-agent collisions. Searching the composite space of all agents has exponential complexity and does not scale well. Decoupled methods are more efficient but are generally incomplete. There are, however, polynomial time algorithms, which utilize single or few-agents primitives with completeness guarantees. One limitation of these alternatives is that the resulting solution is sequential, where only one agent moves at a time. Such solutions are of low quality when compared to methods where multiple agents can move simultaneously. This work proposes an algorithm for multi-agent pathfinding that utilizes similar single-agent primitives but allows all agents to move in parallel. The paper describes the algorithm and its properties. Experimental comparisons suggest that the resulting paths are considerably better than sequential ones, even after a post-processing, parallelization step, as well as solutions returned by decoupled and coupled alternatives. The experiments also suggest good scalability and competitive computational performance.