With the proliferation of online labor markets and other social computing platforms, online experiments have become a low-cost and scalable way to empirically test hypotheses and mechanisms in both human computation and social science. Yet, despite the potential in designing more powerful and expressive online experiments using multiple subjects, researchers still face many technical and logistical difficulties. We see synchronous and longitudinal experiments involving real-time interaction between participants as a dual-use paradigm for both human computation and social science, and present TurkServer, a platform that facilitates these types of experiments on Amazon Mechanical Turk. Our work has the potential to make more fruitful online experiments accessible to researchers in many different fields.

Optimal solutions for multi-agent pathfinding problems are often too expensive to compute. For this reason, suboptimal approaches have been widely studied in the literature. Specifically, in recent years a number of efficient suboptimal algorithms that are complete for certain subclasses have been proposed at highly-rated robotics and AI conferences. However, it turns out that the problem of non-optimal multi-agent pathfinding has already been completely solved in another research community in the 1980s. In this paper, we would like to bring this earlier related work to the attention of the robotics and AI communities.

Several variants of A* have been recently proposed for find-ing optimal solutions for the multi-agent pathfinding (MAPF)problem. However, these variants have not been deeply com-pared either quantitatively or qualitatively. In this paper weaim to fill this gap. In addition to obtaining a deeper under-standing of the existing algorithms, we describe in detail theapplication of the new enhanced partial-expansion techniqueto MAPF and show how pattern databases can be applied ontop of this technique.

Linguistic communication relies on non-linguistic context toconvey meaning. That context might include, for instance, recent orlong-term experience, semantic knowledge of the world, or objects and events in the immediate environment. In this paper, we describe embodied agents instantiated in Soar cognitive architecture that use context derived from their linguistic, perceptual, procedural and semantic knowledge for comprehending imperative sentences.

Multi-agent path finding has been proven to be a PSPACE-hard problem. Generating such a centralised multi-agent plan can be avoided, by allowing agents to plan their paths separately. However, this results in an increased number of collisions and agents must re- plan frequently. In this paper we present a framework for multi-agent path planning, which allows agents to plan independently and solve conflicts locally when they occur. The framework is a generalisation of the CQ-learning algorithm which learns sparse interactions between agents in a multi-agent reinforcement learning setting

Robust methods for representing, generalizing, and sharing knowledge across various robotics systems and configurations are important in many domains of robotics research and application. In this paper we present a method for modeling tasks and robot skills to simplify the programming and reuse of knowledge between robots in manufacturing environments. Specifically, we propose an assembly taxonomy designed to represent the decomposition of high-level, complex assembly tasks into simple skills and skill primitives that the robot must use in a specified sequence. By using programming by demonstration to populate the taxonomy, we propose a method to easily interact with and reuse knowledge in various manufacturing robotics systems, making it possible to reduce programming time and overhead. We present both a detailed discussion of this taxonomy, as well as an example of how the taxonomy can be applied to an assembly task.

Collision avoidance and multi-agent navigation is an important component of modern video games. Recent developments in commodity hardware, in particular the utilization of multi-core and many-core architectures in personal computers and consoles are allowing large numbers of virtual agents to be incorporated into game levels in increasing numbers. We present the hybrid reciprocal velocity obstacle and optimal reciprocal collision avoidance methods for reciprocal collision avoidance and navigation in video games and described their implementations in C++ as HRVO Library and RVO2 Library. The libraries can efficiently simulate groups of twenty-five to one thousand virtual agents in dense conditions and around moving and static obstacles.

We discuss preliminary work focusing on the problem of combining social interaction with task-based action in a dynamic, multiagent bartending domain, using an embodied robot. We show how the users' spoken input is interpreted, discuss how social states are inferred from the parsed speech together with low-level information from the vision system, and present a planning approach that models task, dialogue, and social actions in a simple bartending scenario. This approach allows us to build interesting plans, which have been evaluated in a real-world study, using a general purpose, off-the-shelf planner, as an alternative to more mainstream methods of interaction management.

A general consensus on representation of emotions and feelings in cognitive architectures is currently missing; yet artificial emotional intelligence is vital for the integration of future robots into the human society. This work introduces one possible approach to representation and processing of emotional mental states and attitudes, that allows for implementation of control of agent behavior by emotions as well as for recognition of emotional motivations in another agent's behavior. One particular advantage of this approach is that it allows for representation and processing of complex/social emotional attitudes, like shame, jealousy, resentment, or humor. The proposed validation of the approach is based on simulation of the emergence of emotional relationships in a small group of agents in a virtual environment.

In the multi agent path finding problem (MAPF) paths shouldbe found for several agents, each with a different start andgoal position such that agents do not collide. Previous optimalsolvers applied global A*-based searches. We presenta new search algorithm called Conflict Based Search (CBS).CBS is a two-level algorithm. At the high level, a search isperformed on a tree based on conflicts between agents. At thelow level, a search is performed only for a single agent at atime. In many cases this reformulation enables CBS to examinefewer states than A* while still maintaining optimality.We analyze CBS and show its benefits and drawbacks. Experimentalresults on various problems shows a speedup ofup to a full order of magnitude over previous approaches.