One common use for swarming agents is in social simulation. This paper reports on such a model developed to track protest activities at the May 2012 NATO summit in Chicago. The use of apoptotic stigmergic agents allows the model to run on-line, consuming two kinds of external data and reporting its results in real time.

Robots are increasingly introduced to work in concert with people in high-intensity domains, such as manufacturing, space exploration and hazardous environments. Although there are numerous studies on human teamwork and coordination in these settings, very little prior work exists on applying these models to human-robot interaction. This paper presents results from ongoing work aimed at translating qualitative methods from human factors engineering into computational models that can be applied to human-robot teaming. We describe a statistical approach to learning patterns of strong and weak agreements in human planning meetings that achieves up to 94% prediction accuracy. We also formulate a human-robot interactive planning method that emulates cross-training, a training strategy widely used in human teams. Results from human subject experiments show statistically significant improvements on team fluency metrics, compared to standard reinforcement learning techniques. Results from these two studies support the approach of modeling and applying common practices in human teaming to achieve more effective and fluent human-robot teaming.

Human interaction with robot swarms (HSI) is a young field with very few user studies that explore operator behavior. All these studies assume perfect communication between the operator and the swarm. A key challenge in the use of swarm robotic systems in human supervised tasks is to understand human swarm interaction in the presence of limited communication bandwidth, which is a constraint arising in many practical scenarios. In this paper, we present results of human-subject experiments designed to study the effect of bandwidth limitations in human swarm interaction. We consider three levels of bandwidth availability in a swarm foraging task. The lowest bandwidth condition performs poorly, but the medium and high bandwidth condition both perform well. In the medium bandwidth condition, we display useful aggregated swarm information (like swarm centroid and spread) to compress the swarm state information. We also observe interesting operator behavior and adaptation of operators’ swarm reaction.

This paper provides a comparison between alternate styles and tecnhiques for controlling many subordinate agents: delegation vs. swarm "control" or influence. Each management style is defined and pros and cons articulated. The author then attempts to apply a model he created in prior work of the "tradeoff space" of automation control approaches along three dimensions: competence, workload and unpredictability. This application offers insights about the strengths and weaknesses of each approach, but also points to a limitation in the characterization of the tradeoff space.

AntBeePath is a hybrid bio-inspired algorithm based on the behavior of ants and honeybees aimed at the resolution of the problem of finding the shortest paths for a given network topology. The algorithm, in brief, combines the pheromone release mechanism of existing Ant Colony Optimization (ACO) algorithms with a new bio-inspired mechanism based on the recruitment strategy of bees. Three versions of the algorithm were developed incrementally. Proof-of-concept results indicate that the AntBeePath Decay Hybrid Chain version is more efficient than the other developed versions and, beyond that, presented an improved performance in relation to an equivalent ACO algorithm. The results suggest that a hybrid algorithm, combining the ant’s pheromone release with the new bio-inspired mechanism of bee recruitment along with a stagnation control mechanism can result in a new bio-inspired algorithm for path determination with improved characteristics.

In this position paper, we synthesize "within the system" models of human influence over bio-inspired swarms, summarizing observations from previous experiments and identifying methods of influence that have not yet been explored. We describe (a) differences among agents that can be controlled by a human and those that can't, (b) agents that are aware of the type of other agents and those that aren't, and (c) the effects of attraction, repulsion, and orientation on human-guided swarm behavior. We also briefly discuss the interaction effort required to manage swarms.

In this paper, we investigate what role the network topology plays when controlling a network of mobile robots. This is a question of key importance in the emerging area of human-swarm interaction and we approach this question by letting a human user inject control signals at a single leader-node, which are then propagated throughout the network. Based on a user study, it is found that some topologies are more amenable to human control than others, which can be interpreted in terms of the rank of the controllability matrix of the underlying network dynamics, as well as, measures of node centrality on the leader of the network.

Human control of vehicle swarms faces a dilemma: an operator must be able to exercise precise control over how a mission is executed, but controlling individual vehicles is not scalable. The Proto spatial computing lan- guage offers an intermediate representation, where the motion of a swarm is specified as a vector field, which is then approximated by the movement of individual members (Bachrach, Beal, and McLurkin 2010). I propose that this can be exploited to build a “tactical command” model of swarm control, whereby human “officers” dynamically decompose a swarm into units and task those units to carry out geometric and topological maneuvers under the constraints imposed by the platform. This abstraction may also allow situation awareness interfaces for individual agents to be extended to apply to swarm units.

There have been significant advances in developing each phase of the mission. Secondly, based on our everyday algorithms that allow researchers to examine these experience with physical objects in our environment, behaviors in simulation (Luke et al. 2005), generally assuming the three major physical states of matter, solid, liquid and noise-free estimates of the agents' own, neighbors' and gas, represent a natural and intuitive means of describing the targets' positions. However, the actual information flow into types of motions a swarm of mobile robots can perform as biological agents' in terms of the sensing, processing and they cluster, transit or wander (Gage 1992).

Knowledge engineering from experimental design (KEfED) is a novel approach based on the dependency relationships that occur between the variables of a scientific study. We used this approach to curate the experimental designs of ten scientific papers from a well-established database of HIV vaccine trials in non-human primates. The KEfED models provide a characteristic, data-oriented signature for each measurement made in the study. We present preliminary analysis of these manually-curated, detailed representations using our own open-source curation tools and show the multi-variate statistical analyses on the resultant models of experimental design. The analyses produced a visualization of the similarities between studies and an account of the dependency relationships across studies. We describe our approach in the context of a knowledge engineering strategy based on creating large-scale domain-independent repositories of experimental observatio