Our Work from BYU

Self-Organization in Flocks and Swarms

  • Couzin's paper where he talks about how changing parameters leads to phase transitions (e.g., random to torus phase) in fish schooling. This uses the classic three metric zones from the Boids model: repulsion, attraction, and orientation.
  • Olfati-Saber's great paper on the theory of flocking. He defines a flock as a structured topology (an isotropic lattice) that uses only local connections. Under some fairly reasonable assumptions, he shows that if the members of the flock always know where a leader agent is then the flock will (a) arrange itself into an isotropic lattice and (b) follow the leader when the environment has no obstacles. He also shows how repelling agents can be used to push agents away from (and cause to go tangent to) obstacles in the environment. Email Mike if you'd like a copy of his annotated version of the paper.
  • Evolving the selfish herd extend's Couzin's model to allow agents to adapt their individual parameters in the presence of either (a) predators or (b) food sources. Under the presence of a predator, two apparently stable flocking behaviors result: a slow mill and a fast flock. In terms of expressiveness, these two appear to be different ways in which a predator can influence what the flock does. The fast flock could be used in HuBIRT to cause a group to fan-out as in a mine-sweeping task. Under foraging, clumped food groups are compatible with flocks but isolated food packets promote non-flocking behavior. Note that this paper identifies four different phases of possible collective behavior as a function of model parameters. This is one more than Couzin identified. Email Mike if you'd like a copy of his annotated copy of this paper.
  • Self-organization in self-propelled particles is a physics-based version of swarming work. Interestingly, their model is also additive and includes attraction, orientation, and repulsion. They refine these ideas a bit, and provide some explanation for when various phases emerge and when these phases are stable.
  • This paper on task allocation discusses the use of attraction/repulsion principles based on light sensors for team selection. Different type of lights (green, yellow) indicate different kinds of behaviors (attraction, repulsion, escape). Further, they study the performance achieved using attraction/repulsion based task allocation vs broadcast based task allocation.
  • Modeling schooling in fish, and getting a nice tutorial on the math of collective intelligence.
  • This is a great tutorial on common themes in self-organizing or bio-inspired systems. I've added some annotations for how humans could interact with these systems. These may be relevant when we start to apply agent-based models to the kinds of systems being built in the ONR project.

Moving Beyond Swarms/Flocks to Colonies and Packs.

  • Multi-robot system based on model of wolf hunting behavior to emulate wolf and elk interactions By Jadden, Arkin, and MacNulty appeared in ROBIO 2010. It presents a nice model of wolf collaborative hunting behavior, identifying phases of group behavior and suggesting some individual rules by which emergent group behavior can emerge. The paper is a bit sparse on results. Email Mike if you'd like a copy of his annotated version.
  • Consensus decision making in animals is a nice framework paper that describes how animal and human groups can include flocking behavior but also extend to more sophisticated behavior. They use a compelling example for how honeybees choose a new site for their hive to motivate consensus problems, and this feels to me like it opens the door for HuBIRT that works beyond flocks to more of a multi-tasking, quorum-signaling problem domain. Note that paper gives a simple decision tree based on global/local communication and presence/absence of a conflict of interest; the tree produces examples from nature and identifies mechanisms for producing consensus decision-making. Email Mike if you'd like a copy of his annotated version.
  • Group decisions in humans and animals: A survey provides a useful distinction between collective and interactive decision-making. The paper also provides some useful aggregation rules for bringing together information from various members of a collective, and makes a distinction between shared, partially shared, and unshared decision processes in humans and animals. Furthermore, the paper notes differences between human and animal rationality, and identifies some noteworthy theoretical concepts (e.g., Condorcet's jury theorem) that are applicable to animal decision-making.

The Importance and Role of Variability

Nearest Neighbor Topologies Versus Metric Topologies

  • Mike's annotated copy of Ballerini's classic paper on how topology-based interactions between agents promote group cohesiveness. In this work, Ballerini et al. measure how flocks of starlings interact and find strong evidence that their local interactions are determined by their closest 6-7 neighbors rather than by all neighbors within some kind of metric distance.
  • Limited Interactions in Flocks replicates Ballerini's results on topological structure in simulation. They use a probabilistic approach wherein interaction neighbors are chosen with a probability inversely proportional to their distances from the agent. Once a neighbor is chosen, it influences the agent's behavior according to the rules associated with Couzin's three metric zones. Send Mike an email for a copy of his annotated version of this paper.

Fielded Systems

Leading according to need

Email Mike if you'd like to see his annotated copy of either of the following papers.

  • David Sumpter recently called for work that combines mechanistic and functional explanations for animal interaction. He says that ``Mechanistic explanations look at how animals interact to produce group level patterns“ and ``Functional explanations are based on arguments about why a behaviour has evolved through natural selection.” A mechanistic explanation might be in the form of a set of rules that generate collective intelligence, such as the rules encoded in Couzin's model above, and a functional explanation might be in the form of the evolutionary forces that produce such behavior such as flocking behavior that evolves to avoid predation. He cites the excellent paper by Conradt et al.
  • The paper by Conradt et al. combines a functional description (how hungry a subset of fish are) with a mechanistic description (how assertive or speedy a fish is) for how small subgroups of fish can lead a larger school to an objective that differs from the goal of the majority.
  • Evolution of decision sharing, combining game theory, self organizing systems, and evolutionary stable strategies. Contributed by Brian Pendleton.

HuBIRT is also known as ''human-swarm interaction'' and ''assistive swarming''.

There are a handful of papers under this name in the literature.

  • Doug Gage's work on treating a swarm as a solid, liquid, or gas.
  • A cool exploratory study from Mike Lewis' lab that looks at how humans can use various fundamental swarm behaviors and swarm topologies to perform a set of canonical tasks This technical report is posted with Mike's permission.
  • Bashyal and Venayagamoorthy presented an HSI approach that provided a human with a partial plan and global information, and then allowed the human to adjust the autonomy of a small subset of swarm members to influence swarm behavior. They included a small user study that demonstrated that the HSI team performed better than the swarm alone. An important theme of their paper, in addition to the user study and the ad hoc set of desirable HSI features, is that ``The ideal man-machine interaction is … one that functions autonomously while providing users with a method to inject knowledge and guidance so as to improve [system performance].“ The take this design philosophy to an important extreme where a human's ``control over the swarm is only as much as that of any other member in the swarm,” meaning that the human may be able to control a single individual agent and thereby influence swarm behavior, but not perform any centralized control or exert any global influence on the entire swarm. Another interesting take away from this paper is that the authors use particle swarm optimization as the basis for enforcing efficient foraging for a gradient-based radiation source. Mike's annotated copy.
  • Introduces the term assistive swarming. I don't know the real reference for this and haven't read it carefully, but it seems like the name should be mentioned in literature reviews.
  • The GUARDIANS project seeks to use swarm robotic technology to support firefighters. These two papers present both the use-cases for HSI as well as an artificial potential field-based implementation. The robots include the ability to autonomously respond to obstacles and to follow either a proximate human or a remotely controlled virtual avatar through an environment. This ability to proximate track the robot may be unique in the literature. Although these papers claim that a stability analysis has been performed, I think it is more accurate to say that some simulations have been performed that subjectively establish that the HSI is successful.
  • Work by MaryAnne Fields on controlling swarms, so-called “Soldier-Robotic Swarm Interaction”.
  • A Generalized Graph-Based Method for Engineering Swarm Solutions to Multiagent Problems by Wiegand, et al., explores adapting a type of potential field-like agent controller in a multi-objective problem. Use graph-based method for designing behaviors, suggesting that an organizational approach (e.g., graph topology) may be instrumental in design collective behaviors.
  • Exerting Human Control Over Decentralized Robot Swarms. Zsolt Kira and Mitchell A. Potter. 2009.
  • A copy of the paper that we submitted to SMC2010. It includes Jon Whetten's work as well as a formalized version of Yisong's work.
  • Exerting Human Control Over Decentralized Robot Swarms. Zsolt Kira and Mitchell A. Potter. 2009.


  • An implementation of physicomimetics. Sujit says “Its a nice way of implementing the physiomimetics with light sensors for the follower robots. Yellow light to attract while green light to repel. This is another form of no-broadcast communication.”

Other Examples from Biology or Human Behavior

  • This is a great tutorial on common themes in self-organizing or bio-inspired systems. I've added some annotations for how humans could interact with these systems. These may be relevant when we start to apply agent-based models to the kinds of systems being built in the ONR project.
  • Science article on how network structure influences how behavior diffuses through a social network. Mike's annotated version is available here.
  • Cell proliferation affects the way that tissues are organized, including strong topological properties.

Concepts from Organizational Behavior, Physics, and Engineering

Human Factors

  • Ironies of Automation by Lisanne Bainbridge. Discusses human factors in the context of automated systems and points out some ironies of increased automation. For example, fully automated systems may require much more operator training than semi-automated systems due to a lack of hands on experience.
  • A Theoretical Field-Analysis of Automobile-Driving by James J. Gibson and Laurence E. Crooks. A classic paper 1937 paper detailing human factors involved in automobile driving.


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