[The following report on BDA’14 was written by my student Mahdi Zamani]
[A more polished version of this report is available HERE]
and I attended a workshop
on Biological Distributed Algorithms
co-located with DISC 2014
. The workshop consisted of 20 talks distributed in two days and focused on the relationships between distributed computing and distributed biological systems and in particular, on analysis and case studies that combine the two. The following is a summary of some of the talks we found interesting.
Insect Colonies and Distributed Algorithms; Insect Colony Researcher Viewpoint, Anna Dornhaus, University of Arizona
Anna talked about several open questions in modeling the behavior of different insect colonies. Insect colonies go through many changes over time in response to their changing needs and environment.
Figure 1. BDA 2014
Most changes happen via complex collective behaviors such as task allocation, foraging (food finding), nest building, load transport, etc. One interesting aspect of insect colonies is that unreliable individual behaviors result in complex group behaviors that are reliable. Individuals use various methods of communication such as pheromone trails, versatile signals, visual cues, substrate vibration, and waggle dance. Waggle dance is a sophisticated method of communication among honeybees to indicate resource locations by showing the angle from sun. Biologists are generally interested in computer models to know how individual behaviors impact group behaviors. In particular, they are interested to understand how positive feedbacks (a process A produces more of another process B which in turn produces more of A and so on) lead to significant consequences such as symmetry breaking. For example, ants tend to choose from one food source even if there are multiple similar sources around them. Also, larger colonies result in more symmetry breaking behavior. This motivates the following questions: How does the size of a colony affect collective behavior? Why is the workload distribution so uneven in some biological systems?
Distributed Algorithms and Biological Systems, Nancy Lynch, MIT
Nancy started by describing similarities between biological and distributed systems. Both systems often have components that perform local communications using message passing and local broadcasting. In bio systems, there are components with simple states that follow simple rules. To model a bio system using a distributed algorithm, the first step is to define the problem, the platform (physical capabilities of the system such as memory), and the strategies (rules).
Nancy then talked about two important distributed problems: leader election and maximal independent sets (MIS). In leader election, there is a ring of processes that can communicate with their neighbors and the goal is to pick a leader process. If the processes are all identical and their behaviors are deterministic, then solving this problem is impossible due to symmetry (all processes are similar). On the other hand, if the processes are not identical (i.e., each has a unique ID), then finding a leader is possible. Interestingly, in a setting with identical processes that are allowed to make random choices, this problem can be solved using a biased coin: each party flips a coin with probability 1/n to announce itself as the leader.
In MIS, the goal is to find the largest subset of the vertices of a graph such that no two neighbors (i.e., vertices connected directly via an edge) are both in the subset. There is a Las Vegas algorithm for solving this problem: in each of several rounds, each party flips a biased coin and informs its neighbors that it is in the MIS if it has not received a similar message from its neighbors. Each party stops if either it is in MIS or one of its neighbors in the MIS. Nancy finally talked about three ant colony problems that her research group has recently been working on: Ants foraging, house hunting, and task allocation.
Modeling Slime Mold Networks, Saket Navlakha, CMU
Saket started his talk by explaining an experiment related to slime mold, where the mold food was put in different locations similar to the station of the Tokyo rail system. They observed that the mold grew in a similar network as the rail system. This is very interesting because Japanese engineers could have asked a slime mold to design the rail system instead of spending several hours on the design.
Saket then continued by describing their model of the slime mold behavior for finding food sources. For simplicity, they assume there is a complete graph at first and then by calculating flow over the edges (tubes) some of the edges are disconnected. Then, they measured and compared the cost, efficiency, and fault tolerance of the network generated by their model and the Tokyo rail system: their model is as efficient as the rail system! The brain development has a similar behavior: it generates a complete neural network at first and then prunes over time. This is called the synaptic pruning algorithm.
Figure 2. Yellow slime mold growth
(courtesy of ScienceDaily)
The human brain starts with a very dense network of neurons and each edge keeps track of the number of times it is used to route information based on some pre-determined distribution. Then, the network is pruned based on the flow information. Saket finally talked about a similar distributed model for bacterial foraging (E. coli) in complicated terrains.
Collective Load Transport in Ants, Ofer Feinerman, Weizmann Institute
Ever seen a group of ants carrying a large foot item together? Ofer talked about collective load retrieval, the process in which a large number of ants cooperate to carry a large food item to the nest. Ofer’s research team tracked a group of ants and the load over distances of about 1000 ant lengths and used image analysis to obtain highly detailed information. They showed that the collective motion is highly cooperative and guided by temporary leaders that are knowledgeable regarding the correct direction home. Ofer finally presented a theoretical model suggesting that the ant-load system is poised at a critical point between random and ballistic motions which renders it highly susceptible to a knowledgeable leader. He played a video showing a group of ants carrying their load in a wrong direction. Then, one ant joined the group as the leader and corrected the direction.
Distributed Information Processing by Insect Societies, Stephen Pratt, Arizona State University
Stephen talked about a collective model of optimal house-hunting in rock ant Temnothorax albipennis. Each colony of T. albipennis has a single queen and hundreds of scouts (workers). In the process of house-hunting, scouts first discover new nests and assess them according to some criteria such as size and darkness. Then, they recruit other ants to the new nest using tandem running, where an informed ant leads a second ant to her destination to get a second opinion about the nest (see Figure 3↓).
Figure 3. Ants tandem run (courtesy of Stephen Pratt)
When the number of ants in the new nest reaches a threshold, scouts begin rapid transport of the rest of the colony by carrying nest-mates. In each time step, each ant is in one of these three states: explore, tandem, and transport. The transition between these states happens based on the ant’s evaluation of the quality of the nest sites and the population of the ants in this sites. Stephen defines optimality with regards to the speed and accuracy of the decision-making process. He asked: Does a colony have a greater cognitive capacity than an individual? For the house-hunting process, recent lab experiments show that when the number of nests (choices) increases, colony performs much better in choosing the good choices while lone ants visit more nests. He then asked: Do colonies make more precise discriminations than individuals? To answer this, Stephen’s team ran experiments to measure how individuals and colonies can correctly compare a dark nest with nests with various brightness levels. Interestingly, they observed that colonies can correctly choose darker nests with significantly more accuracy than individuals. They also show that even two ants perform significantly better than one.
Cells, Termites, and Robot Collectives, Radhika Nagpal, Harvard University
Radhika talked about biological systems from an engineering viewpoint. Collective behaviors often result in self-repairing and self-organizing properties which are crucial for building robust systems. In bio systems, these properties are achieved from cooperation of vast numbers of unreliable agents.
Figure 4. Termite-inspired robots
(courtesy of Harvard University)
Radhika described a bio-inspired distributed robot system that can perform group tasks such as collective construction, collective transport, and pattern/shape formation. The robots achieve a desired global goal in a completely decentralized fashion by performing local interactions with other robots. In particular, they model a large population of termites for building complex structures (see Figure 4↑).
Confidence Sharing: An Economic Strategy for Efficient Information Flows in Animal Groups, Amos Korman, CNRS and University of Paris Diderot
Amos started his talk by defining two methods of communication that exist in biological systems: passive (indirect) and active (direct) communication. Passive communication is done by transferring information with no direct intention of signaling, i.e., cues from the behavior of one animal are indirectly perceived by others. For example, it is shown that animals align their movements to those performed by their neighbors. In active communication, an animal communicates directly with others by sending parts of its internal state via, for example, pheromone trails, cell signaling, etc. Amos then continued his talk by arguing that confidence exists among animals: they are shown to become more responsive as their certainty drops. For example, crickets increase their speed when they are more confident about their intention. This confidency is propagated from one cricket to others via passive and active communication. By sharing their confidence, agents improve their unreliable individual estimates. Amos described an algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. This gives a very simple and near optimal algorithm. The algorithm continuously updates agents confidence level based on the interaction it has with other agents. Unfortunately, if there are bandwidth and computational restrictions to agents, then the performance of this algorithm decreases significantly. Also, the algorithm assumes two agents who exchange confidence information must have disjoint set of exchange history.
Task Allocation in Ant Colonies, Alex Cornejo, Harvard University
Alex presented a general mathematical model for task allocation in ant colonies that can be used to achieve optimal division of labor. Based on this model, Alex described a distributed randomized efficient algorithm for task allocation that imposes minimal assumptions on the capabilities of the individual ants. The proposed algorithm requires constant amount of memory. Moreover, it assumes the ants have a primitive binary feedback function to sense the current labor allocation and to determine whether a particular task requires more workers or not. The algorithm also assumes individual workers do not differ in their ability to perform tasks. The proposed algorithm for ants converges to a near-optimal task allocation with high probability in time which is logarithmic in the size of the colony.
Compared to the previous work on task allocation, the proposed algorithm is different in the following aspects: it uses constant memory per ant, works only when the number of tasks to allocate is a small constant, and the allocation is for proportions of workers (and not for individual workers), and all workers are similar. Once workers are variant in ability to perform tasks, the task allocation problem becomes NP-hard. One drawback that was pointed out by one of the participants is that their model does not strictly adhere to the real ants’ behaviors.
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