NDLI: Quorum sensing without counting , a discounting approach , or : Nobody goes there anymore , it ’ s too crowded

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Quorum sensing without counting , a discounting approach , or : Nobody goes there anymore , it ’ s too crowded

Content Provider	Semantic Scholar
Author	Pavlic, Theodore P. Hanson, Jake R. Valentini, Gabriele Walker, Sara Imari Pratt, Stephen C.
Copyright Year	2018
Abstract	Quorum sensing is ubiquitous in nature, but the underlying mechanisms that individuals use to sense quorums are not well understood beyond bacterial quorum sensing. Encounter rate appears to be an important cue of quorum attainment in ants, but how ants synthesize their individual-level experiences to determine whether they have reached a critical encounter rate is still unknown. Computer scientists have suggested quorum-sensing strategies that are implemented on an individual agents with some way to count discrete encounters with other individuals. Instead, motivated by observations of honeybees and classic research on temporal discounting in psychology, we propose a quorum sensing algorithm based on the likelihood that an ant will re-encounter a nest entrance within a short time period of the last encounter with another ant. Simulations of the resulting mechanism show both outcome and response-time characteristics that are qualitatively similar to ants, and the implementation does not require maintaining a count of previous encounters with other agents. Quorum sensing (QS), or the population-dependent regulation of a behavior, is a biological distributed algorithm widely used by microorganisms, particularly bacteria [1]. The phenomenon was first described as “autoinduction” by Nealson [2], who determined that bioluminescent bacteria would only express luminescence when in the presence of a sufficiently high concentration of an “autoinducer” chemical that they also produce. It is almost as if the bacteria will not pay the cost of luminescence unless there are enough surrounding bacteria that the group together could produce visible light to a macro-organism. In fact, this density-dependent ability to produce light appears to be central to symbiotic mutualisms between certain animals and photoluminescent bacteria. For example, the luminous bacterium Vibrio fischeri will initiate a benign and persistent infection of the “light organ” of a young bobtail squid Euprymna scolopes, and this squid will actively manage the density of the bacteria to turn on and off luminescence as needed [3]. In particular, the squid is thought to use its bioluminescent light organ at night for counter illumination Supported by NSF Grant PHY-1505048. to conceal its silhouette against the bright moonlight. In the morning, the squid expels enough bacteria to turn off the luminescence; however, the remaining culture of V. fischeri in the light organ will reproduce over the next 12 hours to be at luminescent densities again by nightfall. Thus, the bacteria are so successful at coordinating their light emissions that the squid can treat the large collective as a single, macroscopic unit akin to a flood light. Since the early studies by Nealson [2] of bacterial bioluminescence, a wide range of other coordinated bacterial behaviors have been discovered that also depend on quorum sensing, such as biofilm formation and expression of virulence [1], [4]–[6]. In fact, QS is so pervasive in bacteria that a single bacterium will be able to simultaneously detect conspecific quorums as well as heterospecific quorums. As reviewed by Sumpter and Pratt [7], quorum-sensing responses are a ubiquitous feature of collective decision making in animal systems as well – including cockroaches, ants, honeybees, spiders, birds, fish, and primates. However, whereas much is known about the precise chemo-sensory mechanisms underlying QS in bacteria, very little is known about these mechanisms in animals. Experimentally, it is clear that animals are sensitive to the densities of others around them, but it is not clear what individual-level cues are driving the density-dependent behavior. In this abstract, we introduce a novel, bio-inspired method for quorum sensing in mobile, multi-agent systems. Unlike other quorum-sensing approaches, this method does not rely on each agent maintaining a count of previous encounters. Consequently, it is simple to implement on hardware with low capabilities, and it is a plausible mechanism for how social-insects may be detecting their own quorums. 1. Background: QS in Social Insects Honeybees and some species of crevice-dwelling ants, particularly those in the genus Temnothorax, frequently must find, compare, and select the best of a number of potential nest sites in the environment [8]. The shift in this “best-ofN” problem from exploration to exploitation of the chosen nest occurs when a sufficient number of nestmates co-occur at one of the candidates. In other words, collective agreement emerges out of coordination via simultaneous quorum sensing. For the case of Temnothorax ants, Pratt [9] has shown that the encounter rate experienced by an individual ant when moving within a nest-site candidate is a strong predictor of whether she will commit to that site and initiate a fast-paced migration behavior of her other nestmates (i.e., fast “Transport” behavior and slow “Tandem Run” behavior). As we have summarized in Fig. 1(a), the probability for an ant to commit is consistent with a sigmoidal function of encounter rate that reaches 50% probability of commitment at a certain critical encounter rate regardless of other properties of the nest, such as its size, or the total number of ants within the nest. Still, from the perspective of an ant randomly moving throughout a dark nest, a string of short times between encounters may occur by chance alone even at low densities, and so inferring density from encounter data is not trivial. In fact, as summarized in Fig. 1(b), ants experiencing mean inter-encounter times close to the critical encounter rate remained in the candidate nest much longer than ants experiencing very long or very short interencounter times. This temporal observation supports the interpretation that ants must gain more confidence in their estimates when encounter rates are near the critical value. Given the putative importance of encounter rate, a number of bio-inspired methods for mobile, artificial agents have been developed that invent an individual-level mechanism that is sensitive to encounter rate. Peysakhov and Regli [10] consider the case of servers deployed across a network that can be brought up and down by mobile agents traversing the network. In this scenario, the mobile agent would use its encounters with running servers to determine when to bring up servers (in the case of low encounter rate) or bring down servers (in the case of high encounter rate). The implementation of Peysakhov and Regli uses a counter that continually decays toward a threshold for bringing up a new server and rises on encounters with running servers until reaching a threshold for bringing down a server. At a critical number of servers, the stimulus and decay processes balance, and no changes are made. This approach is essentially identical to drift–diffusion characterizations of human decision making used in cognitive psychology [e.g.,11]. More recently, Musco et al. [12] proposed a mechanism for estimating the encounter rate with other ants directly as opposed to detecting whether it was sufficiently high or low for a decision. In their approach, ants continually increase an encounter count and then use the temporal average encounter rate as an estimate of the true encounter rate. This approach is identical to the one Pavlic and Passino [13] proposed as a decision policy heuristic for a forager estimating its encounter rate with the environment in order to determine the portfolio of tasks that will maximize its long-term rate of reward. However, Musco et al. constrain their agent on a discrete space, and thus are able to prove arbitrary close convergence to the actual encounter rate. What both families of quorum-sensing methods – threshold-based models and rate-estimation models – have in (a) Decision Outcomes
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Alternate Webpage(s)	http://www.public.asu.edu/~gvalent3/pdf/PavHanValWalPra.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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