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Nigel R Franks - One of the best experts on this subject based on the ideXlab platform.
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variability in individual assessment behaviour and its implications for Collective Decision making
Proceedings of The Royal Society B: Biological Sciences, 2017Co-Authors: Thomas A Osheawheller, Naoki Masuda, Ana B Sendovafranks, Nigel R FranksAbstract:Self-organized systems of Collective behaviour have been demonstrated in a number of group-living organisms. There is, however, less research relating to how variation in individual assessments may facilitate group Decision-making. Here, we investigate this using the decentralized system of Collective nest choice behaviour employed by the ant Temnothorax albipennis, combining experimental results with computational modelling. In experiments, isolated workers of this species were allowed to investigate new nest sites of differing quality, and it was found that for any given nest quality, there was wide variation among individuals in the durations that they spent within each nest site. Additionally, individual workers were consistent in spending more time in nest sites of higher quality, and less time in those of lower quality. Hence, the time spent in a new nest site must have included an assessment of nest quality. As nest site visit durations (henceforth termed assessment durations) are linked to recruitment, it is possible that the variability we observed may influence the Collective Decision-making process of colonies. Thus, we explored this further using a computational model of nest site selection, and found that heterogeneous nest assessments conferred a number of potential benefits. Furthermore, our experiments showed that nest quality assessments were flexible, being influenced by experience of prior options. Our findings help to elucidate the potential mechanisms underlying group behaviour, and highlight the importance of heterogeneity among individuals, rather than precise calibration, in shaping Collective Decision-making.
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seasonality in communication and Collective Decision making in ants
Proceedings of The Royal Society B: Biological Sciences, 2014Co-Authors: Nathalie Stroeymeyt, Martin Giurfa, C Jordan, G Mayer, S Hovsepian, Nigel R FranksAbstract:The ability of animals to adjust their behaviour according to seasonal changes in their ecology is crucial for their fitness. Eusocial insects display strong Collective behavioural seasonality, yet the mechanisms underlying such changes are poorly understood. We show that nest preference by emigrating Temnothorax albipennis ant colonies is influenced by a season-specific modulatory pheromone that may help tune Decision-making according to seasonal constraints. The modulatory pheromone triggers aversion towards low-quality nests and enhances colony cohesion in summer and autumn, but not after overwintering—in agreement with reports that field colonies split in spring and reunite in summer. Interestingly, we show that the pheromone acts by downgrading the perceived value of marked nests by informed and naive individuals. This contrasts with theories of Collective intelligence, stating that accurate Collective Decision-making requires independent evaluation of options by individuals. The violation of independence highlighted here was accordingly shown to increase error rate during emigrations. However, this is counterbalanced by enhanced cohesion and the transmission of valuable information through the colony. Our results support recent claims that optimal Decisions are not necessarily those that maximize accuracy. Other criteria—such as cohesion or reward rate—may be more relevant in animal Decision-making.
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a simple threshold rule is sufficient to explain sophisticated Collective Decision making
PLOS ONE, 2011Co-Authors: Elva J H Robinson, Nigel R Franks, Samuel Ellis, Saki Okuda, James A. R. MarshallAbstract:Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a ‘good enough’ option. Social animals make Collective Decisions about many group behaviours including foraging and migration. The key to the Collective choice lies with individual behaviour. We present a case study of a Collective Decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed Decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible Decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of Collective Decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, Decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote Collective rationality in group Decision-making.
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experience dependent flexibility in Collective Decision making by house hunting ants
Behavioral Ecology, 2011Co-Authors: James A. R. Marshall, Nathalie Stroeymeyt, Elva J H Robinson, P M Hogan, Martin Giurfa, Nigel R FranksAbstract:When making a Decision, solitary animals often adjust to local conditions by using flexible evaluation and Decision criteria, even though these may occasionally lead to irrationality. By contrast, Collective Decision making in large animal groups—such as, nest choice by emigrating ant colonies—is usually considered to rely on robust, fixed preference rules and to be immune to irrationality. Here, we show that familiarization with available nest sites prior to emigration can lead to flexible Collective Decisions in the house-hunting ant Temnothorax albipennis. Colonies allowed to inspect a mediocre nest site while their home nest is still intact usually develop an aversion toward that nest. We found that aversion strength was not determined by the quality of the familiar nest only but was also influenced by the quality of the home nest. As a result, nest choice in later emigrations depended strongly on the quality of the previously experienced home nest, allowing colonies to adjust to the local quality of available sites. Additionally, we found that in a worst-case scenario where the only alternatives are of even lower quality, developing an aversion toward a mediocre nest can occasionally lead to poor Collective Decisions. We discuss whether the observed flexibility in Collective choices necessarily requires experience-dependent changes in individual Decision criteria and develop a new analytical model of nest choice in house-hunting ants showing that a fixed-threshold Decision strategy at the individual level can lead to experience-dependent, flexible Decisions at the colony level. Key words: ants; Collective Decision making, irrationality, nest choice; previous experience. [BehavEcol]
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On optimal Decision making in brains and social insect colonies
Modelling Natural Action Selection, 2011Co-Authors: James A. R. Marshall, Tim Kovacs, Robert Planqué, Rafal Bogacz, Anna Dornhaus, Nigel R FranksAbstract:The problem of how to compromise between speed and accuracy in Decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between Decision-making in primate brains and Collective Decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a Decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of Decision-making. In primate Decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes Decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of Collective Decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal Collective Decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how Collective Decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of Decision-making in diverse biological systems.
Heiko Hamann - One of the best experts on this subject based on the ideXlab platform.
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Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins
2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019Co-Authors: Mohammad Divband Soorati, Maximilian Krome, Marco Mora-mendoza, Javad Ghofrani, Heiko HamannAbstract:Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of Collective Decision-making. Despite intensive research on models of Collective Decision-making, the implementation in multi-robot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the Decision-making process and by increasing the scenario difficulty. Most studies on large-scale multi-robot Decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant Collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to Collectively reconsider its previous Decision accordingly. This is only possible by preventing lock-ins, a global consensus state of no return (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.
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Photomorphogenesis for Robot Self-assembly: Adaptivity, Collective Decision-making, and Self-repair
2019Co-Authors: Mohammad Divband Soorati, Javad Ghofrani, Mary Katherine Heinrich, Payam Zahadat, Heiko HamannAbstract:Self-assembly in biological systems is an inspiration for engineered large-scale multi-modular systems with desirable characteristics, such as robustness, scalability, and adaptivity. Previous works have shown that simple mobile robots can be used to emulate and study self-assembly behaviors. However, many of these studies were restricted to rather static and inflexible aggregations in predefined shapes, and were limited in adaptivity compared to that observed in nature. We propose a photomorphogenesis approach for robots using our vascular morphogenesis model---a light-stimuli directed method for multi-robot self-assembly inspired by the tissue growth of trees. Robots in the role of `leaves' collect a virtual resource that is proportional to a real, sensed environmental feature. This resource is then shared throughout the whole robot aggregate and determines where it grows or shrinks as a reaction to the dynamic environment. In our approach the robots use supplemental bioinspired models to Collectively select a seed robot to decide who starts to self-assemble (and where), or to assemble static aggregations. The robots then use our vascular morphogenesis model to aggregate in a directed way preferring bright areas, hence resembling natural phototropism (growth towards light). In this assembly, they are adaptive and able to react to a dynamic environment by Collectively and autonomously rearranging the aggregate, discarding outdated parts and growing new ones. In representative experiments, the self-assembling robots Collectively make rational Decisions on where to grow. Cutting off parts of the aggregate triggers a self-organizing repair process in the robots, and the parts regrow. All these capabilities of adaptivity, Collective Decision-making, and self-repair in our robot self-assembly originate directly from self-organized behavior of the vascular morphogenesis model. Our approach opens up opportunities for self-assembly with reconfiguration on short time-scales with high adaptivity of dynamic forms and structures.
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Collective Decision with 100 kilobots speed versus accuracy in binary discrimination problems
Autonomous Agents and Multi-Agent Systems, 2016Co-Authors: Gabriele Valentini, Heiko Hamann, Eliseo Ferrante, Marco DorigoAbstract:Achieving fast and accurate Collective Decisions with a large number of simple agents without relying on a central planning unit or on global communication is essential for developing complex Collective behaviors. In this paper, we investigate the speed versus accuracy trade-off in Collective Decision-making in the context of a binary discrimination problem--i.e., how a swarm can Collectively determine the best of two options. We describe a novel, fully distributed Collective Decision-making strategy that only requires agents with minimal capabilities and is faster than previous approaches. We evaluate our strategy experimentally, using a swarm of 100 Kilobots, and we study it theoretically, using both continuum and finite-size models. We find that the main factor affecting the speed versus accuracy trade-off of our strategy is the agents' neighborhood size--i.e., the number of agents with whom the current opinion of each agent is shared. The proposed strategy and the associated theoretical framework can be used to design swarms that take Collective Decisions at a given level of speed and/or accuracy.
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self organized Collective Decision making in a 100 robot swarm
National Conference on Artificial Intelligence, 2015Co-Authors: Gabriele Valentini, Heiko Hamann, M DorigoAbstract:We study a self-organized Collective Decision-making strategy to solve a site-selection problem using a swarm of simple robots. Robots can only move forward or turn in place; sense the intensity of the ambient light; and exchange 3-byte messages with peers in a limited range. The goal of the swarm is to Collectively decide which of the sites available in the environment is the best candidate site. We define a distributed and iterative Decision-making strategy: robots explore the available options, determine the options' qualities, decide autonomously which option to take, and communicate their Decision to neighboring robots. We study the effectiveness and robustness of the proposed strategy using a swarm of 100 Kilobots and we focus on the impact of the neighborhood size over the dynamics of the system.
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derivation of a micro macro link for Collective Decision making systems
Parallel Problem Solving from Nature, 2014Co-Authors: Heiko Hamann, Gabriele Valentini, Yara Khaluf, M DorigoAbstract:Relating microscopic features (individual level) to macroscopic features (swarm level) of self-organizing Collective systems is challenging. In this paper, we report the mathematical derivation of a macroscopic model starting from a microscopic one for the example of Collective Decision-making. The Collective system is based on the application of a majority rule over groups of variable size which is modeled by chemical reactions (micro-model). From an approximated master equation we derive the drift term of a stochastic differential equation (macro-model) which is applied to predict the expected swarm behavior. We give a recursive definition of the polynomials defining this drift term. Our results are validated by Gillespie simulations and simulations of the locust alignment.
Martin Stevens - One of the best experts on this subject based on the ideXlab platform.
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Speed versus accuracy in Collective Decision making
Proceedings of the Royal Society B: Biological Sciences, 2003Co-Authors: Nigel R Franks, Jon P. Fitzsimmons, Anna Dornhaus, Martin StevensAbstract:We demonstrate a speed versus accuracy trade-off in Collective Decision making. House-hunting ant colonies choose a new nest more quickly in harsh conditions than in benign ones and are less discriminating. The errors that occur in a harsh environment are errors of judgement not errors of omission because the colonies have discovered all of the alternative nests before they initiate an emigration. Leptothorax albipennis ants use quorum sensing in their house hunting. They only accept a nest, and begin rapidly recruiting members of their colony, when they find within it a sufficient number of their nest-mates. Here we show that these ants can lower their quorum thresholds between benign and harsh conditions to adjust their speed-accuracy trade-off. Indeed, in harsh conditions these ants rely much more on individual Decision making than Collective Decision making. Our findings show that these ants actively choose to take their time over judgements and employ Collective Decision making in benign conditions when accuracy is more important than speed.
Marco Dorigo - One of the best experts on this subject based on the ideXlab platform.
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managing byzantine robots via blockchain technology in a swarm robotics Collective Decision making scenario
Adaptive Agents and Multi-Agents Systems, 2018Co-Authors: Volker Strobel, Eduardo Castello Ferrer, Marco DorigoAbstract:While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots---i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a Collective Decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing Collective Decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.
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Collective Decision with 100 kilobots speed versus accuracy in binary discrimination problems
Autonomous Agents and Multi-Agent Systems, 2016Co-Authors: Gabriele Valentini, Heiko Hamann, Eliseo Ferrante, Marco DorigoAbstract:Achieving fast and accurate Collective Decisions with a large number of simple agents without relying on a central planning unit or on global communication is essential for developing complex Collective behaviors. In this paper, we investigate the speed versus accuracy trade-off in Collective Decision-making in the context of a binary discrimination problem--i.e., how a swarm can Collectively determine the best of two options. We describe a novel, fully distributed Collective Decision-making strategy that only requires agents with minimal capabilities and is faster than previous approaches. We evaluate our strategy experimentally, using a swarm of 100 Kilobots, and we study it theoretically, using both continuum and finite-size models. We find that the main factor affecting the speed versus accuracy trade-off of our strategy is the agents' neighborhood size--i.e., the number of agents with whom the current opinion of each agent is shared. The proposed strategy and the associated theoretical framework can be used to design swarms that take Collective Decisions at a given level of speed and/or accuracy.
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majority rule opinion dynamics with differential latency a mechanism for self organized Collective Decision making
Swarm Intelligence, 2011Co-Authors: Eliseo Ferrante, Alexander Scheidler, Carlo Pinciroli, Mauro Birattari, Marco DorigoAbstract:Collective Decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a Collective Decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system’s behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model’s predictions through a swarm robotics experiment in which robot teams must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.
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Collective Decision-making based on social odometry
Neural Computing and Applications, 2010Co-Authors: Álvaro Gutiérrez, Alexandre Campo, Félix Monasterio-huelin, Luis Magdalena, Marco DorigoAbstract:In this paper, we propose a swarm intelligence localization strategy in which robots have to locate different resource areas in a bounded arena and forage between them. The robots have no knowledge of the arena dimensions and of the number of resource areas. The strategy is based on peer-to-peer local communication without the need for any central unit. Social Odometry leads to a self-organized path selection. We show how Collective Decisions lead the robots to choose the closest resource site from a central place. Results are presented with simulated and real robots.
Naomi Ehrich Leonard - One of the best experts on this subject based on the ideXlab platform.
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Collective Decision making in ideal networks the speed accuracy tradeoff
IEEE Transactions on Control of Network Systems, 2014Co-Authors: Vaibhav Srivastava, Naomi Ehrich LeonardAbstract:We study Collective Decision-making in a model of human groups, with network interactions, performing two alternative choice tasks. We focus on the speed-accuracy tradeoff, i.e., the tradeoff between a quick Decision and a reliable Decision, for individuals in the network. We model the evidence aggregation process across the network using a coupled drift-diffusion model (DDM) and consider the free response paradigm in which individuals take their time to make the Decision. We develop a reduced DDM as a decoupled approximation to the coupled DDM and characterize its efficiency. We determine high probability bounds on the error rate and the expected Decision time for the reduced DDM. We show the effect of the Decision-maker's location in the network on their Decision-making performance under several threshold selection criteria. Finally, we extend the coupled DDM to the coupled Ornstein-Uhlenbeck model for Decision-making in two alternative choice tasks with recency effects, and to the coupled race model for Decision-making in multiple alternative choice tasks.
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Collective Decision making in ideal networks the speed accuracy tradeoff
arXiv: Optimization and Control, 2014Co-Authors: Vaibhav Srivastava, Naomi Ehrich LeonardAbstract:We study Collective Decision-making in a model of human groups, with network interactions, performing two alternative choice tasks. We focus on the speed-accuracy tradeoff, i.e., the tradeoff between a quick Decision and a reliable Decision, for individuals in the network. We model the evidence aggregation process across the network using a coupled drift diffusion model (DDM) and consider the free response paradigm in which individuals take their time to make the Decision. We develop reduced DDMs as decoupled approximations to the coupled DDM and characterize their efficiency. We determine high probability bounds on the error rate and the expected Decision time for the reduced DDM. We show the effect of the Decision-maker's location in the network on their Decision-making performance under several threshold selection criteria. Finally, we extend the coupled DDM to the coupled Ornstein-Uhlenbeck model for Decision-making in two alternative choice tasks with recency effects, and to the coupled race model for Decision-making in multiple alternative choice tasks.
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node certainty in Collective Decision making
Conference on Decision and Control, 2012Co-Authors: Ioannis Poulakakis, Luca Scardovi, Naomi Ehrich LeonardAbstract:This paper brings into focus the relationship between the location of a Decision-making unit in a network of Decision makers and its certainty in integrating information toward a Decision. A collection of units, each represented by a Drift-Diffusion Model (DDM), accrues evidence in continuous time by observing a (noisy) stimulus. Their task is to make a Decision that depends on accurately identifying the stimulus observed. It is shown that common structural centrality measures based on nodal degree or geodesic distance cannot be used to rank the units according to their certainty in integrating information. Instead, the variance associated with the state of a Decision-making unit depends on the communication topology in a way that incorporates all possible paths connecting that unit with the rest.
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coupled stochastic differential equations and Collective Decision making in the two alternative forced choice task
Advances in Computing and Communications, 2010Co-Authors: Ioannis Poulakakis, Luca Scardovi, Naomi Ehrich LeonardAbstract:This paper investigates the effect of coupling in a Collective Decision-making scenario, in which the task is to correctly identify a (noisy) stimulus between two known alternatives. Multiple interconnected Decision-making units, each represented by a Drift-Diffusion Model (DDM), accumulate evidence toward a Decision. A number of different graph topologies among the DDM's are considered, and their effect on the accuracy of the Decision is investigated. It is deduced that, for the same stimuli, the average of the collected evidence increases linearly with time toward the correct Decision regardless of the communication topology. However, the uncertainty associated with the process is affected by the interconnection graph, implying that certain topologies are better than others.