Self-organized cooperation between robotic swarms

We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.     

Task partitioning in swarms of robots: an adaptive method for strategy selection

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals?? skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.     

Genes,agents and the institution of responsible action

The concern of this paper is with how the accounts of human beings and their behaviour now emerging from genetics, genomics and the new human biotechnology should be related to traditional accounts in which we identify ourselves as responsible agents, capable of choice, who normally act freely and voluntarily. The paper addresses these apparently competing accounts in terms of their functions and modes of use, and thereby arrives at a general solution to this current version of the ancient problem of free will and determinism. The causal scientific discourse of genetics and the everyday discourse of responsibility and choice do different things for us, it suggests, and should not be regarded as articulating conflicting theories. Whilst the former is oriented to the task of naturalistic explanation, the latter is predominantly, if not entirely, a medium of communication through which we affect each other and thereby mutually regulate our conduct. If this is indeed the case, then interesting implications follow concerning the proper relationship of the two kinds of account, which need no longer be regarded as incompatible with each other. And insights emerge into both the limitations and the profound importance of the contribution that genetics and genomics seem destined to make to the understanding of human behaviour.     

Variable responses to large-scale climate change in European Parus populations

Spring temperatures in temperate regions have increased over the past 20 years and many organisms have responded to this increase by advancing the timing of their growth and reproduction. However, not all populations show an advancement of phenology. Understanding why some populations advance and others do not will give us insight into the possible constraints and selection pressures on the advancement of phenology. By combining two decades of data on 24 populations of tits (Parus sp.) from six European countries, we show that the phenological response to large-scale changes in spring temperature varies across a species' range, even between populations situated close to each other. We show that this variation cannot be fully explained by variation in the temperature change during the pre- and post-laying periods, as recently suggested. Instead, we find evidence for a link between rising temperatures and the frequency of second broods, which results in complex shifts in the laying dates of first clutches. Our results emphasize the need to consider links between different life-history parameters in order to predict the ecological consequences of large-scale climate changes.     

On the role of collective sensing and evolution in group formation

Collective sensing is an emergent phenomenon which enables individuals to estimate a hidden property of the environment through the observation of social interactions. Previous work on collective sensing shows that gregarious individuals obtain an evolutionary advantage by exploiting collective sensing when competing against solitary individuals. This work addresses the question of whether collective sensing allows for the emergence of groups from a population of individuals without predetermined behaviors. It is assumed that group membership does not lessen competition on the limited resources in the environment, e.g., groups do not improve foraging efficiency. Experiments are run in an agent-based evolutionary model of a foraging task, where the fitness of the agents depends on their foraging strategy. The foraging strategy of agents is determined by a neural network, which does not require explicit modeling of the environment and of the interactions between agents. Experiments demonstrate that gregarious behavior is not the evolutionary-fittest strategy if resources are abundant, thus invalidating previous findings in a specific region of the parameter space. In other words, resource scarcity makes gregarious behavior so valuable as to make up for the increased competition over the few available resources. Furthermore, it is shown that a population of solitary agents can evolve gregarious behavior in response to a sudden scarcity of resources, thus individuating a possible mechanism that leads to gregarious behavior in nature. The evolutionary process operates on the whole parameter space of the neural networks; hence, these behaviors are selected among an unconstrained set of behavioral models.     

Motor Demands Constrain Cognitive Rule Structures

Study of human executive function focuses on our ability to represent cognitive rules independently of stimulus or response modality. However, recent findings suggest that executive functions cannot be modularized separately from perceptual and motor systems, and that they instead scaffold on top of motor action selection. Here we investigate whether patterns of motor demands influence how participants choose to implement abstract rule structures. In a learning task that requires integrating two stimulus dimensions for determining appropriate responses, subjects typically structure the problem hierarchically, using one dimension to cue the task-set and the other to cue the response given the task-set. However, the choice of which dimension to use at each level can be arbitrary. We hypothesized that the specific structure subjects adopt would be constrained by the motor patterns afforded within each rule. Across four independent data-sets, we show that subjects create rule structures that afford motor clustering, preferring structures in which adjacent motor actions are valid within each task-set. In a fifth data-set using instructed rules, this bias was strong enough to counteract the well-known task switch-cost when instructions were incongruent with motor clustering. Computational simulations confirm that observed biases can be explained by leveraging overlap in cortical motor representations to improve outcome prediction and hence infer the structure to be learned. These results highlight the importance of sensorimotor constraints in abstract rule formation and shed light on why humans have strong biases to invent structure even when it does not exist.     

Artificial pheromone for path selection by a foraging swarm of robots

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.     

Simulating the evolution of signal transduction pathways

We use a generic model of a network of proteins that can activate or deactivate each other to explore the emergence and evolution of signal transduction networks and to gain a basic understanding of their general properties. Starting with a set of non-interacting proteins, we evolve a signal transduction network by random mutation and selection to fulfill a complex biological task. In order to validate this approach we base selection on a fitness function that captures the essential features of chemotactic behavior as seen in bacteria. We find that a system of as few as three proteins can evolve into a network mediating chemotaxis-like behavior by acting as a "derivative sensor". Furthermore, we find that the dynamics and topology of such networks show many similarities to the natural chemotaxis pathway, that the response magnitude can increase with increasing network size and that network behavior shows robustness towards variations in some of the internal parameters. We conclude that simulating the evolution of signal transduction networks to mediate a certain behavior may be a promising approach for understanding the general properties of the natural pathway for that behavior.     

From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.     

Value of service based resource management for large-scale computing systems

Task scheduling for large-scale computing systems is a challenging problem. From the users perspective, the main concern is the performance of the submitted tasks, whereas, for the cloud service providers, reducing operation cost while providing the required service is critical. Therefore, it is important for task scheduling mechanisms to balance users’ performance requirements and energy efficiency because energy consumption is one of the major operational costs. We present a time dependent value of service (VoS) metric that will be maximized by the scheduling algorithm that take into consideration the arrival time of a task while evaluating the value functions for completing a task at a given time and the tasks energy consumption. We consider the variation in value for completing a task at different times such that the value of energy reduction can change significantly between peak and non-peak periods. To determine the value of a task completion, we use completion time and energy consumption with soft and hard thresholds. We define the VoS for a given workload to be the sum of the values for all tasks that are executed during a given period of time. Our system model is based on virtual machines, where each task will be assigned a resource configuration characterized by the number of the homogeneous cores and amount of memory. For the scheduling of each task submitted to our system, we use the estimated time to compute matrix and the estimated energy consumption matrix which are created using historical data. We design, evaluate, and compare our task scheduling methods to show that a significant improvement in energy consumption can be achieved when considering time-of-use dependent scheduling algorithms. The simulation results show that we improve the performance and the energy values up to 49% when compared to schedulers that do not consider the value functions. Similar to the simulation results, our experimental results from running our value based scheduling on an IBM blade server show up to 82% improvement in performance value, 110% improvement in energy value, and up to 77% improvement in VoS compared to schedulers that do not consider the value functions.     

Tool use by the forest ant Aphaenogaster rudis: Ecology and task allocation

Debris dropping behavior by ants during foraging has been labeled alternately as tool use or a protective behavior. To address this controversy, we investigated the circumstances under which the common forest ant Aphaenogaster rudis drops and retrieves debris in the forests of Vermont, in the U.S.A. We tested the hypotheses, first, that debris dropping functions to protect workers from entanglement or drowning in liquids, and second, that debris dropping functions as part of foraging tool use. To determine how workers are allocated to the debris dropping and retrieval tasks, we studied individually marked foragers in the field and laboratory. Our results provide evidence that the debris dropping behavior of Aphaenogaster rudis deserves to be labeled as foraging tool use; A. rudis ants do not drop debris in non-food substances that present a hazard of entanglement or drowning to workers. We also found that potential tools represent a small, but non-negligible, percentage of the items that A. rudis foragers bring back to their colonies. Furthermore, debris dropping by A. rudis at baits discouraged colonization by other ant species. Finally, we provide the first evidence that tool use is a specialized task performed by a subset of A. rudis foragers within each colony at any given point in time. The execution of this task by a small proportion of workers may enhance the competitive ability of this ecologically dominant forest ant. Received 3 April 2006; revised 13 August 2006; accepted 1 September 2006.     

Ventilation response thresholds do not change with age or self-reinforcement in workers of the bumble bee Bombus impatiens

The response threshold model is a potential mechanism for task allocation in social insects, and it assumes that workers vary in the levels of task stimuli to which they respond. Furthermore, response thresholds of individual workers may change over time through self-reinforcement (experience), such that workers become more sensitive to task stimuli. However, in addition to self-reinforcement, aging is another process that occurs through time. Distinguishing whether response thresholds change within workers due to self-reinforcement or aging may give insight into the flexibility of this task allocation mechanism. Using a ventilation paradigm, we manipulated workers of Bombus impatiens to have either repeated or lack of exposures to increases in nest air temperature, thereby allowing us to manipulate experience and thus self-reinforcement. Nest air temperature was the task stimulus, and ventilation (fanning) was the behavioral response. We found that ventilation response thresholds do not decrease either with age or experience in workers of B. impatiens, contrary to what has been reported for B. terrestris workers (Weidenmüller, 2004). Instead, we found high levels of intra-individual variation in response thresholds. Our results also show that workers with lower average response thresholds respond to heating events with higher probability than those with higher ventilation thresholds. These results provide insight into the role of the response threshold framework for task allocation; we also discuss how response probabilities may play a role in task allocation among workers.     

Thresholds of Response in Nest Thermoregulation by Worker Bumble Bees, Bombus bifarius nearcticus (Hymenoptera: Apidae)

Regulation of nest temperature is important to the fitness of eusocial insect colonies. To maintain appropriate conditions for the developing brood, workers must exhibit thermoregulatory responses to ambient temperature. Because nest-mate workers differ in task performance, thermoregulatory behavior provides an opportunity to test threshold of response models for the regulation of division of labor. We found that worker bumble bees ( Bombus bifarius nearcticus ) responded to changes in ambient temperature by altering their rates of performing two tasks – wing fanning and brood cell incubation. At the colony level, the rate of incubating decreased, and the rate of fanning increased, with increasing temperature. Changes in the number of workers performing these tasks were more important to the colony response than changes in workers' task performance rates. At the individual level, workers' lifetime rates of incubation and fanning were positively correlated, and most individuals did not specialize exclusively on either of these temperature-sensitive tasks. However, workers differed in the maximum temperature at which they incubated and in the minimum temperature at which they fanned. More individuals fanned at high and incubated at low temperatures. Most of the workers that began incubating at higher temperatures continued performing this task at lower temperatures, when additional nest-mates became active. The converse was true for fanning behavior. These data are consistent with a threshold of response model for thermoregulatory behavior of B. bifarius workers.     

Green beards in the light of indirect genetic effects

The green‐beard effect is one proposed mechanism predicted to underpin the evolution of altruistic behavior. It relies on the recognition and the selective help of altruists to each other in order to promote and sustain altruistic behavior. However, this mechanism has often been dismissed as unlikely or uncommon, as it is assumed that both the signaling trait and altruistic trait need to be encoded by the same gene or through tightly linked genes. Here, we use models of indirect genetic effects (IGEs) to find the minimum correlation between the signaling and altruistic trait required for the evolution of the latter. We show that this correlation threshold depends on the strength of the interaction (influence of the green beard on the expression of the altruistic trait), as well as the costs and benefits of the altruistic behavior. We further show that this correlation does not necessarily have to be high and support our analytical results by simulations.     

Writing in the Air: Contributions of Finger Movement to Cognitive Processing

The present study investigated the interactions between motor action and cognitive processing with particular reference to kanji-culture individuals. Kanji-culture individuals often move their finger as if they are writing when they are solving cognitive tasks, for example, when they try to recall the spelling of English words. This behavior is called kusho, meaning air-writing in Japanese. However, its functional role is still unknown. To reveal the role of kusho behavior in cognitive processing, we conducted a series of experiments, employing two different cognitive tasks, a construction task and a stroke count task. To distinguish the effects of the kinetic aspects of kusho behavior, we set three hand conditions in the tasks; participants were instructed to use either kusho, unrelated finger movements or do nothing during the response time. To isolate possible visual effects, two visual conditions in which participants saw their hand and the other in which they did not, were introduced. We used the number of correct responses and response time as measures of the task performance. The results showed that kusho behavior has different functional roles in the two types of cognitive tasks. In the construction task, the visual feedback from finger movement facilitated identifying a character, whereas the kinetic feedback or motor commands for the behavior did not help to solve the task. In the stroke count task, by contrast, the kinetic aspects of the finger movements influenced counting performance depending on the type of the finger movement. Regardless of the visual condition, kusho behavior improved task performance and unrelated finger movements degraded it. These results indicated that motor behavior contributes to cognitive processes. We discussed possible mechanisms of the modality dependent contribution. These findings might lead to better understanding of the complex interaction between action and cognition in daily life.     

Risk sensitivity and theory of mind in human coordination

What humans do when exposed to uncertainty, incomplete information, and a dynamic environment influenced by other agents remains an open scientific challenge with important implications in both science and engineering applications. In these contexts, humans handle social situations by employing elaborate cognitive mechanisms such as theory of mind and risk sensitivity. Here we resort to a novel theoretical model, showing that both mechanisms leverage coordinated behaviors among self-regarding individuals. Particularly, we resort to cumulative prospect theory and level-k recursions to show how biases towards optimism and the capacity of planning ahead significantly increase coordinated, cooperative action. These results suggest that the reason why humans are good at coordination may stem from the fact that we are cognitively biased to do so.     

Multiple feedbacks and the prevalence of alternate stable states on coral reefs

The prevalence of alternate stable states on coral reefs has been disputed, although there is universal agreement that many reefs have experienced substantial losses of coral cover. Alternate stable states require a strong positive feedback that causes self-reinforcing runaway change when a threshold is passed. Here we use a simple model of the dynamics of corals, macroalgae and herbivores to illustrate that even weak positive feedbacks that individually cannot lead to alternate stable states can nonetheless do so if they act in concert and reinforce each other. Since the strength of feedbacks varies over time and space, our results imply that we should not reject or accept the general hypothesis that alternate stable states occur in coral reefs. Instead, it is plausible that shifts between alternate stable states can occur sporadically, or on some reefs but not others depending on local conditions. Therefore, we should aim at a better mechanistic understanding of when and why alternate stable states may occur. Our modelling results point to an urgent need to recognize, quantify, and understand feedbacks, and to reorient management interventions to focus more on the mechanisms that cause abrupt transitions between alternate states.