Modelling foraging ants in a dynamic and confined environment

In social insects, the superposition of simple individual behavioral rules leads to the emergence of complex collective patterns and helps solve difficult problems inherent to surviving in hostile habitats. Modelling ant colony foraging reveals strategies arising from the insects’ self-organization and helps develop of new computational strategies in order to solve complex problems. This paper presents advances in modelling ants’ behavior when foraging in a confined and dynamic environment, based on experiments with the Argentine ant Linepithema humile in a relatively complex artificial network. We propose a model which overcomes the problem of stagnation observed in earlier models by taking into account additional biological aspects, by using non-linear functions for the deposit, perception and evaporation of pheromone, and by introducing new mechanisms to represent randomness and the exploratory behavior of the ants.     

Path formation in a robot swarm

We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths in complex obstacle environments and exhibit very good scalability, robustness, and fault tolerance characteristics. Additionally, we observe that chains perform better for small robot group sizes, while vectorfield performs better for large groups.     

Balancing organization and flexibility in foraging dynamics

Proper pattern organization and reorganization are central problems facing many biological networks which thrive in fluctuating environments. However, in many cases the mechanisms that organize system activity oppose those that support behavioral flexibility. Thus, a balance between pattern organization and pattern flexibility is critically important for overall biological fitness. We study this balance in the foraging strategies of ant colonies exploiting food in dynamic environments. We present discrete time and space simulations of colony activity that uses a pheromone-based recruitment strategy biasing foraging towards a food source. After food relocation, the pheromone must evaporate sufficiently before foraging can shift colony attention to a new food source. The amount of food consumed within the dynamic environment depends non-monotonically on the pheromone evaporation time constant—with maximal consumption occurring at a time constant which balances trail formation and trail flexibility. A deterministic, ‘mean field’ model of pheromone and foragers on trails mimics our colony simulations. This reduced framework captures the essence of the flexibility-organization balance, and relates optimal pheromone evaporation to the timescale of the dynamic environment. We expect that the principles exposed in our study will generalize and motivate novel analysis across a broad range systems biology.     

Resource exploitation strategies in the presence of traffic between food sources

A mathematical model of food recruitment and resource exploitation in group-living organisms accounting for direct traffic of individuals between the available sources is developed. It is shown that traffic between sources gives rise to the enhancement of the range of stability of the homogeneous mode of exploitation and of the range of coexistence of homogeneous and semi-inhomogeneous ones, as well as the appearance of symmetry breaking transitions leading to fully inhomogeneous exploitation modes.     

Particle swarm optimization

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems.     

The emergence of synchronization behavior in Physarum polycephalum and its particle approximation

The regeneration process of contractile oscillation in the plasmodium of Physarum polycephalum is investigated experimentally and modelled computationally. When placed in a well, the Physarum cell restructures the body (fusion of small granule-like cells) and shows various complex oscillation patterns. After it completed the restructuring and regained synchronized oscillation within the body, the cell shows bilateral oscillation or rotating wave pattern. This regeneration process did not depend on the well size and all the cases tested here showed similar time course. Phase synchronization analysis based on Hilbert Transform also suggested that the cell can develop a fully synchronized oscillation within a fixed time no matter what the cell size is. A particle-based computational model was developed in order to model the emergence of oscillation patterns. Particles employing very simple and identical sensory and motor behaviors interacted with each other via the sensing and deposition of chemoattractants in a diffusive environment. From a random and almost homogeneous distribution, emergent domains of oscillatory activity emerged. By increasing the sensory radius the model simulated the regeneration process of the real plasmodium. In addition, the model replicated the rotating wave and bilateral oscillation pattern when the sensory radius was increased. The results suggest that complex emergent oscillatory behaviors (and thus the high-level systems which may utilize them, such as pumping and transport mechanisms) may be developed from simple materials inspired by Physarum slime mold.     

新型粒子群优化算法在多序列联配中的应用

将粒子群优化算法应用于序列联配,提出了一种改进的粒子群优化算法,该算法在粒子群的进化过程中根据粒子的适应值动态地调整粒子群的惯性权重与粒子群飞行速度范围,提高了算法的收敛速度和收敛精度;针对PSO算法可能出现的早熟现象,引入重新初始化机制,增强了算法的搜索能力,实验表明该算法是有效的。     

The autonomy of biological individuals and artificial models

This paper aims to offer an overview of the meaning of autonomy for biological individuals and artificial models rooted in a specific perspective that pays attention to the historical and structural aspects of its origins and evolution. Taking autopoiesis and the recursivity characteristic of its circular logic as a starting point, we depart from some of its consequences to claim that the theory of autonomy should also take into account historical and structural features. Autonomy should not be considered only in internal or constitutive terms, the largely neglected interactive aspects stemming from it should be equally addressed. Artificial models contribute to get a better understanding of the role of autonomy for life and the varieties of its organization and phenomenological diversity.     

The nature and evolution of intelligence in orangutans (Pongo pygmaeus)

Orangutans share many intellectual qualities with African great apes and humans, likely because of their recent common ancestry. They may also show unique intellectual adaptations because of their long evolutionary divergence from the African lineage. This paper assesses orangutan intelligence in light of this evolutionary history. Evidence derives from observations of juvenile ex-captive orangutans reintroduced to free forest life by the Wanariset Orangutan Reintroduction Project, East Kalimantan, Indonesia. The intellectual qualities shared by great apes and humans point to a distinct “great ape” intelligence with hierarchization as a pivotal cognitive mechanism. Evolutionary reconstructions jibe with this view and suggest that technically difficult foods may have been key selection pressures. Orangutans should then show hierarchical intelligence when obtaining difficult foods. Evidence on ex-captive orangutans' techniques for processing difficult foods concurs. Intellectual qualities distinct to orangutans may owe to arboreal travel pressures; in particular arboreality may aggravate foraging problems. Evidence confirms that ex-captive orangutans' techniques for accessing difficult foods located arboreally are intellectually complex—i.e. they show hierarchization. These findings suggest other factors probably important to understanding great ape and orangutan forms of intelligence and their evolutionary origins.     

Decision-making processes: The case of collective movements

Besides focusing on the adaptive significance of collective movements, it is crucial to study the mechanisms and dynamics of decision-making processes at the individual level underlying the higher-scale collective movements. It is now commonly admitted that collective decisions emerge from interactions between individuals, but how individual decisions are taken, i.e. how far they are modulated by the behaviour of other group members, is an under-investigated question. Classically, collective movements are viewed as the outcome of one individual's initiation (the leader) for departure, by which all or some of the other group members abide. Individuals assuming leadership have often been considered to hold a specific social status. This hierarchical or centralized control model has been challenged by recent theoretical and experimental findings, suggesting that leadership can be more distributed. Moreover, self-organized processes can account for collective movements in many different species, even in those that are characterized by high cognitive complexity. In this review, we point out that decision-making for moving collectively can be reached by a combination of different rules, i.e. individualized (based on inter-individual differences in physiology, energetic state, social status, etc.) and self-organized (based on simple response) ones for any species, context and group size.     

The evolution of brain size and intelligence in man

The brain size of hominids has increased approximately threefold during the evolution of the hominids fromAustralopithecus toHomo sapiens. It is proposed that the principal reason for this increase is that larger brains conferred greater intelligence, and greater intelligence conferred a selection advantage. A number of anthropologists have difficulty accepting this thesis because they believe that brain size is not associated with intelligence in man. Evidence is reviewed, and new evidence from two studies is presented, to show that brain size as measured by head size is positively correlated with intelligence as measured by intelligence tests. On two recent samples statistically significant correlations of .21 and .30 were obtained between estimates of brain size and IQ. It is considered that brain size is positively associated with intelligence in man and that this is the major reason for the increase in brain size of the hominids during the last 3.2 million years.     

Self-organisation and communication in groups of simulated and physical robots

In social insects, both self-organisation and communication play a crucial role for the accomplishment of many tasks at a collective level. Communication is performed with different modalities, which can be roughly classified into three classes: indirect (stigmergic) communication, direct interactions and direct communication. The use of stigmergic communication is predominant in social insects (e.g. the pheromone trails in ants), where, however, direct interactions (e.g. antennation in ants) and direct communication (e.g. the waggle dance in honey bees) can also be observed. Taking inspiration from insect societies, we present an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities. In particular, the robots are placed in an arena presenting holes and open borders, which they should avoid while moving coordinately. Artificial evolution is responsible for the synthesis in a simulated environment of the robot’s neural controllers, which are subsequently tested on physical robots. We study different communication strategies among the robots: no direct communication, handcrafted signalling and a completely evolved approach. We show that the latter is the most efficient, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies. Moreover, we show that the evolved controllers produce a self-organising system that is robust enough to be tested on physical robots, notwithstanding the huge gap between simulation and reality.Electronic Supplementary Material Supplementary material is available for this article at     

The evolution of animal 'cultures' and social intelligence

Decades-long field research has flowered into integrative studies that, together with experimental evidence for the requisite social learning capacities, have indicated a reliance on multiple traditions ('cultures') in a small number of species. It is increasingly evident that there is great variation in manifestations of social learning, tradition and culture among species, offering much scope for evolutionary analysis. Social learning has been identified in a range of vertebrate and invertebrate species, yet sustained traditions appear rarer, and the multiple traditions we call cultures are rarer still. Here, we examine relationships between this variation and both social intelligence--sophisticated information processing adapted to the social domain--and encephalization. First, we consider whether culture offers one particular confirmation of the social ('Machiavellian') intelligence hypothesis that certain kinds of social life (here, culture) select for intelligence: 'you need to be smart to sustain culture'. Phylogenetic comparisons, particularly focusing on our own study animals, the great apes, support this, but we also highlight some paradoxes in a broader taxonomic survey. Second, we use intraspecific variation to address the converse hypothesis that 'culture makes you smart', concluding that recent evidence for both chimpanzees and orangutans support this proposition.     

The effects of juvenile hormone on Lasius niger reproduction

Reproduction has been shown to be costly for survival in a wide diversity of taxa. The resulting trade-off, termed the reproduction-survival trade-off, is thought to be one of the most fundamental forces of life-history evolution. In insects the pleiotropic effect of juvenile hormone (JH), antagonistically regulating reproduction and pathogen resistance, is suggested to underlie this phenomenon. In contrast to the majority of insects, reproductive individuals in many eusocial insects defy this trade-off and live both long and prosper. By remodelling the gonadotropic effects of JH in reproductive regulation, the queens of the long-lived black garden ant Lasius niger (living up to 27 years), have circumvented the reproduction-survival trade off enabling them to maximize both reproduction and pathogen resistance simultaneously. In this study we measure fertility, vitellogenin gene expression and protein levels after experimental manipulation of hormone levels. We use these measurements to investigate the mechanistic basis of endocrinological role remodelling in reproduction and determine how JH suppresses reproduction in this species, rather then stimulating it, like in the majority of insects. We find that JH likely inhibits three key aspects of reproduction both during vitellogenesis and oogenesis, including two previously unknown mechanisms. In addition, we document that juvenile hormone, as in the majority of insects, has retained some stimulatory function in regulating vitellogenin expression. We discuss the evolutionary consequences of this complex regulatory architecture of reproduction in L. niger, which might enable the evolution of similar reproductive phenotypes by alternate regulatory pathways, and the surprising flexibility regulatory role of juvenile hormone in this process.     

The biological effects of high pressures: underlying principles

Physico-chemical principles set constraints on the response of biological systems to high-pressure gases and to pressure per se. They also indicate the mechanisms that may be involved. Classical thermodynamics, intermolecular forces and the theory of solutions and many other areas of physical chemistry have contributed to our understanding of the problems faced by divers breathing gases at high pressure, in particular the high pressure neurological syndrome, inert gas narcosis and decompression sickness. The value and the limitations of physico-chemical arguments when applied to the problems of underwater physiology are analysed.     

The uniqueness of biological self-organization: challenging the Darwinian paradigm

Here we discuss the challenge posed by self-organization to the Darwinian conception of evolution. As we point out, natural selection can only be the major creative agency in evolution if all or most of the adaptive complexity manifest in living organisms is built up over many generations by the cumulative selection of naturally occurring small, random mutations or variants, i.e., additive, incremental steps over an extended period of time. Biological self-organization—witnessed classically in the folding of a protein, or in the formation of the cell membrane—is a fundamentally different means of generating complexity. We agree that self-organizing systems may be fine-tuned by selection and that self-organization may be therefore considered a complementary mechanism to natural selection as a causal agency in the evolution of life. But we argue that if self-organization proves to be a common mechanism for the generation of adaptive order from the molecular to the organismic level, then this will greatly undermine the Darwinian claim that natural selection is the major creative agency in evolution. We also point out that although complex self-organizing systems are easy to create in the electronic realm of cellular automata, to date translating in silico simulations into real material structures that self-organize into complex forms from local interactions between their constituents has not proved easy. This suggests that self-organizing systems analogous to those utilized by biological systems are at least rare and may indeed represent, as pre-Darwinists believed, a unique ascending hierarchy of natural forms. Such a unique adaptive hierarchy would pose another major challenge to the current Darwinian view of evolution, as it would mean the basic forms of life are necessary features of the order of nature and that the major pathways of evolution are determined by physical law, or more specifically by the self-organizing properties of biomatter, rather than natural selection.     

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

Groups of organisms, from bacteria to fish schools to human societies, depend on their ability to make accurate decisions in an uncertain world. Most models of collective decision-making assume that groups reach a consensus during a decision-making bout, often through simple majority rule. In many natural and sociological systems, however, groups may fail to reach consensus, resulting in stalemates. Here, we build on opinion dynamics and collective wisdom models to examine how stalemates may affect the wisdom of crowds. For simple environments, where individuals have access to independent sources of information, we find that stalemates improve collective accuracy by selectively filtering out incorrect decisions (an effect we call stalemate filtering). In complex environments, where individuals have access to both shared and independent information, this effect is even more pronounced, restoring the wisdom of crowds in regions of parameter space where large groups perform poorly when making decisions using majority rule. We identify network properties that tune the system between consensus and accuracy, providing mechanisms by which animals, or evolution, could dynamically adjust the collective decision-making process in response to the reward structure of the possible outcomes. Overall, these results highlight the adaptive potential of stalemate filtering for improving the decision-making abilities of group-living animals.     

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting. In addition, cytopathologists should ensure that these algorithms are developed, trained, tested and deployed in an ethical manner. Cytopathologists need to become informed consumers of these AI algorithms by understanding their workings and limitations, how their performance is assessed and how to validate and verify their output in clinical practice.