Continuous learning of emergent behavior in robotic matter.

One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic platform assembled from identical autonomous units that continuously sense their environment and react to it. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. We show that the physical connection between the units is enough to achieve learning, and no additional communication or centralized information is required. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular "robotic matter" that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.

Stochasticity of Biological Soft Matter: Emerging Concepts in Intrinsically Disordered Proteins and Biological Phase Separation.

At the turn of this century, cardinal changes took place in the perceptions of the structure and function of proteins, as well as in the organizational principles of membrane-less organelles. As a result, the model of the organization of living matter is changing to one described by highly dynamic biological soft matter positioned at the edge of chaos. Intrinsically disordered proteins (IDPs) and membrane-less organelles are key examples of this new outlook and may represent a critical foundation of life, defining its complexity and the evolution of living things.

The emergence of lines of hierarchy in collective motion of biological systems.

The emergence of large-scale structures in biological systems, and in particular the formation of lines of hierarchy, is observed at many scales, from collections of cells to groups of insects to herds of animals. Motivated by phenomena in chemotaxis and phototaxis, we present a new class of alignment models that exhibit alignment into lines. The spontaneous formation of such 'fingers' can be interpreted as the emergence of leaders and followers in a system of identically interacting agents. Various numerical examples are provided, which demonstrate emergent behaviors similar to the 'fingering' phenomenon observed in some phototaxis and chemotaxis experiments; this phenomenon is generally known to be a challenging pattern for existing models to capture. A novel protocol for pairwise interactions provides a fundamental alignment mechanism by which agents may form lines of hierarchy across a wide range of biological systems.

The power of weak, transient interactions across biology: A paradigm of emergent behavior.

A growing list of diverse biological systems and their equally diverse functionalities provides realizations of a paradigm of emergent behavior. In each of these biological systems, pervasive ensembles of weak, short-lived, spatially local interactions act autonomously to convey functionalities at larger spatial and temporal scales. In this article, a range of diverse systems and functionalities are presented in a cursory manner with literature citations for further details. Then two systems and their properties are discussed in more detail: yeast chromosome biology and human respiratory mucus.

Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian.

BACKGROUND AND OBJECTIVE: Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process. METHODS: Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. RESULTS: A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the "energy" involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. CONCLUSIONS: The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories.

Spontaneous circulation of active microtubules confined by optical traps.

We propose an experiment to demonstrate spontaneous ordering and symmetry breaking of kinesin-driven microtubules confined to an optical trap. Calculations involving the feasibility of such an experiment are first performed which analyze the power needed to confine microtubules and address heating concerns. We then present the results of first-principles simulations of active microtubules confined in such a trap and analyze the types of motion observed by the microtubules as well as the velocity of the surrounding fluid, both near the trap and in the far-field. We find three distinct phases characterized by breaking of distinct symmetries and also analyze the power spectrum of the angular momenta of polymers to further quantify the differences between these phases. Under the correct conditions, microtubules were found to spontaneously align with one another and circle the trap in one direction.

Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development.

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell-cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

A Design of the Resilient Enterprise: A Reference Architecture for Emergent Behaviors Control.

The sooner disruptive emergent behaviors are detected, the sooner preventive measures can be taken to ensure the resilience of business processes execution. Therefore, organizations need to prepare for emergent behaviors by embedding corrective control mechanisms, which help coordinate organization-wide behavior (and goals) with the behavior of local autonomous entities. Ongoing technological advances, brought by the Industry 4.0 and cyber-physical systems of systems paradigms, can support integration within complex enterprises, such as supply chains. In this paper, we propose a reference enterprise architecture for the detection and monitoring of emergent behaviors in enterprises. We focus on addressing the need for an adequate reaction to disruptions. Based on a systematic review of the literature on the topic of current architectural designs for understanding emergent behaviors, we distill architectural requirements. Our architecture is a hybrid as it combines distributed autonomous business logic (expressed in terms of simple business rules) and some central control mechanisms. We exemplify the instantiation and use of this architecture by means of a proof-of-concept implementation, using a multimodal logistics case study. The obtained results provide a basis for achieving supply chain resilience "by design", i.e., through the design of coordination mechanisms that are well equipped to absorb and compensate for the effects of emergent disruptive behaviors.

An update on memory formation and retrieval: An engram-centric approach.

OBJECTIVE: We explore here that memory loss observed in the early stage of Alzheimer's disease (AD) is a disorder of memory retrieval, instead of a storage impairment. This engram-centric explanation aims to enlarge the conceptual frame of memory as an emergent behavior of the brain and to propose a new treatment strategy for memory retrieval in dementia-AD. BACKGROUND: The conventional memory hypothesis suggests that memory is stored as multiple traces in hippocampal neurons but recent evidence indicates that there are specialized memory engrams responsible for the storage and the retrieval of different memory types. UPDATED MEMORY HYPOTHESIS: There are specialized memory engram neurons for each memory type and when information will be stored as a memory arrives in the hippocampus through afferent neurons finds its neuron according to the excitability states of engram neurons. The excitability level in engram neurons seems like a code canalizing the interactions between engrams and information. Therefore, to enhance the excitability of memory engram neurons improves memory loss observed in AD. In addition, we suggest that the hippocampus creates an index for information stored in memory engram cells in specialized regions for different types of memory, instead of storing all information; and different anatomic locations of engram cells and their roles in memory retrieval point out that memory could be an emergent behavior of the brain, and the interaction between serotonin fluctuation and engram neurons could be neural underpinnings of terminal lucidity. MAJOR CHALLENGES FOR THE MODEL: The major challenge for this engram-centric memory retrieval model is the translation from bench to patient, specifically the delivery of optogenetic tools in patients. Engram neurons can be specifically activated by optogenetic tools, but optogenetics is an invasive technique which requires optic fiber implantation into the brain. In addition, light can overheat the tissue and thus induce damage in tissue. Furthermore, light is a foreign object and its direct implantation into the brain may cause neuroinflammation, the main trigger of neurodegenerative diseases. Therefore, to test the engram hypothesis in human, new tools to allow specific engram activation should be discovered.

Effects of load mass and size on cooperative transport in ants over multiple transport challenges.

Some ant species cooperatively transport a wide range of extremely large, heavy food objects of various shapes and materials. While previous studies have examined how object mass and size affect the recruitment of additional workers, less is understood about how these attributes affect the rest of the transport process. Using artificial baits with independently varying mass and size, we reveal their effects on cooperative transport in Paratrechina longicornis across two transport challenges: movement initiation and obstacle navigation. As expected, object mass was tightly correlated with number of porters as workers adjust group size to the task. Mass affected performance similarly across the two challenges, with groups carrying heavy objects having lower performance. Yet, object size had differing effects depending on the challenge. While larger objects led to reduced performance during movement initiation - groups took longer to start moving these objects and had lower velocities - there was no evidence for this during obstacle navigation, and the opposite pattern was weakly supported. If a group struggles to start moving an object, it does not necessarily predict difficulty navigating around obstacles; groups should persist in trying to move 'difficult' objects, which may be easier to transport later in the process. Additionally, groups hitting obstacles were not substantially disrupted, and started moving again sooner than at the start, despite the nest direction being blocked. Paratrechina longicornis transport groups never failed, performing well at both challenges while carrying widely varying objects, and even transported a bait weighing 1900 times the mass of an individual.

Emergent oscillations assist obstacle negotiation during ant cooperative transport.

Collective motion by animal groups is affected by internal interactions, external constraints, and the influx of information. A quantitative understanding of how these different factors give rise to different modes of collective motion is, at present, lacking. Here, we study how ants that cooperatively transport a large food item react to an obstacle blocking their path. Combining experiments with a statistical physics model of mechanically coupled active agents, we show that the constraint induces a deterministic collective oscillatory mode that facilitates obstacle circumvention. We provide direct experimental evidence, backed by theory, that this motion is an emergent group effect that does not require any behavioral changes at the individual level. We trace these relaxation oscillations to the interplay between two forces; informed ants pull the load toward the nest whereas uninformed ants contribute to the motion's persistence along the tangential direction. The model's predictions that oscillations appear above a critical system size, that the group can spontaneously transition into its ordered phase, and that the system can exhibit complete rotations are all verified experimentally. We expect that similar oscillatory modes emerge in collective motion scenarios where the structure of the environment imposes conflicts between individually held information and the group's tendency for cohesiveness.

Intelligent judgements over health risks in a spatial agent-based model.

BACKGROUND: Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. METHODS: We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). RESULTS: We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. CONCLUSIONS: Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.

Nonautonomous contact guidance signaling during collective cell migration.

Directed migration of groups of cells is a critical aspect of tissue morphogenesis that ensures proper tissue organization and, consequently, function. Cells moving in groups, unlike single cells, must coordinate their migratory behavior to maintain tissue integrity. During directed migration, cells are guided by a combination of mechanical and chemical cues presented by neighboring cells and the surrounding extracellular matrix. One important class of signals that guide cell migration includes topographic cues. Although the contact guidance response of individual cells to topographic cues has been extensively characterized, little is known about the response of groups of cells to topographic cues, the impact of such cues on cell-cell coordination within groups, and the transmission of nonautonomous contact guidance information between neighboring cells. Here, we explore these phenomena by quantifying the migratory response of confluent monolayers of epithelial and fibroblast cells to contact guidance cues provided by grooved topography. We show that, in both sparse clusters and confluent sheets, individual cells are contact-guided by grooves and show more coordinated behavior on grooved versus flat substrates. Furthermore, we demonstrate both in vitro and in silico that the guidance signal provided by a groove can propagate between neighboring cells in a confluent monolayer, and that the distance over which signal propagation occurs is not significantly influenced by the strength of cell-cell junctions but is an emergent property, similar to cellular streaming, triggered by mechanical exclusion interactions within the collective system.

Why the heart is like an orchestra and the uterus is like a soccer crowd.

The human uterus has no pacemaker or motor innervation, yet develops rhythmic, powerful contractions that increase intrauterine pressure to dilate the cervix and force the fetus through the pelvis. To achieve the synchronous contractions required for labor, the muscle cells of the uterus act as independent oscillators that become increasingly coupled by gap junctions toward the end of pregnancy. The oscillations are facilitated by changes in resting membrane potential that occur as pregnancy progresses. Reductions of potassium channels in the myocyte membranes in late pregnancy prolong myocyte action potentials, further facilitating transmission of signals and recruitment of neighboring myocytes. Late in pregnancy prostaglandin production increases leading to increased myocyte excitability. Also late in pregnancy myocyte actin polymerizes allowing actin-myosin interactions that generate force, following myocyte depolarization, calcium entry, and activation of myosin kinase. Labor occurs as a consequence of the combination of increased myocyte to myocyte connectivity, increased depolarizations that last longer, and activated intracellular contractile machinery. During labor the synchronous contractions of muscle cells raise intrauterine pressure to dilate the cervix in a process distinct from peristalsis. The synchronous contractions occur in a progressively larger region of the uterine wall. As the size of the region increases with increasing connectivity, the contraction of that larger area leads to an increase in intrauterine pressure. The resulting increased wall tension causes myocyte depolarization in other parts of the uterus, generating widespread synchronous activity and increased force as more linked regions are recruited into the contraction. The emergent behavior of the uterus has parallels in the behavior of crowds at soccer matches that sing together without a conductor. This contrasts with the behavior of the heart where sequential contractions are regulated by a pacemaker in a similar way to the actions of a conductor and an orchestra.

A six-compartment model for COVID-19 with transmission dynamics and public health strategies.

The global crisis of the COVID-19 pandemic has highlighted the need for mathematical models to inform public health strategies. The present study introduces a novel six-compartment epidemiological model that uniquely incorporates a higher isolation rate for unreported symptomatic cases of COVID-19 compared to reported cases, aiming to enhance prediction accuracy and address the challenge of initial underreporting. Additionally, we employ optimal control theory to assess the cost-effectiveness of interventions and adapt these strategies to specific epidemiological scenarios, such as varying transmission rates and the presence of asymptomatic carriers. By applying this model to COVID-19 data from India (30 January 2020 to 24 November 2020), chosen to capture the initial outbreak and subsequent waves, we calculate a basic reproduction number of 2.147, indicating the high transmissibility of the virus during this period in India. A sensitivity analysis reveals the critical impact of detection rates and isolation measures on disease progression, showing the robustness of our model in estimating the basic reproduction number. Through optimal control simulations, we demonstrate that increasing isolation rates for unreported cases and enhancing detection reduces the spread of COVID-19. Furthermore, our cost-effectiveness analysis establishes that a combined strategy of isolation and treatment is both more effective and economically viable. This research offers novel insights into the efficacy of non-pharmaceutical interventions, providing a tool for strategizing public health interventions and advancing our understanding of infectious disease dynamics.

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics.

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism's traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

Hierarchical adaptive nanostructured PVD coatings for extreme tribological applications: the quest for nonequilibrium states and emergent behavior.

Adaptive wear-resistant coatings produced by physical vapor deposition (PVD) are a relatively new generation of coatings which are attracting attention in the development of nanostructured materials for extreme tribological applications. An excellent example of such extreme operating conditions is high performance machining of hard-to-cut materials. The adaptive characteristics of such coatings develop fully during interaction with the severe environment. Modern adaptive coatings could be regarded as hierarchical surface-engineered nanostructural materials. They exhibit dynamic hierarchy on two major structural scales: (a) nanoscale surface layers of protective tribofilms generated during friction and (b) an underlying nano/microscaled layer. The tribofilms are responsible for some critical nanoscale effects that strongly impact the wear resistance of adaptive coatings. A new direction in nanomaterial research is discussed: compositional and microstructural optimization of the dynamically regenerating nanoscaled tribofilms on the surface of the adaptive coatings during friction. In this review we demonstrate the correlation between the microstructure, physical, chemical and micromechanical properties of hard coatings in their dynamic interaction (adaptation) with environment and the involvement of complex natural processes associated with self-organization during friction. Major physical, chemical and mechanical characteristics of the adaptive coating, which play a significant role in its operating properties, such as enhanced mass transfer, and the ability of the layer to provide dissipation and accumulation of frictional energy during operation are presented as well. Strategies for adaptive nanostructural coating design that enhance beneficial natural processes are outlined. The coatings exhibit emergent behavior during operation when their improved features work as a whole. In this way, as higher-ordered systems, they achieve multifunctionality and high wear resistance under extreme tribological conditions.

Teaching a small foreign language vocabulary to children using tact and listener instruction with a prompt delay.

This study consisted of a systematic replication of previous research examining the effects of tact and listener instruction on the emergence of native-to-foreign (NF) and foreign-to-native (FN) intraverbals in children who had experienced difficulties learning to read and write. We assigned different sets of stimuli to tact and listener conditions, and taught 4 children to tact or respond as listeners in a foreign language using a progressive prompt delay with differential reinforcement. All participants mastered tacts and listener responses in the foreign language. For all participants, tact instruction yielded greater emergence of intraverbals compared to listener instruction. Tact instruction also produced all possible bidirectional (NF and FN) intraverbals relations for 3 of 4 participants, but listener instruction never resulted in the emergence of all possible relations. These results replicate previous findings suggesting that tact instruction is a more efficient way to teach a foreign language and extend them to progressive prompt-delay procedures.

Distributed cognition for collaboration between human drivers and self-driving cars.

This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities-the human and the autonomous agent-that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the rider-horse metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.

Phototaxis in Cyanobacteria: From Mutants to Models of Collective Behavior.

Cyanobacteria rely on photosynthesis, and thus have evolved complex responses to light. These include phototaxis, the ability of cells to sense light direction and move towards or away from it. Analysis of mutants has demonstrated that phototaxis requires the coordination of multiple photoreceptors and signal transduction networks. The output of these networks is relayed to type IV pili (T4P) that attach to and exert forces on surfaces or other neighboring cells to drive "twitching" or "gliding" motility. This, along with the extrusion of polysaccharides or "slime" by cells, facilitates the emergence of group behavior. We evaluate recent models that describe the emergence of collective colony-scale behavior from the responses of individual, interacting cells. We highlight the advantages of "active matter" approaches in the study of bacterial communities, discussing key differences between emergent behavior in cyanobacterial phototaxis and similar behavior in chemotaxis or quorum sensing.