Interior as Ecosystem

We consider the synthesis of control policies from temporal logic specifications for robots that interact with multiple dynamic environment agents. Each environment agent is modeled by a Markov chain whereas the robot is modeled by a finite transition system (in the deterministic case) or Markov decision process (in the stochastic case). Existing results in probabilistic verification are adapted to solve the synthesis problem. To partially address the state explosion issue, we propose an incremental approach where only a small subset of environment agents is incorporated in the synthesis procedure initially and more agents are successively added until we hit the constraints on computational resources. Our algorithm runs in an anytime fashion where the probability that the robot satisfies its specification increases as the algorithm progresses.

Explosive synchronization through dynamical environment

We report the emergence of stable amplitude chimeras and chimera death in a two-layer network where one layer has an ensemble of identical nonlinear oscillators interacting directly through local coupling and indirectly through dynamic agents that form the second layer. The nonlocality in the interaction among the dynamic agents in the second layer induces different types of chimera-related dynamical states in the first layer. The amplitude chimeras developed in them are found to be extremely stable, while chimera death states are prevalent for increased coupling strengths. The results presented are for a system of coupled Stuart-Landau oscillators and can, in general, represent systems with short-range interactions coupled to another set of systems with long-range interactions. In this case, by tuning the range of interactions among the oscillators or the coupling strength between two types of systems, we can control the nature of chimera states and the system can also be restored to homogeneous steady states. The dynamic agents interacting nonlocally with long-range interactions can be considered as a dynamic environment or a medium interacting with the system. We indicate how the second layer can act as a reinforcement mechanism on the first layer under various possible interactions for desirable effects.

In recent years, living conditions of a population have changed rapidly due to complex social, economic, cultural, political and other processes. This naturally leads not only to a transformation of the system of values, motivation, worldview, but also an entire way of life. High rates of these changes simultaneously led a delegation given, on the one hand, a diversity of forms of response behavior, from the other – their gradual integration into the socio-economic relations in a search result models of individual and collective adaptation to a new dynamic environment, characterized by high uncertainty and heterogeneity. Thus, the purpose of this study is to identify and describe the main models of population adaptation on an interdisciplinary basis, not limited to an analysis of complex processes within a single field of knowledge. In this regard, the task is to find a way to harmonize (coordinate) different points of view on a nature of a single phenomenon. The object of our research is a variety of adaptation processes of a population to dynamically changing conditions of its life. At the same time, the subject should be a wide range of different types of interactions between a population and an external environment, as well as a population “within itself”, as part of the model of its adaptation to changing conditions. To achieve the goal and solve problems, a set of methods that complement each other is used, namely: theoretical analysis and synthesis, systematization and comparative analysis. As a result, it was found that population adaptation is a rather complex phenomenon, the study of which goes far beyond the formal framework of any one field of knowledge, being at their intersection, which requires the search for new approaches that allow integrating explanations of the nature of phenomena that are currently interpreted within their paradigms. The article presents the main types of models of population adaptation to dynamically changing socio-economic conditions of life, their features, identity and differences, as well as areas of application

Beamforming plays a key role in improving the spectrum utilization efficiency of multi-antenna systems. However, we observe that (i) conventional beam prediction solutions suffer from high model training overhead and computational latency and thus cannot adapt quickly to changing wireless environments, and (ii) deep-learning-based beamforming may face the risk of catastrophic oblivion in dynamically changing environments, which can significantly degrade system performance. Inspired by the above challenges, we propose a continuous-learning-inspired beam prediction model for fast beamforming adaptation in dynamic downlink millimeter-wave (mmWave) communications. More specifically, we develop a meta-empirical replay (MER)-based beam prediction model. It combines empirical replay and optimization-based meta-learning. This approach optimizes the trade-offs between transmission and interference in dynamic environments, enabling effective fast beamforming adaptation. Finally, the high-performance gains brought by the proposed model in dynamic communication environments are verified through simulations. The simulation results show that our proposed model not only maintains a high-performance memory for old tasks but also adapts quickly to new tasks.

Collaborative monitoring of large infrastructures, such as military, transportation and maritime systems are decisive issues in many surveillance, protection, and security applications. In many of these applications, dynamic multi-agent systems using reinforcement learning for agents’ autonomous path planning, where agents could be moving randomly to reach their respective goals and avoiding topographical obstacles intelligently, becomes a challenging problem. This is specially so in a dynamic agent environment. In our prior work we presented an intelligent multi-agent hybrid reactive and reinforcement learning technique for collaborative autonomous agent path planning for monitoring Critical Key Infrastructures and Resources (CKIR) in a geographically and a computationally distributed systems. Here agent monitoring of large environments is reduced to monitoring of relatively smaller track-able geographically distributed agent environment regions. In this paper we tackle this problem in the challenging case of complex and cluttered environments, where agents’ initial random-walk paths become challenging and relatively nonconverging. Here we propose a multi-agent distributed hybrid reactive re-enforcement learning technique based on selected agent intermediary sub-goals using a learning reward scheme in a distributed-computing memory setting. Various case study scenarios are presented for convergence study to the shortest minimum-amount-of-time exploratory steps for faster and efficient agent learning. In this work the distributed dynamic agent communication is done via a Message Passing Interface (MPI).

A crucial requirement for service robots is to be able to move in dynamic environments shared with humans as well as interact with them. Navigation in such environments is a challenging task, as the environment is constantly changing, future states have to be predicted and planning and execution must be carried on-line. However, even in very complex situations, humans can easily find a path that avoid both dynamic agents and static obstacles. This paper proposes a technique to take advantage of the human movement in such populated environments, using a probabilistic approach for the leader selection, according to the robot's desired destination. By choosing a leader to be followed in dynamic environments, the robot can take advantage of the paths traveled by humans or other robots, effortlessly avoiding dynamic and static features as its leader does, relieving the robot from the burden of having to generate its own path. Both the leader selection and the leader following algorithms have been tested in a real environment, with a robotic wheelchair.

A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or overconservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.

This research aims to apply the application of Deconstruction to the Aesthetic Dimensions of Dance as presented to the people of Riau with a focus on criticism of contemporary dance postures. Based on the theory of Deconstruction introduced by Jacques Derrida, this research aims to deconstruct the meaning and structure of traditional dance in a contemporary context. Qualitative research methods were used through indirect observation and content analysis of dance performances in Riau. It is hoped that this research will provide a deeper understanding of how the concept of Deconstruction can be applied in studying and strengthening the aesthetic aspects of traditional Riau dance art in a modern contemporary context. The results of this research show that various symbolic meanings are revealed in gender, ecological perspectives, respect for nature, ethics and manners, through dance movements, props used, decoration and clothing, up to the design of the seven floors. All components in the Malay worship dance have a deep meaning and certain complexity. This article contributes to enriching the cultural treasures behind dance which presents contemporary Riau and the deep meaning behind it.

One of the well-studied issues in multi-agent systems is the standard action-selection and sequencing problem where a goal task can be performed in different ways, by different agents.Tasks have constraints while agents have different characteristics such as capacity, access to resources, motivations, etc. This class of problems has been tackled under different approaches. Moreover, in open, dynamic environments, agents must be able to adapt to the changing organizational goals, available resources, their relationships to another agents, and so on. This problem is a key one in multi-agent systems and relates to models of learning and adaptation, such as those observed among social insects. The present paper tackles the process of generating, adapting, and changing multiagent organization dynamically at system runtime, using a swarm inspired approach. This approach is used here mainly for task allocation with low need of pre-planning and specification, and no need of explicit coordination. The results of our approach and another quantitative one are compared here and it is shown that in dynamic domains, the agents adapt to changes in the organization, just as social insects do.

Motor adaptation maintains movement accuracy. To evaluate movement accuracy, motor adaptation relies on an error signal, generated by the movement target, while suppressing error signals from irrelevant objects in the vicinity. Previous work used static testing environments, where all information required to evaluate movement accuracy was available simultaneously. Using saccadic eye movements as a model for motor adaptation, we tested how movement accuracy is maintained in dynamic environments, where the availability of conflicting error signals varied over time. Participants made a vertical saccade toward a target (either a small square or a large ring). Upon saccade detection, two candidate stimuli were shown left and right of the target, and participants were instructed to discriminate a feature on one of the candidates. Critically, candidate stimuli were presented sequentially, and saccade adaptation, thus, had to resolve a conflict between a task-relevant and a task-irrelevant error signal that were separated in space and time. We found that the saccade target influenced several aspects of oculomotor learning. In presence of a small target, saccade adaptation evaluated movement accuracy based on the first available error signal after the saccade, irrespective of its task relevance. However, a large target not only allowed for greater flexibility when evaluating movement accuracy, but it also promoted a stronger contribution of strategic behavior when compensating inaccurate saccades. Our results demonstrate how motor adaptation maintains movement accuracy in dynamic environments, and how properties of the visual environment modulate the relative contribution of different learning processes.NEW & NOTEWORTHY Motor adaptation is typically studied in static environments, where all information that is required to evaluate movement accuracy is available simultaneously. Here, using saccadic eye movements as a model, we studied motor adaptation in a dynamic environment, where the availability of conflicting information about movement accuracy varied over time. We demonstrate that properties of the visual environment determine how dynamic movement errors are corrected.

Exposure of yellow-legged gulls to Toxoplasma gondii along the Western Mediterranean coasts: Tales from a sentinel.

Multiple UAVs have been extensively deployed recently to reduce human workload, resulting in increased automation and efficiency. Path planning of numerous UAVs is a challenging optimization problem and a key component in various applications. Traditional strategies cannot provide accurate, optimal solutions rapidly in complex mission settings. In this context, flocks of birds exhibit intricate patterns of group escape when faced with predators. Local group interactions may lead to the autonomy of these patterns. However, most nature-inspired intelligent planning techniques have slow search speeds and easily fall into local areas. An intelligent planning method emulating the behavior of pigeons to achieve intelligence, safety, and consistency in UAV flocks in a complicated environment is designed. The combinatorial approach of pigeon-inspired optimization and transfer learning (TL-PIO) is the focus of the multi-objective optimization task. On the one hand, path planning and formation control of individual clusters with a dynamic agent are dealt with combinatorial efforts of multi-agent systems (MAS) and flocking model. On the other hand, swapping and synchronization of individual clusters construct flocks in a dynamic environment. Specifically, interaction and swapping positions of the best members among all clusters are involved to plan optimized paths and configure agents in one flock. Experimental results have been validated through a detailed numerical analysis of proposed algorithm over other combinatorial approaches, namely social learning pigeon-inspired optimization (SL-PIO), social learning particle swarm optimization (SL-PSO), and social learning ant colony optimization (SL-ACO). TL-PIO achieves an improvement of 25% over SL-PIO and 18% over SL-ACO in seven test functions and 15% over SL-PSO but only in five test functions. Outcomes reveal the developed approach has the fastest convergence rate and high local optimal avoidance and exploration ability, significantly reducing costs and illustrating supremacy over other methods. The presented work practically implies researchers and practitioners adopt it for distinct benefits in real-world applications.

Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used ego-centric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset. The results demonstrate that the allo-centric grid representation significantly improves scene prediction, in comparison to the conventional ego-centric grid approach.

ATALK: A decentralized agent platform for engineering open and dynamic organizations