Behaviour Flexibility in Dynamic and Unpredictable Environments: The ICagent Approach

Firm performance in dynamic environments: The role of operational slack and operational scope

Enhanced cognitive ability is beneficial in unpredictable and harsh environments, as it enables animals to respond with flexibility. For animals living in urbanized areas, local environments not only are altered but can rapidly change during their lifetime. Urban residents are therefore challenged with identifying novel dangers and safe refuges in dynamic environments. We demonstrate that the tropical agamid lizard Psammophilus dorsalis experiences dramatically different habitats not only across the rural to urban spatial scale but also over the short temporal scale of a few years in suburban areas. Differences in environmental stability are expected to affect rates of learning and reversal learning in resident lizards. In testing arenas, lizards from these populations were required to choose a designated 'safe' refuge instead of an 'unsafe' one after simulated predator attacks. The contingency for safety was switched during the reversal learning task. In general, P. dorsalis showed high rates of learning and reversal learning, but lizards from suburban areas were quicker to learn and unlearn the location of the safe refuge than those from rural areas. This demonstrates for the first time to our knowledge that suburban lizards have faster learning and reversal learning skills for a key survival-related behaviour, finding safety in unpredictable environments.

This article focuses on achieving efficient and safe navigation for robots in dynamic and unpredictable environments. We propose a hierarchical path planning framework that integrates global path planning with local dynamic obstacle avoidance. This framework aims to quickly plan a collision-free, shortest, and safest path for the robot while adapting the navigation path according to uncertainties in the operating environment. Global path planning is conducted using the improved gray wolf optimization algorithm (IGWO), trajectory tracking is achieved through a pure pursuit control algorithm, and a dynamic switching mechanism based on deep reinforcement learning (DRL) significantly enhances the navigation performance of the robotic system. The effectiveness of this approach has been verified through simulations and experiments. Simulation results indicate that in global path planning, the IGWO algorithm achieves faster convergence compared to algorithms, such as GWO-MP and GWO-CS. The planned path lengths are reduced by approximately 4.01% and 2.27%, respectively, and the fitness values are decreased by 4.78% and 1.9%, demonstrating superior path planning performance. For local dynamic obstacle avoidance, both single-robot and multirobot systems successfully avoided obstacles in multiple experiments. Finally, physical experiments conducted in various complex scenarios show that both single-robot and multirobot systems can effectively execute global planning and respond to unexpected obstacles. These results further demonstrate the method’s wide applicability and robust performance across diverse complex environments.

Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems’ fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning.

Swarm robot systems perform long-term tasks such as information gathering and cooperative exploration while adapting to dynamic and unpredictable environments. These systems are increasingly applied in real-world scenarios such as lunar exploration and disaster response, where multiple robots operate independently and cooperatively across large areas. To support such missions, swarm robots often construct wireless communication networks spanning their deployment regions. Conventionally, the robustness of these systems has been evaluated mainly in terms of accidental robot failures, relying on redundant inter-robot connectivity. However, real-world environments also introduce spatially correlated failures, triggered by environmental phenomena such as flooding or structural collapse. To capture these effects, this chapter introduces a percolation theory–based framework for analyzing the robustness of communication networks formed by mobile robots with wireless relay capabilities. The proposed model incorporates two-dimensional noise fields to simulate environmentally induced failures and compares network robustness under environmental and accidental failure conditions. The results highlight distinct failure dynamics and identify redundancy strategies that can enhance overall system resilience. Furthermore, we establish how the spatial deployment and density of robots influence Quality of Service (QoS) indicators such as reliability and connectivity. Experimental results demonstrate that maintaining QoS under environmentally correlated (noise-induced) and accidental failures requires different redundancy strategies. These findings provide valuable insights for designing resilient 6G, Internet of Things (IoT), and disaster-recovery communication networks capable of operating in dynamic environments.

Adaptive team performance is essential in high-pressure, unpredictable environments. This study investigates the impact of team coordination and team communication on adaptive team performance. Data were collected from 230 team members using structured questionnaires and analyzed through Partial Least Squares (PLS). Results reveal significant and positive relationships between coordination and communication, and adaptive team performance. These findings highlight the importance of strengthening team functional processes to enhance responsiveness and resilience. The study provides practical insights for enhancing team effectiveness in dynamic operational environments, particularly where rapid decision-making and flexibility are essential for success in unpredictable and challenging conditions.

This research proposes a novel hardware-optimized design to accelerate Convolutional Neural Networks (CNNs) using Verilog HDL. The design is specifically developed for the DARKNET-19 system model, which serves as the backbone of the YOLOv3-tiny algorithm, a widely used framework for real-time object detection in dynamic environments. The CNN architecture was implemented in Verilog HDL and synthesized using Synopsys Design Compiler, with a focus on improving both object detection accuracy and hardware resource efficiency. The proposed design efficiently performs key CNN operations, including convolution, pooling, and activation, enabling faster real-time object detection compared to many existing methods. To improve performance, the hardware design incorporates parallel processing techniques, allowing multiple computations to be executed simultaneously. This significantly reduces the system latency and power consumption. The convolutional layers of the DARKNET-19 architecture are efficiently mapped onto the hardware platform, ensuring optimized data storage and fast memory access, which further enhances processing speed and detection accuracy. An innovative feature of the design is a 2-dimensional image preprocessing module that prepares input images before they are fed into the CNN. This preprocessing stage includes image resizing, brightness normalization, and color adjustment, which helps the CNN process visual data more effectively. After preprocessing, the images pass through several CNN layers. The convolutional layers extract key features from the images, while the pooling and activation layers refine these features to improve detection performance. Finally, the processed data is analyzed by the YOLOv3-tiny algorithm, which identifies and locates objects in the images with high precision. Experimental results demonstrate that the proposed high-speed and resource-efficient hardware architecture is well-suited for real-time object detection applications, particularly in highly dynamic and unpredictable environments.

Combinatorial applications such as configuration, transportation and resource allocation often operate under highly dynamic and unpredictable environments. In this regard, one of the main challenges is to maintain a consistent solution anytime constraints are (dynamically) added. While many solvers have been developed to tackle these applications, they often work under idealized assumptions of environmental stability. In order to address limitation, we propose a methodology, relying on nature-inspired techniques, for solving constraint problems when constraints are added dynamically. The choice for nature-inspired techniques is motivated by the fact that these are iterative algorithms, capable of maintaining a set of promising solutions, at each iteration. Our methodology takes advantage of these two properties, as follows. We first solve the initial constraint problem and save the final state (and the related population) after obtaining a consistent solution. This saved context will then be used as a resume point for finding, in an incremental manner, new solutions to subsequent variants of the problem, anytime new constraints are added. More precisely, once a solution is found, we resume from the current state to search for a new one (if the old solution is no longer feasible), when new constraints are added. This can be seen as an optimization problem where we look for a new feasible solution satisfying old and new constraints, while minimizing the differences with the solution of the previous problem, in sequence. This latter objective ensures to find the least disruptive solution, as this is very important in many applications including scheduling, planning and timetabling. Following on our proposed methodology, we have developed the dynamic variant of several nature-inspired techniques to tackle dynamic constraint problems. Constraint problems are represented using the well-known constraint satisfaction problem (CSP) paradigm. Dealing with constraint additions in a dynamic environment can then be expressed as a series of static CSPs, each resulting from a change in the previous one by adding new constraints. This sequence of CSPs is called the dynamic CSP (DCSP). To assess the performance of our proposed methodology, we conducted several experiments on randomly generated DCSP instances, following the RB model. The results of the experiments are reported and discussed.

CoNi-MPC [1] provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b>operative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b>on-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</b>nertial frame-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b>bstacle <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>voidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: (1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states; (2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction; and (3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios.

This paper describes a new approach to path planning of robot manipulators with many degrees of freedom. It is designed for on-line motion in dynamic and unpredictable environments. The robots react to moving obstacles using a local and reactive algorithm restricted to a subset of its configuration space. The lack of a long-term view of local algorithms (local minima problems) is solved using an off-line pre-planning stage that chooses the subset of the configuration space that minimises the probability of not finding collision free paths. The approach is implemented and tested on a system of three Scorbot-er IX five link robots.

The Business Intelligence (BI) traditional goals still stay: provide the right information users in organization with right information at the right time and place. The BI systems by their nature demonstrate as heterogeneous, while all plans to unify them eventually fail. Moreover the expectations of R&D of proactive BI let us conclude, the heterogeneity may become chaotic. We claim that, for mastering such a heterogeneous and dynamic system, a programmatically accessible and self managed knowledge management system, capable of storing and representing the whole content of a BI system is required. The properties of Knowledge Management Structures (KMS), combined with intelligent agents (IA) and Multi-agent systems (MAS) show great capabilities, and have, in limited scope, proved their strengths, acting in heterogeneous svstems. In the following paper we synthesized the state of the art research in the fields of BI, KMS, IA and tried to fill a gap disabling successful application of IA for management of KMS in a dynamical environment. To do so, a model of an IA, capable of reasoning and acting in a dynamic KMS environment and sharing its knowledge is presented.

A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm. Index Terms— Real-time computer control, Mobile robot, Multi-agent system, Path planning algorithms.

This study addresses the challenges of real-time channel state estimation and adaptive parameter adjustment in dynamic LoRa networks, where the existing methods often fail to adapt efficiently to highly variable channel conditions. This study presents an innovative approach for real-time channel state estimation and adaptive parameter adjustment in long-range (LoRa) networks in dynamic Internet of Things (IoT) environments. When these types of networks are used in dynamic IoT environments, they are known to face challenges in the two above-mentioned areas. In our approach, a hybrid feature extraction method that integrates statistical analysis with domain-specific knowledge is utilized for real-time data labeling, focusing on the signal-to-noise (SNR) and received signal strength indicator (RSSI) metrics. This approach employs an adaptive sliding window technique for efficient processing of recent data. Subsequently, a multi-task long short-term memory (LSTM) neural network is introduced for the simultaneous prediction of multiple channel states. This multi-task model employs an online incremental learning approach to enhance the real-time performance and responsiveness of the model within dynamic environments. It also incorporates a confidence measure for estimated states to increase the prediction reliability. Finally, based on the confidence measure predictions and channel state estimation, the system dynamically adjusts the LoRa parameters, including the spreading factor, coding rate, transmission power, and bandwidth. Our results demonstrate that the confidence-based adaptive strategy coupled with adaptive sliding window processing and incremental learning effectively balances performance optimization with stability in challenging IoT scenarios. This study contributes a robust, data-driven approach for real-time channel state estimation and adaptive parameter control, addressing the unique challenges of IoT networks in dynamic environments. Our approach achieved a packet delivery ratio of 100%, reduced energy consumption to 0.07987 Joules per packet, and demonstrated a prediction accuracy between 97.70% and 97.9% for estimating the different channel states. This innovative framework provides significant improvements in channel state estimation, communication reliability, adaptive parameter control, and computational efficiency, thereby ensuring robust performance in IoT environments at the same time.

Logics and Multi-agents: towards a new symbolic model of cognition

Chapter 21 - Event-driven programming-based path planning and navigation of UAVs around a complex urban environment