Temporal Landscapes: A Graphical Temporal Logic for Reasoning

We present an elementary introduction to a new logic for reasoning about behaviors that occur over time. This logic is based on temporal type theory. The syntax of the logic is similar to the usual first-order logic; what differs is the notion of truth value. Instead of reasoning about whether formulas are true or false, our logic reasons about temporal landscapes. A temporal landscape may be thought of as representing the set of durations over which a statement is true. To help understand the practical implications of this approach, we give a wide variety of examples where this logic is used to reason about autonomous agents.

Purpose This article explores how autonomous systems are socially situated in the mining industry through the lens of Communities of Practice. It examines how learning, coordination and shared meaning-making unfold when new technology is introduced, and how implementation extends beyond technical deployment to include social and organisational processes. Design/methodology/approach The study draws on a qualitative case study of a Swedish open-pit mine where an autonomous haulage system was implemented. Wenger's (2008) Communities of Practice framework was used as a sensitising concept to analyse empirical material, focusing on mutual engagement, joint enterprise and shared repertoire in everyday work practices. Findings The analysis shows how community members co-constructed new work practices, formed collective interpretations of the technology, and engaged in relational work to legitimise the system within the wider organisation. Successful implementation was found to depend not only on technical performance but also on social processes of meaning-making, identity formation, and the negotiation of organisational change. Practical implications By foregrounding the social dimensions of technological change, the study suggests that organisations introducing autonomous systems should support collaborative learning and provide spaces for shared meaning-making, thereby increasing the likelihood of successful implementation. Originality/value The paper contributes to research on technology, work, and organisation by highlighting the situated and practice-based nature of technological implementation. It underscores the value of attending to communities of practice when investigating how new technology gains traction and relevance in complex organisational settings.

A detailed survey of the available literature on path planning of Autonomous Ground Vehicle (AGV) is conducted, including the overview of single-robot control architectures, different path-planning approaches, analyses of current sensor systems and different velocity estimation techniques. In order to achieve the full autonomous operation of a mobile robot, path or motion planning, i.e., the planning of a collision-free path from a start to goal position through a collection of obstacles, is the fundamental task in the field of autonomous control systems. As AGVs are used in a wide variety of applications to perform autonomous tasks, organising their intelligence plays a key role in successfully programming a robot for a particular application and applying the right control architecture makes the autonomous control problem easier to solve. For autonomous vehicles, sensors play an important role in acquiring different attributes of the working environment and, by extracting meaningful information from these data, the autonomous system can acquire knowledge about its environment. Moreover, different velocity estimation techniques are reviewed in the context of dealing with the dynamic obstacles. A brief review of the available literature on path-planning approaches and techniques is provided.

Rauscher method becomes the matter of interest because in combination with the method of nonlinear normal vibration modes it allows to calculate steady forced vibrations in the system with multiple degrees of freedom (DOF) via reduction in the number of DOFs. However, modern realizations of that approach have drawbacks such as iterative nature and the need to have initial approximation for the solution. The primary principle of Rauscher method is in obtaining periodic solutions of a non-autonomous system via studying some equivalent autonomous one. In the paper, a new non-iterative variant of Rauscher method is considered. In its current statement, the method can be used in analysis of forced harmonic oscillations in a nonlinear system with one degree of freedom. The primary goals of the study were to find out what kind of equivalent autonomous systems could be built for a given non-autonomous one and how they can be used for the construction of periodic solutions and/or periodic phase plane orbits of the initial system. It is shown that three different types of equivalent autonomous dynamical systems can be built for a given 1-DOF non-autonomous one. The system of 1st type is a fourth-order dynamical system. Technically it can be considered as a 2-DOF system where additional “DOF” is explicitly “responsible” for forced oscillations. The system of 2nd type is a third-order dynamical system. Its periodic orbits are exactly the same as in the initial system. Using the invariant manifold of the system of 1st type, the system of 2nd type can be reduced to the form $$W(x,x')=0$$ (which is called here the equivalent system of the 3rd type). It is important that the function $$W(x,x')$$ can be built a priori. Once $$W(x,x')$$ is found: (i) one can obtain different periodical orbits corresponding to forced oscillations in the initial system; (ii) one can estimate amplitudes of vibrations for these regimes; (iii) one can track bifurcations of periodical regimes of the initial system with respect to change in amplitude of external excitation f. As shown in the paper, periodical orbits of the initial non-autonomous system can be obtained via two different approaches: (i) as set of points on phase plane satisfying the condition $$W(x,x')=0$$ ; (ii) via the application of harmonic balance method to the equivalent system of 1st type using system’s energy level as a continuation parameter. This approach has advantage over application of harmonic balance method to initial system because the latter requires good initial guess for expansion coefficients, while the new approach does not and always starts from zero initial guess.

Inter-domain routing protocol routes network reachability information between autonomous systems (ASs) in the network. Network reachability information contains the details about the neighbors of the ASs and it contains messages used to maintain the connection between ASs. On one hand, given the size and the rapid growth of the Internet, any inter-domain routing protocol should satisfy basic desirable algorithmic properties, such as scalability, robustness and rapid convergence by Griffin, T., and Wilford, G. 1999. On the other hand, for economic reasons inter-domain routing should support policy routing, where Internet service providers (ISPs) have the flexibility to implement a wide variety of private routing policies that ISPs choose not to reveal. Moreover, the routing protocol should provide sufficient information to enable ISPs to make informed policy decisions. Border gateway protocol (BGP) by Goodell, G. et al. (2003) is the inter-domain routing protocol that is currently being used. BGP has a number of problems which includes scalability and large convergence time by Labovitz, C et al. (2000). This paper explains in detail the alternative to BGP which is the hybrid optimized link-state path-vector routing protocol (HLP) [1] and gives a comparison based on the simulation of these protocols. The comparison shows us that HLP overcomes some of the important problems faced in BGP.

Visual blind spots caused by occlusion from lead vehicles represent a significant latent risk factor contributing to traffic accidents. Although Level 2 (L2) autonomous driving systems can partially mitigate this issue, human drivers are still required to actively perceive potential hazards and understand the behaviour of the autonomous vehicle to ensure driving safety. Therefore, designing interactive interfaces that reduce the risks associated with visual blind spots is critical in autonomous driving scenarios. However, current research on this typical scenario remains relatively limited. This study proposes an innovative cooperative driving warning strategy, focussing on driving situations where the driver's line of sight is blocked by a lead vehicle. The strategy integrates environmental cues and behavioural information from the autonomous driving system through an augmented reality (AR) interface, aiming to facilitate efficient cooperation between human drivers and autonomous systems in perceiving visual blind spots. We systematically evaluated the proposed interface prototype using a driving simulator under L2 autonomous driving conditions. The results indicate that the interface significantly enhances drivers' situation awareness and reduces their reaction time to potential hazards. Additionally, the design improves the quality of human-machine cooperation by decreasing conflicts with the autonomous system, increasing trust in the system, and significantly boosting user satisfaction. This study provides new insights into the design of human-machine cooperative perception interfaces for blind spot scenarios and offers both theoretical foundations and practical implications for the future development of cooperative driving systems.

Destination IP prefix-based routing protocols are core to Internet routing\ntoday. Internet autonomous systems (AS) possess fixed IP prefixes, while\npackets carry the intended destination AS's prefix in their headers, in clear\ntext. As a result, network communications can be easily identified using IP\naddresses and become targets of a wide variety of attacks, such as DNS/IP\nfiltering, distributed Denial-of-Service (DDoS) attacks, man-in-the-middle\n(MITM) attacks, etc. In this work, we explore an alternative network\narchitecture that fundamentally removes such vulnerabilities by disassociating\nthe relationship between IP prefixes and destination networks, and by allowing\nany end-to-end communication session to have dynamic, short-lived, and\npseudo-random IP addresses drawn from a range of IP prefixes rather than one.\nThe concept is seemingly impossible to realize in todays Internet. We\ndemonstrate how this is doable today with three different strategies using\nsoftware defined networking (SDN), and how this can be done at scale to\ntransform the Internet addressing and routing paradigms with the novel concept\nof a distributed software defined Internet exchange (SDX). The solution works\nwith both IPv4 and IPv6, whereas the latter provides higher degrees of IP\naddressing freedom. Prototypes based on OpenvSwitches (OVS) have been\nimplemented for experimentation across the PEERING BGP testbed. The SDX\nsolution not only provides a technically sustainable pathway towards\nlarge-scale traffic analysis resistant network (TARN) support, it also unveils\na new business model for customer driven, customizable and trustable end-to-end\nnetwork services.\n

This chapter explores the integration of Curriculum Learning (CL) strategies with Reinforcement Learning (RL) to accelerate policy development in Autonomous Decision Systems (ADS). By leveraging a structured learning progression, CL enhances the efficiency of RL algorithms, enabling autonomous systems to master complex tasks more effectively and reliably. The chapter delves into key methodologies such as task difficulty scaling, dynamic environment adaptation, and the incremental introduction of complexity, offering practical insights for various applications. Case studies across domains like robotics, healthcare, and autonomous vehicles highlight the synergy between CL and RL, demonstrating their potential to optimize decision-making, improve generalization, and reduce training time. The chapter further emphasizes the transformative impact of CL-RL integration on real-world systems, pushing the boundaries of autonomous decision-making in uncertain and dynamic environments. This research contributes to advancing the capabilities of autonomous systems through intelligent learning strategies, with implications for both theory and practice.

Stimuli-responsive polymers system, which responds to external stimuli such as temperature, pH, electric fields, etc., have been much investigated in order to apply to actuators, artificial muscle, drug delivery systems (DDS), shape-memory, molecular recognition material, separation-system and purification material, etc. [1-21] However, the conventional-type stimuli-responsive polymer system provides only one unique action to the external stimulus. On the other hand, many biological systems, such as the brain waves, autonomic heartbeat and periodic hormone secretion, show autonomous oscillations and respond to external stimuli. If we construct the autonomous polymer system like a living organism with tailor-made design, unprecedented polymer system would be realized. In this study, in order to construct the autonomous polymer system like a living organism, the Belousov-Zhabotinsky (BZ) has been utilized as the chemical energy source of the autonomous motion for polymer systems. The BZ reaction is well known as an oscillating reaction accompanying spontaneous redox oscillations to generate a wide variety of nonlinear phenomena, e.g., target or spiral pattern in an unstirred solution, and periodicity, multi-periodicity, or chaos in a stirred solution [2230]. The overall process of the BZ reaction is the oxidization of an organic substrate by an oxidizing agent in the presence of the catalyst under strong acidic conditions. In the BZ reaction, the metal catalyst undergoes spontaneous redox oscillation. In the case of utilizing the ruthenium tris(2,2’-bipyridine) that is the metal catalyst of the BZ reaction, the Ru catalyst has the different solubility in the reduced and oxidized states, respectively: the reduced Ru catalyst has a hydrophobic property, and the oxidized one has a hydrophilic one. In order to cause the self-oscillation of the polymer system (liner-polymer chain and polymer gel), polymer main-chain covalently bonded to the (ruthenium (4-vinyl-4’-methyl2,2-bipyridine) bis(2,2’-bipyridine)bis(hexafluorophosphate)) (Ru(bpy)3) as a catalyst of the BZ reaction were synthesized [31-33]. The main-chain of the self-oscillating polymer system adopted thermoresponsive poly(Nisopropylacrylamide) (poly(NIPAAm)). As a result, the polymer system cause the autonomous and spontaneous aggregation-disaggregation selfoscillation for the linear-polymer chain and the swelling-deswelling self-oscillation for the polymer gel under the constant temperature conditions with the coexistence of the BZ substrates other than the metal catalyst. However, operating condition of the self-oscillating

Theoretical Findings Validate Historical Data Reuse for Improved Policy Optimization A new study, “Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate” by Yifan Lin, Yuhao Wang, and Enlu Zhou, explores an advanced approach to reinforcement learning. The research focuses on improving policy optimization by reusing historical trajectories through importance sampling in natural policy gradient methods. The authors rigorously analyze the convergence properties of this approach and demonstrate that reusing past data enhances convergence rates while maintaining theoretical guarantees. Their findings have practical implications for applications where data collection is costly or limited, such as robotics and autonomous systems. By integrating these insights into policy optimization frameworks, the study provides a valuable contribution to the field of reinforcement learning.

The rapid evolution of autonomous systems is reshaping urban mobility and accelerating the development of intelligent transportation networks. A key challenge in real-world deployment is the ability to operate reliably under uncertainty–arising from sensor noise, dynamic agents, and adverse weather conditions. This paper investigates Bayesian Neural Networks (BNNs) as a principled framework for uncertainty-aware decision-making in autonomous navigation. Through three representative case studies–urban navigation, obstacle avoidance, and weather-induced visual degradation–we demonstrate how BNNs outperform deterministic neural networks by providing calibrated predictions and uncertainty estimates. These probabilistic outputs enable conservative and interpretable decision-making in high-risk environments, thereby enhancing safety and robustness. Our results show that BNNs offer substantial improvements in trajectory accuracy, adaptability to occlusions, and resilience to perceptual distortion. This study bridges theoretical advances in Bayesian deep learning with practical implications for autonomous vehicles, establishing BNNs as a foundational tool for building safer and more trustworthy mobility systems.

Artificial intelligence (AI) has changed the course of our lives in many ways, both professionally and personally. Soon, autonomous systems will be integral to a wide variety of fields, including space, marine, and aviation robotics, field, and road robotics, and service robots. However, automated systems not only do routine tasks but also significantly improve the quality of the labor force. Artificial intelligence and robotics have advanced to the point that it's possible to build a computer with awareness, sentience, and the ability to think. Innovative research into combining AI with robots is yielding many exciting prospects for the future of technology. The process of compiling these potentials has begun. Abbreviating "robotic process automation" to "RPA" helps keep things simple. backed by tools that facilitate a new kind of Bioinformatic process automation (BPA) in the business world something which is based on software, such as artificial intelligence (AI) or robots. employees of a society or organization.

Here, we share our unique data acquisition (DAQ) system, using an open-source ARM microcontroller SoC, for underwater acoustic beam-forming applications, especially for use on autonomous vehicles. Here, we present our results of performance experiments, demonstrating the speed and accuracy of sampling and processing audio-frequency acoustic signal processing, for a wide variety of signals and sampling regimes. We will demonstrate trade-offs between sampling rates and resolutions for accuracy and speed at desired frequencies, and how these may be dynamically managed by autonomous systems. Finally we will share some of our work, towards underwater acoustic beam-forming using 3D acoustic vector sensors, and our implementation of these devices on unmanned autonomous vehicles.

This abstract is intended for the special session “Acoustic Vector Field Studies.” In this work, we will show our progress towards a low-power, lightweight, embedded data acquisition system and real-time signal processor for acoustic vector sensors, using an open-source microcontroller. Modern advances in low-cost, low-power “systems-on-a-chip” (SoC) provide unprecedented computational power, and some state-of-the-art devices are particularly well designed for acoustic signal processing applications. Here, we present the results of performance experiments, demonstrating the speed and accuracy of sampling and processing audio-frequency signals for a wide variety of signals and sampling regimes. We demonstrate trade-offs between sampling rates and resolutions for accuracy and speed at desired frequencies, and how these may be dynamically managed by autonomous systems. Finally, we will share some of our work towards real-time acoustic beamforming for use on autonomous vehicles.

The goal of machine learning research is to equip robots with human-like perception capabilities so that they can sense its working environment, understand the collected data, take appropriate actions, and learn from its experience so as to enhance future performance. As a result, the acquisition of knowledge about its environment is one of the most important tasks of an autonomous system of any kind. This is done by taking measurements using various sensors and then extracting meaningful information from those measurements. In this chapter, we will present a vision based machine perception model from the perspective of a system design domain and discuss strategies for extracting information acquired from vision sensors for mobile robot localization tasks. More specifically, we aim to apply several important machine learning techniques to vision-based mobile robot navigation applications by discussing three issues, namely, information acquisition, environmental representation, and reasoning, leading to a general high-level model of the problem. The model is intended to be generic enough to allow a wide variety of tasks to be performed using a single set of sensory data. It is argued that the model has a direct correspondence with some recent biological evidences and can be applied to solving real-world problems specifically for an autonomous system operating in outdoor unknown environments.