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Cognitive agents make systems autonomous through the process of decision automation by mining an existing knowledge repository at run time. These processes can often be highly compute intensive, and would thus run slowly on the low-power computing platforms typically seen in autonomous systems. This paper examines how knowledge be represented in a Q-table and proposes a novel fast algorithm to mine that knowledge based on constraints. We evaluate this approach for the knowledge mining process of a specific agent: Cognitively Enhanced Complex Event Processing (CECEP). Within CECEP, knowledge is represented using Cognitive Domain Ontologies (CDO), and is mined using situational inputs and constraints. This is a novel approach to store information and is able to accommodate CDOs with millions of solutions. To show that the approach can run on low power hardware in real-time, this algorithm was executed on two low-power minicomputing platforms - Intel’s NUC and Asus’s Tinker Board. At present, no other optimized CDO solvers can generate solutions on these platforms. The algorithm generated the same amount of solutions as a GPU-enabled optimized path-based forward checking CDO solver, while consuming around 7.7 and 5.15 times less energy (Joules) on the NUC and Tinker Board respectively.

Aiming at the decision-making problem of UCAV autonomous maneuvering in close range one-to-one scenarios, a close range UCAV autonomous maneuvering decision-making algorithm based on expert know-ledge base and matrix game method is proposed, which can conduct real-time situational analysis and maneuver decision-making in one-to-one close range air combat to achieve victory. This algorithm constructs the launching mechanism of UCAV autonomous air combat based on tactical library, builds the dynamics model of fighter and missile, and builds the situation evaluation function based on typical air combat factors and missile attack area analysis. Further combining the situation evaluation function based on missile attack area with matrix game theory, a set of autonomous air combat decision model is constructed. On this basis, the algorithm is used for game confrontation simulation on both sides of UCAV in typical scenarios. Multiple sets of simulation results showed that the algorithm can effectively conduct real-time maneuvering decision-making game confrontation, and the single step decision-making time of a single machine can be maintained within 80 ms.

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A generic prioritization framework is introduced for addressing the problem of automated prioritization of region of interest or target selection. The framework is based on the assumption that clustering of preliminary data for preidentified regions or targets of interest within an operational area has already occurred, i.e., post-classification, and that the clustering quality can be expressed as an energy/objective function. Region or target of interest prioritization then means to rank regions or targets of interest according to their probability of changing the energy/objective function value upon subsequent hypothetical probing as opposed to actually conducted reexamination, i.e., thorough follow-up or in-situ measurements. The mathematical formalism for calculating these probabilities to contribute to this change of the energy/objective function value is introduced and validated through numerical simulations. Moreover, these probabilities can also be understood as a confidence-check of the classification, i.e., the pre-clustering of the preliminary data. The operation of the prioritization framework is independent of the algorithm used to pre-cluster the preliminary data, and supports autonomous decision-making. It is widely applicable across many scientific disciplines and areas, ranging from the microscopic to the macroscopic scale. Due to its ability to help maximize scientific return while optimizing resource utilization, it is particularly relevant in the context of resource-constrained autonomous robotic planetary exploration as it may extend the Remaining Useful Lifetime (RUL) – a key aspect in Prognostics and Health Management (PHM) – of space missions. On a more general, PHM-relevant level, the prioritization framework may provide an additional mechanism of identifying and correcting the maintenance status of system components to assist predictive maintenance or condition-based maintenance.

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The demand for autonomous UAV swarm operations has been on the rise following the success of UAVs in various challenging tasks. Yet conventional swarm control approaches are inadequate for coping with swarm scalability, computational requirements, and real-time performance. In this paper, we demonstrate the capability of emerging multi-agent reinforcement learning (MARL) approaches to successfully and efficiently make sequential decisions during UAV swarm collaborative tasks. We propose a scalable, real-time, MARL approach for UAV collaborative navigation where members of the swarm have to arrive at target locations at the same time. Centralized training and decentralized execution (CTDE) are used to achieve this, where a combination of negative and positive reinforcement is employed in the reward function. Curriculum learning is used to facilitate the sought performance, especially due to the high complexity of the problem which requires extensive exploration. A UAV model that highly resembles the respective physical platform is used for training the proposed framework to make training and testing realistic. The scalability of the platform to various swarm sizes, speeds, goal positions, environment dimensions, and UAV masses has been showcased in (1) a load drop-off scenario, and (2) UAV swarm formation without requiring any re-training or fine-tuning of the agents. The obtained simulation results have proven the effectiveness and generalizability of our proposed MARL framework for cooperative UAV navigation.

Nowadays, the autonomous driving transportation systems are at the heart of both academic and industry research for the distinguished advantages including increased network capacity, enhanced punctuality, greater flexibility and improved overall safety level. With the responsibility of transporting passengers in a safe, comfortable and efficient way, the decision making method plays a critical position in the autonomous driving of high-speed trains. Focusing on solving the autonomous decision making problem, this paper proposes a novel learning based framework by combining the deep learning technology with the distributed tracking control approach. To cope with the data insufficiency problem in training the deep learning network, a generative adversarial network (GAN) based data argumentation scheme is proposed to generate data samples that have the same distribution with actual data samples, and a hybrid learning network is constructed to predict the speed trajectory from the multi-attribute data with both temporal sequences and static features. Then, based on the model predictive control (MPC) scheme, a distributed tracking control model is formulated to minimize the tracking deviations and balance the performance of punctuality, energy-efficiency and riding comfort. Further, the dual decomposition technique is adopted to deal with the coupling constraints for the safe distance headway such that the separation for the autonomous driving of high-speed trains is achieved. Finally, simulation experiments based on actual scenarios of the Beijing-Shanghai high-speed railway are conducted to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Human–agent teaming (HAT) is becoming more commonplace across industry, military, and consumer settings. Agents are becoming more advanced, more integrated, and more responsible for tasks previously assigned to humans. In addition, the dyadic human–agent teaming nature is evolving from a one–one pair to one–many, in which the human is working with numerous agents to accomplish a task. As capabilities become more advanced and humanlike, the best method for humans and agents to effectively coordinate is still unknown. Therefore, current research must start diverting focus from how many agents can a human manage to how can agents and humans work together effectively. Levels of autonomy (LOAs), or varying levels of responsibility given to the agents, implemented specifically in the decision-making process could potentially address some of the issues related to workload, stress, performance, and trust. This study sought to explore the effects of different LOAs on human–machine team coordination, performance, trust, and decision making in hand with assessments of operator workload and stress in a simulated multi-unmanned aircraft vehicle (UAV) intelligence surveillance and reconnaissance (ISR) task. The results of the study can be used to identify human factor roadblocks to effective HAT and provide guidance for future designs of HAT. Additionally, the unique impacts of LOA and autonomous decision making by agents on trust are explored.

Self-driving safety is one of the major concerns raised with regard to pushing the use of automated vehicles on roads. Fully automated vehicles are forced to make appropriate decisions in an uncertain environment where driverless and human-driven vehicles share the road together. This study proposes a new model that can be used to enhance the autonomous driving behavior at a non-signalized intersection considering the traffic safety guarantee, delay time, and smooth driving. The proposed model is called the responsibility-sensitive safety-based partially observable Markov decision process model for the decision-making mechanism of automated vehicles. The model not only increases traffic safety guarantee and smooth driving, but also reduces the delay time. First, we generate some specific driving scenarios using the automated driving toolbox in MATLAB. Second, the driving strategy of automated vehicles is optimized by the partially observable Markov decision process framework using the adaptive cruise control system. Finally, the responsibility-sensitive safety algorithm is implemented under adaptive model predictive control. The proposed model performs better than the classical adaptive model predictive control. In the best case, the proposed model took a 31.60 % reduction in braking time and a 51.20 % improvement in smoothing speed control. [ABSTRACT FROM AUTHOR]

Cognitive agents are typically utilized in autonomous systems for automated decision making. With the widespread use of autonomous systems in complex environments, the need for real-time cognitive agents is essential. Cognitive agents are more capable when they are able to process larger amounts of information to make more informed and intelligent decisions. The solution search space for cognitive agents increases exponentially with large volumes of varied data. In this paper, we present the parallelization of the knowledge-mining component of a cognitive agent that can be programmed to reason like humans. This study examined a novel high-performance path-based forward checking algorithm on 128 compute nodes at the Ohio Supercomputing Center (768 cores) to achieve a speedup of over 200 times compared to a serial implementation of our algorithm. The serial implementation is around 10–25 times faster than a conventional Java-based constraint solver at generating the first solution. [ABSTRACT FROM AUTHOR]

Maritime safety is an ongoing process in shipping that is constantly being improved by the modernization of equipment and constant improvements in operators’ safety procedures and training. However, human error remains a significant factor in maritime accidents, as it contributes to 75% of incidents. Addressing this problem, the current paper shows a proof of principal for on-board fire monitoring and extinguishing software agents that may be used to upgrade present systems and contribute to an autonomous ship design. Agent technology that engages fire detection and firefighting equipment while minimizing human intervention will reduce the risks of human error and increase maritime safety.

This paper examines some ethical and cognitive aspects of machines making moral decisions in difficult situations. We compare the situations when humans have to make tough moral choices with those in which machines make such decisions. We argue that in situations where machines make tough moral choices, it is important to produce justification for those decisions that are psychologically compelling and acceptable by people. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conceptions of autonomy in western philosophy and ethics have often centred on self-governance and self-determination. However, a growing bulk of literature also questions such conceptions, including the understanding of the autonomous self as a self-governing independent individual that chooses, acts, and lives in accordance with her or his own values, norms, or sense of self. This article contributes to the critical interrogation of selfhood, autonomy, and autonomous decision making by combining a feminist focus on relational dimensions of selfhood and autonomy with phenomenological philosophy of the embodied self as being-in-the-world. It offers a philosophical investigation of different dimensions of bodily relational autonomy by turning to phenomenological accounts of the lived body as self-reflexive. When so doing, we hope to contribute to bridging the gap that sometimes exists between discussions of autonomy in analytic moral philosophy and of freedom and facticity in phenomenological philosophy. We see this gap as unfortunate, and hold that a nuanced understanding of autonomy and autonomous decision making can be reached if these strands ofphilosophy are brought into dialogue. [ABSTRACT FROM AUTHOR]

In this paper, we examine a problem of autonomous vehicle decision making for search operations performed by undersea autonomous vehicles. Specifically, we consider the problem of determining whether to continue with preplanned search paths or to switch to focused local search efforts in the presence of limited contact information. Using techniques from information theory, we develop a context of search channel capacity from which rapid computations of performance bounds can be computed based on sensing capabilities and prior search information. These bounds characterize search planning alternatives and are useful in making the initial online determination of search plan adaptation. We illustrate the utility of our approach with numerical examples applying multiple undersea searchers. [ABSTRACT FROM PUBLISHER]

System complexity has reached a level where it is hard to apply existing information analysis methods to automatically derive appropriate decisions. Building automation is on the verge of being unable to extract relevant information and control a building accordingly. Many different industries in today's automation could provide information by means of different sensors, but the ability to integrate this information is missing. This paper describes approaches on how to cope with increased complexity by introducing models for perception and decision making that are based on findings in neuroscience and psychoanalysis, scientific disciplines that are far-off from engineering but nevertheless promise valuable contributions to intelligent automation. [ABSTRACT FROM PUBLISHER]

Cross-layer optimization solutions have been proposed in recent years to improve the performance of wireless users that operate in a time-varying, error-prone network environment. However, these solutions often rely on centralized cross-layer optimization solutions that violate the layered network architecture of the protocol stack by requiring layers to provide access to their internal protocol parameters to other layers. This paper presents a new systematic framework for cross-layer optimization, which allows each layer to make autonomous decisions to maximize the wireless user's utility by optimally determining what information should be exchanged among layers. Hence, this cross-layer framework preserves the current layered network architecture. Since the user interacts with the wireless environment at various layers of the protocol stack, the cross-layer optimization problem is solved in a layered fashion such that each layer adapts its own protocol parameters and exchanges information (messages) with other layers that cooperatively maximize the performance of the wireless user. Based on the proposed layered framework, we also design a message-exchange mechanism that determines the optimal cross-layer transmission strategies, given the user's experienced environment dynamics. [ABSTRACT FROM AUTHOR]

A new national action plan is needed to articulate the direction of healthcare in the new millennium and ameliorate the nursing shortage. Without a proper understanding of how labour statistics relate to deeply entrenched barriers, current short term solutions will only serve to sabotage future efforts. In order to raise nursing professionalism, three changes are suggested. First, there is a need to replace top down management with participatory management. This must be done through a redefinition of the nurses' role at the ward level through specialisation and autonomous decision making. Second, changes should be made at the policy making level by realising the full leadership potential of nurses at the very top of the hierarchy as well as fairer representation at the Board level. Third, it must be done through developing better clinical practice and creating conducive environment for nurses to continue their nursing education. [ABSTRACT FROM AUTHOR]

Human-agent teaming (HAT) is becoming more commonplace across industry, military, and consumer settings. Agents are becoming more advanced, more integrated, and more responsible for tasks previously assigned to humans. In addition, the dyadic human-agent teaming nature is evolving from a one-one pair to one-many, in which the human is working with numerous agents to accomplish a task. As capabilities become more advanced and humanlike, the best method for humans and agents to effectively coordinate is still unknown. Therefore, current research must start diverting focus from how many agents can a human manage to how can agents and humans work together effectively. Levels of autonomy (LOAs), or varying levels of responsibility given to the agents, implemented specifically in the decision-making process could potentially address some of the issues related to workload, stress, performance, and trust. This study sought to explore the effects of different LOAs on human-machine team coordination, performance, trust, and decision making in hand with assessments of operator workload and stress in a simulated multi-unmanned aircraft vehicle (UAV) intelligence surveillance and reconnaissance (ISR) task. The results of the study can be used to identify human factor roadblocks to effective HAT and provide guidance for future designs of HAT. Additionally, the unique impacts of LOA and autonomous decision making by agents on trust are explored., Competing Interests: The author SR was affiliated to the company Aptima. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rebensky, Carmody, Ficke, Carroll and Bennett.)

Maritime safety is an ongoing process in shipping that is constantly being improved by the modernization of equipment and constant improvements in operators' safety procedures and training. However, human error remains a significant factor in maritime accidents, as it contributes to 75% of incidents. Addressing this problem, the current paper shows a proof of principal for on-board fire monitoring and extinguishing software agents that may be used to upgrade present systems and contribute to an autonomous ship design. Agent technology that engages fire detection and firefighting equipment while minimizing human intervention will reduce the risks of human error and increase maritime safety. [ABSTRACT FROM AUTHOR]