A novel dynamic function allocation method in human-machine systems focusing on trigger mechanism and allocation strategy

The DERA Centre for Human Sciences is investigating function allocation for future Naval systems with the aim of providing optimal allocation of functions within “lean” manned ships. Existing methods of allocating functions between human and machine were developed around old, “dumb” technology, the inherent assumption being that once this process had been undertaken, the outcome remained fixed. However, for future systems, knowledge-based technology provides the potential for functions to be allocated dynamically, according lo the operational context. As part of a programme to examine the potential of dynamic function allocation, a series of six experiments utilising the Multi-Attribute Task Battery (MATB) were conducted, and a brief overview of the experimental findings is reported. The main focus of this paper is to describe a new experimental environment which has been developed to address the limitations in the MATB, particularly the relevance of these tasks to Naval systems. This is an abstraction of a Naval Anti-Air Warfare Controller's task, incorporating dynamic function allocation. It will facilitate an in-depth investigation of the potential benefits of this technology for the Naval Command and Control domain.

Context-sensitive function allocation can take two forms: (a) ‘adaptable automation’, where changes in the allocation of functions are initiated by the user, or (b) ‘adaptive automation’, where the automation triggers changes. However, operational experience suggests that neither approach by itself is sufficient and appropriate. This paper describes a hybrid dynamic function allocation scheme, in which the system and the operator collaborate on selecting and activating appropriate automation levels. The default mode of the system is adaptive automation, but operators can override system selections when they feel the need and have the time to do so. The effectiveness of the hybrid system was compared to fixed function allocations, to an adaptive scheme and to adaptable function allocation in the context of simulated robotic arm control tasks. The hybrid system improved performance and reduced workload in routine scenarios. The system was also preferred over the fixed and adaptive approach by the operators. However, these benefits can be explained mainly by the excessive use of high levels of automation in this condition. The findings have implications on future designs of dynamic function allocation schemes in complex systems.

Dynamic function allocation theories have matured, but not to the extent that they can be implemented in actual environments. The authors identify a roadmap to help to get to that stage, after a brief review of relevant function allocation theories and the gaps in knowledge in adaptive allocation theories. Past research has focused on short‐term implementations of dynamic function allocation, which last for minutes (but never more than a few hours). Stable and enduring implementations of dynamic function allocation, lasting over days or possibly years, require a better and scientific understanding of the interaction among trigger mechanisms, adaptation strategies, and interruption strategies. The possibility of an unintentional consequence of this adaptive allocation, that the automated agent becomes confined to limited levels of automation, has to be ruled out after experimental evidence. © 2010 Wiley Periodicals, Inc.

The objective of this study was to investigate alternatives for allocating the tasks associated with defensive counter measures in a fighter cockpit environment. The three methods allocated the functions either totally to the operator or a simulated expert system and dynamically at the operator's request to either. The analysis of the objective data showed there were no significant performance differences among the three treatment conditions. However, the analysis of post treatment subjective data showed the subjects did have confidence in the simulated expert systems's ability to handle the threats (p < .01) and they had a significant preference for some form of computer assistance during the missions (p < .01).

This paper uses the modified Backstepping (BS) Control method and the Dynamic Control Allocation (DCA) method to solve the problem that when the fighter aircraft under different flight conditions, it will be adaptively tracking different commands. Firstly, we introduce the classical Lyapunov method and classical backstepping control method, and then propose a modified backstepping control method to be applied by general flight control system model. Finally, for the problem that the common control allocation method can't compensate for ignoring the actuator dynamics, a dynamic control allocation method is introduced, and it is successfully applied to the design of the whole closed loop control system. The controller reserves the stable nonlinear term of the system, eliminates the unstable nonlinear term, and minimize the impact of ignoring the actuator dynamic through the dynamic allocation. The simulation results show that the methods in this paper have a good response. It can be implemented in different flight conditions with a good performance of command tracking and lots of robustness.

Due to the large uncertainty of signal reception, the transmission speed of the wireless communication network resource allocation method is low. Based on social relations, a dynamic allocation method for wireless communication network resources is designed. Establish a wireless ad hoc network composed of a random number of nodes to determine the upper limit of its security capacity, and the nodes at the edge need to undertake more communication transmission loads on average. According to the similarity between users, a triple closed model is used to measure the social trust relationship in wireless communication networks. Considering the social trust and encounter frequency among users, the optimal relay node is selected for the wireless network communication link. The sum of data transmission rates under each allocation scheme is calculated and sorted in descending order, and resources are dynamically allocated based on the principle of maximum overall network throughput. Compared with other schemes through simulation, the dynamic allocation method of wireless communication network resources based on social relations can increase the network transmission speed and improve the communication performance.

To meet the growing demands of the National Airspace System (NAS) stakeholders and provide the level of service, safety and security needed to sustain future air transport, the Next Generation Air Transportation System (NextGen) concept calls for technologies and systems offering increasing support from automated systems that provide decision-aiding and optimization capabilities. This is an exciting application for some core aspects of Artificial Intelligence research since the automation must be designed to enable the human operators to access and process a myriad of information sources, understand heightened system complexity, and maximize capacity, throughput and fuel savings in the NAS.. This paper introduces an emerging application of techniques from mixed initiative (adjustable autonomy), multi-agent systems, and task scheduling techniques to the air traffic control domain. Consequently, we have created a testbed for investigating the critical challenges in supporting the early design of systems that allow for optimal, context-sensitive function (role) allocation between air traffic controller and automated agents. A pilot study has been conducted with the testbed and preliminary results show a marked qualitative improvement in using dynamic function allocation optimization versus static function allocation.

Contemporary processor allocation schemes for multicomputers suffer from a fragmentation problem which causes underutilization of the processing nodes. The RSR and ANCA schemes, based on the concepts of size-reduction and non-contiguous allocation, show considerable performance improvement. However the penalties associated with these schemes limit their usage in environments with memory-bounded or communication-intensive jobs. We propose to use simple directives provided by the users to apply the appropriate allocation scheme and thus maximize the performance benefit. We present such a hybrid processor allocation scheme which combines the previously proposed conventional allocation, RSR and ANCA schemes. The hybrid allocation scheme is evaluated via extensive simulation. It outperforms the conventional allocation scheme and can be implemented easily while maximizing the resource utilizations.

This paper proposes a new backward‐type speech coding system (ADPCM‐AB) for wideband (7 kHz) speech and sound signals. In this system, a split‐band adaptive predictive coding scheme with gradient PARCOR lattice filter and a dynamic bit allocation scheme are employed, where quantization bits are dynamically allocated over the subbands (frequency), the subintervals (time), and the channels (stereo) in accordance with the distribution of the residual energies. They serve to remove the redundancies due to the periodic concentration of the prediction residual energy and the nonuniform nature of the speech spectrum. The ADPCM‐AB needs neither longer delay time than 4 ms nor transmission of side information parameters because the parameters for predictive coding and dynamic bit allocation are calculated with the locally decoded signals. It is clarified that the ADPCM‐AB system has the best speech quality among the conventional backward‐type coding systems. It is also shown that this system provides speech quality subjectively equivalent to 11‐bit linear PCM (176 kb/s) at 64 kb/s.

The increasing popularity of Artificial Intelligence (AI) emphasizes the need for a deeper understanding of the factors that can influence its use. Providing accurate explanations may be a critical factor to help establish trust and increase the likelihood of the operator following an AI agent’s recommendations. However, the impact of explanations on human performance, trust, mental workload (MW) and situation awareness (SA), and confidence are not fully understood. In this paper, we investigate the effects of providing explanations about the agent’s recommendations (of varying levels of accuracy) on human operator’s performance, trust, MW, SA, and confidence compared to an agent that provides no explanations. Thirty individuals were divided into three groups and randomly assigned to an agent accuracy level (high, medium, low). Each participant completed two sessions using the AI agent: one with explanations, one without explanations. In each session, the user’s performance was measured objectively (number of anomalies correctly diagnosed and time to diagnosis), trust, MW, SA, and confidence in their response were measured using surveys. Participants’ performance, trust, SA, and confidence were higher in the sessions when the agent provided explanations for its recommendations. Accuracy had a significant effect on performance, MW and user confidence but not on trust, or SA.

Herein we delineate the relationship between poor joint human automation interaction (HAI) system performance and situation awareness (SA) focusing on the effect of system design. Degraded SA is one reason that human users make poor interaction decisions that cause joint system performance to suffer. One key reason for degraded SA is the hierarchical design where the human user is at the apex of the command hierarchy. Within this structure the human user is not able to be fully integrated into the system, which can lead to ‘out of the loop’ performance issues. We propose that SA could be measured in real time by leveraging psychophysiological methods often used in cognitive neuroscience research. We then discuss a potential framework that could not only identify degraded SA in real time, but could mitigate these degradations. Such a framework would be successful because it would genuinely integrate the human user into the system, essentially closing the loop that underlies joint system failures.

Sectoring is a group control algorithm that is used in elevator traffic control systems by grouping passengers that have common destinations or common origins into elevator cars that serve these floors. The building is split into sectors usually comprising contiguous floors. Two different alternative algorithms for sectoring are discussed in this paper. The first approach is based on dynamic allocation with equal sector allocation. The second approach is based on static allocation with unequal sector sizes. Under static allocation, the same elevator car is allocated to the same sector in every round trip. Under dynamic allocation, each elevator car is allocated to a different sector in each round trip. Under the dynamic allocation scheme suggested in this paper, the elevator cars are sequenced to the various sectors in the buildings in a round-robin fashion. It is important to note that under both schemes, the provided (relative) handling capacity of different sectors is equalised. Five different buildings have been analysed using the two suggested sectoring algorithms. The building is first designed by finding the required number and speed of elevators assuming conventional control. Each building is then analysed using one of the two suggested sectoring algorithms. In order to compare the performance of the two sectoring algorithms, the provided (relative) handling capacity is calculated. The provided (relative) handling capacity of the two suggested algorithms is then compared. Very little difference was found between the two algorithms. The dynamic sectoring with equal sector sizes offers the convenience of having equal sector sizes. The static sectoring with unequal sector sizes is more convenient for passengers that are familiar with the building. Practical application: This paper analyses two different options for sectoring the control system of an elevator system in a building. Each of the two sectoring methods is suitable for different situations. The dynamic allocation method is more suitable for destination group control systems and offers the group controller more flexibility. The static allocation method with unequal sector sizes ensures that the passengers remain familiar with the elevators that they use to get to their floors. The programmer of the elevator group controller can programme both methods in the controller and allow it to switch to the most suitable algorithm depending on the prevailing conditions.

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced. Second, the research status and the advantages and disadvantages of the two allocation models are analyzed. Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.

The integration of digital towers in air traffic control (ATC) intensifies visual complexity of controllers, increasing the risk of detection failure (DF) to warnings and compromising airspace safety. The inherent variability in human situational awareness and behaviors further complicates the differentiation and recognition of various DFs. This study deciphers DF by categorizing it into types based on Endsley’s situation awareness theory, identifying specific causes and key indicators. A four-phase framework—DF classification, DF induction experiment, gaze dynamics analytics, and DF-type recognition—was applied to gaze data from 26 subjects. Results revealed distinct gaze patterns for non-perception, unaware perception, and aware perception of warnings, with continuous warnings weakening operators’ awareness but enhancing foresight of warning implications. A random forest model achieved 80% precision in DF-type recognition, offering empirical support for real-time DF recognition and targeted interventions to improve visual warning detection and human-computer interaction in aviation safety.

A deep learning based dynamic bandwidth allocation method for XG-PON based mobile fronthaul for CRAN