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Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time.

With a focus on the Western-Canadian province of Manitoba, this article aims to develop a conceptual and empirical exploration of how apprenticeship learning can be transformed to meet the needs of Indigenous apprentices. Conceptually, the article layers an articulation of decolonising education onto apprenticeship learning in such a way as to explicitly support apprentices' prior knowledge and recognise the socio-political context within which apprenticeship learning occurs. Empirically, the article draws on qualitative in-depth interviews with Indigenous carpentry apprentices about their experiences with on-the-job training in order to better understand how Indigenous adult learners negotiate well-documented systemic barriers to education and employment. By contrasting participants' experiences in industry worksites to their experiences with Indigenous-centric curriculum offered by the Manitoba-based social enterprise BUILD, the article develops a discussion of how market-driven apprenticeship programmes limit the potential achievements of Indigenous learners and how a decolonising approach to apprenticeship learning that prioritises Indigenous epistemologies may result in higher levels of learner success. [ABSTRACT FROM AUTHOR]

Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real world, one might have access to expert trajectories in different environments where system dynamics is different while the learning task is the same. For such scenarios, two types of learning objectives can be defined. One where the learned policy performs very well in one specific environment and another when it performs well across all environments. To balance these two objectives in a principled way, our work presents the cross apprenticeship learning (CAL) framework. This consists of an optimization problem where an optimal policy for each environment is sought while ensuring that all policies remain close to each other. This nearness is facilitated by one tuning parameter in the optimization problem. We derive properties of the optimizers of the problem as the tuning parameter varies. We identify conditions under which an agent prefers using the policy obtained from CAL over the traditional apprenticeship learning. Since the CAL problem is nonconvex, we provide a convex outer approximation. Finally, we demonstrate the attributes of our framework in the context of a navigation task in a windy gridworld environment.

The purpose of the present study was to explore perceptions of shared reality in teacher-student partnerships in ballet, and how these perceptions were related to experiences of quality in the relationship and well-being. A longitudinal qualitative study design was adopted, with three female ballet students and their teacher participating in three semi-structured interviews each over an eight-month period. Data were analyzed using a combination of thematic and narrative analysis. Findings indicated that across the teacher-student relationships there were perceptions of a shared reality only on the professional and distanced level and not on a relational and personal level. This was achieved by a common experience of what matters in the world of ballet education as well as students finding their teacher as trustworthy. Furthermore, it appeared that the authoritarian apprenticeship culture in ballet, where the teachers give clear instructions and feedback, and where the student role is to listen and adapt to the teacher’s instructions, was a barrier to fully achieving a shared reality on a personal and deeper level. In turn, this influenced the quality of the relationship and the wellbeing of both teacher and students. The present study offers critical reflections on the cultural backdrop of teaching and learning in ballet and highlights the importance of teachers to facilitate a supportive, unconditional, and trustworthy relationship so that they can work together in a more productive manner.

From a knowledge transfer (KT) perspective, this study explores the rationale for chefs' mentorship-based abuse in professional kitchens. Qualitative data from five hospitality firms show that such abuse among chefs is inherently related to "master/apprentice" KT practices. Essentially, the master/apprentice KT is a social exchange where the master imparts practical knowledge, and apprentices reciprocate with social rewards. However, due to the lack of monetary pricing/payment systems, participants' agreements on knowledge exchange issues (i.e., duties, powers and benefits) are often informal and implicit, relying more on their mutual trust than the organization's managerial support. Given this, hospitality managers prefer authorizing masters to self-manage specific KT practices. Correspondingly, mentorship-based abuse becomes a prevalent mentoring strategy for masters to improve their KT performance and mentoring rewards. Overall, this study contributes to the research on mentorship-based abuse in hospitality by proposing a novel framework encompassing young employees' apprentice learning and KT-oriented social interactions. • Mentorship-based abuse in kitchens is linked to knowledge transfer and master-apprentice social exchanges. • Knowledge transfer challenges and master self-management rationalizes and perpetuate abusive mentoring strategies. • Improving knowledge systems and mentoring structures can reduce mentorship-based abuse in hospitality. [ABSTRACT FROM AUTHOR]

Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision process from examples provided by the teacher. The reward function is often considered the most succinct description of a task. In simple applications, the reward function may be known or easily derived from properties of the system and hard coded into the learning process. However, in complex applications, this may not be possible, and it may be easier to learn the reward function by observing the actions of the teacher. This paper provides a comprehensive survey of the literature on IRL. This survey outlines the differences between IRL and two similar methods - apprenticeship learning and inverse optimal control. Further, this survey organizes the IRL literature based on the principal method, describes applications of IRL algorithms, and provides areas of future research. [ABSTRACT FROM AUTHOR]

Inverse reinforcement learning features a reward function based on reward features and positive demonstrations. When complex learning tasks are performed, the entire state space is used to form the set of reward features, but this large set results in a long computational time. Retrieving important states from the full state space addresses this problem. This study formulated a method that extracts critical features by combining negative and positive demonstrations of searching for critical states from the entire state space to increase learning efficiency. In this method, two types of demonstrations are used: positive demonstrations, which are given by experts and agents imitate, and negative demonstrations, which demonstrate incorrect motions to be avoided by agents. All significant features are extracted by identifying the critical states over the entire state space. This is achieved by comparing the difference between the negative and positive demonstrations. When these critical states are identified, they form the set of reward features, and a reward function is derived that enables agents to learn a policy using reinforcement learning quickly. A speeding car simulation was used to verify the proposed method. The simulation results demonstrate that the proposed approach allows an agent to search for a positive strategy and that the agent then displays intelligent expert-like behavior. [ABSTRACT FROM AUTHOR]

Hyper-heuristics are general purpose search methods for solving computationally difficult problems. A selection hyper-heuristic is composed of two key components: a heuristic selection method and move acceptance criterion. Under an iterative single-point search framework, a solution is modified by selecting and applying a predefined low-level heuristic, with a decision then taken to accept or reject the resulting solution. Designing a selection hyper-heuristic is an extremely challenging task. In this study, we investigate computer-aided design of a selection hyper-heuristic for the open vehicle routing problem. A time delay neural network is used as an offline apprenticeship learning method. Our approach first observes the search behaviour of multiple expert human-designed selection hyper-heuristics on a selected sample of training instances, before automatically generating a selection hyper-heuristic capable of solving unseen instances effectively. The proposed approach is tested on open vehicle routing problem instances of different sizes to examine the performance and generality of the selection hyper-heuristics generated. Improved performance is demonstrated over a set of well-known benchmarks from the literature when compared to using the existing expert systems directly. [ABSTRACT FROM AUTHOR]

Quantitative analysis in this special issue (Greenfield, Brown, & Du, 2021) showed that the COVID-19 pandemic has led most parents to report greater expectations for their children to help with family subsistence. This familistic development exemplifies the shifts in behavior and values predicted by Greenfield's Theory of Social Change, Cultural Evolution, and Human Development when survival concerns rise and the social world retracts. Here, we use qualitative analysis to uncover the psychological processes behind the quantitative shift. Our California sample consisted of 109 parents with at least one child between age 7 and 18 living at home during the pandemic when they answered the survey. Forty-six of these parents provided qualitative data concerning expectations for their children's household responsibilities during COVID-19. An open-ended question asked parents to explain why their expectations of their children to help around the house and to carry out self-maintenance had changed or remained the same. Prominent themes in the qualitative responses manifest a shift from a mindset found in a large-scale urban society toward that found in a small-scale subsistence community: Before the pandemic, parents focused on increasing their children's competitiveness in society through extracurricular activities like tutoring, but that transitioned into a focus on household duties such as cooking and cleaning. In some cases, this shift was linked to an increase in life satisfaction; in other cases, it was linked to a decline in life satisfaction.

Reinforcement learning (RL) is a data‐driven approach to synthesizing an optimal control policy. A barrier to wide implementation of RL‐based controllers is its data‐hungry nature during online training and its inability to extract useful information from human operator and historical process operation data. Here, we present a two‐step framework to resolve this challenge. First, we employ apprenticeship learning via inverse RL to analyze historical process data for synchronous identification of a reward function and parameterization of the control policy. This is conducted offline. Second, the parameterization is improved online efficiently under the ongoing process via RL within only a few iterations. Significant advantages of this framework include to allow for the hot‐start of RL algorithms for process optimal control, and robust abstraction of existing controllers and control knowledge from data. The framework is demonstrated on three case studies, showing its potential for chemical process control. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we present an original decision support algorithm to assist the anesthesiologists delivery of drugs to maintain the optimal Depth of Anesthesia (DoA). Methods: Derived from a Transform Predictive State Representation algorithm (TPSR), our model learned by observing anesthesiologists in practice. This framework, known as apprenticeship learning, is particularly useful in the medical field as it is not based on an exploratory process – a prohibitive behavior in healthcare. The model only relied on the four commonly monitored variables: Heart Rate (HR), the Mean Blood Pressure (MBP), the Respiratory Rate (RR) and the concentration of anesthetic drug (AAFi). Results: Thirty-one patients have been included. The performances of the model is analyzed with metrics derived from the Hamming distance and cross entropy. They demonstrated that low rank dynamical system had the best performances on both predictions and simulations. Then, a confrontation of our agent to a panel of six real anesthesiologists demonstrated that $\boldsymbol{95.7}$ % of the actions were valid. Conclusion: These results strongly support the hypothesis that TPSR based models convincingly embed the behavior of anesthesiologists including only four variables that are commonly assessed to predict the DoA. Significance: The proposed novel approach could be of great help for clinicians by improving the fine tuning of the DoA. Furthermore, the possibility to predict the evolutions of the variables would help preventing side effects such as low blood pressure. A tool that could autonomously help the anesthesiologist would thus improve safety-level in the surgical room. [ABSTRACT FROM AUTHOR]

Rather than using baseline-oriented approaches, autonomous spacecraft trajectory planning within asteroid systems may be based on human behavior that is learned by demonstration. In this paper, we conducted a numerical experiment to observe a human agent steering in real-time spacecraft motion within simulated binary asteroid environments. The resulting collection of human-planned trajectories demonstrates effective guidance mechanisms and reveals underlying path-planning processes that may serve for more agile and autonomous asteroid exploration. [ABSTRACT FROM AUTHOR]

In cognitive radio, Reinforcement Learning (RL) has been widely applied to the construction of cognition engine. However, two crucial challenges remain to be resolved: First, it takes long time to interact with the environment before reaching intelligent decision. Second, agents improve their performance through trial and error, but some applications in Cognitive Radio Networks (CRN) cannot afford the extensive latency and energy expenditure. An apprenticeship learning scheme based on expert demonstrations is adopted for solving above problems. Firstly, the reinforcement responsibility rating is introduced to enhance the efficiency of power allocation by allowing multi-level transition for the transmit power. In order to avoid the failure of expert node identification due to SU's remote location, an adaptive radius Bregman Ball model is presented. Furthermore, the Multi-Teacher Deep Q-learning from Demonstrations (MT-DQfD) is proposed to accelerate the learning procedure by sharing demonstrations derived from multiple expert nodes. Our experiments illustrate that the proposed cross-layer routing protocol reduces the training period while improves transmission quality compared to the traditional algorithms. Moreover, the newly-joined nodes can achieve better performance than the experts. [ABSTRACT FROM AUTHOR]

Efficiency and safety are vital for aviation operations in order to improve the combat capacity of aircraft carrier. In this article, the theory of apprenticeship learning, as a kind of artificial intelligence technology, is applied to constructing the method of automated scheduling. First, with the use of Markov decision process frame, the simulative model of aircrafts launching and recovery was established. Second, the multiplicative weights apprenticeship learning algorithm was applied to creating the optimized scheduling policy. In the situation with an expert to learn from, the learned policy matches quite well with the expert's demonstration and the total deviations can be limited within 3%. Finally, in the situation without expert's demonstration, the policy generated by multiplicative weights apprenticeship learning algorithm shows an obvious superiority compared to the three human experts. The results of different operation situations show that the method is highly robust and well functional. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing 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.)

Aircraft detection has attracted increasing attention in the field of remote sensing image analysis. Complex background, illumination change and variations of aircraft kind and size in remote sensing images make the task challenging. In our work, we propose an effective aircraft detection framework based on reinforcement learning and a convolutional neural network (CNN) model. Aircraft in remote sensing images can be accurately and robustly located with the help of the searching mechanism that the candidate region is dynamically reduced to the correct location of aircraft, which is implemented through reinforcement learning. The detection framework overcomes the difficulties that the current detection methods based on reinforcement learning are only able to detect a fixed number of objects. Specifically, we adopt the restricted EdgeBoxes that generate the high-quality candidate boxes through the prior aircraft knowledge at first. Then, we train an intelligent detection agent through reinforcement learning and apprenticeship learning. The detection agent accurately locates the aircraft in the candidate boxes within several actions, and it even performs better than the greed strategy in apprenticeship learning. During the final detection step, we carefully design the CNN model that predicts the probability that the localization result generated by the detection agent is an aircraft. Comparative experiments demonstrate the accuracy and efficiency of our aircraft detection framework. [ABSTRACT FROM AUTHOR]

This article addresses the question of whether early Pleistocene hominins are plausibly viewed as having possessed a protolanguage, that is, a communication system exemplifying some but not all of the distinctive features of fully modern human language. I argue that the answer is 'yes,' mounting evidence from the early Pleistocene 'lithics niche.' More specifically, I first describe a cognitive platform that I think would have been sufficient, given appropriate socio-ecological conditions, for the creation and retention of a protolanguage. Then, using archaeological evidence pertaining to hominin lithic behavior from the early Pleistocene, I attempt to make plausible the idea that each of these cognitive abilities were in fact in place by this point in hominin evolutionary history and also that the requisite socio-ecological conditions were satisfied. [ABSTRACT FROM AUTHOR]

• Expert demonstration data of hybrid electric vehicle energy management are represented by domain adaptive meta-learning. • By embedding expert demonstration model, an apprenticeship-reinforcement learning framework is established to improve fuel economy. • It is verified under three hybrid vehicle models with different powertrain architectures and several driving cycles. • Compared to traditional reinforcement learning, the apprenticeship-reinforcement learning can effectively improve the fuel emergency and CO 2 emissions. Deep reinforcement learning (DRL) is a potential solution to develop efficient energy management strategies (EMS) for hybrid electric vehicles (HEV) that can adapt to the changing topology of electrified powertrains and the uncertainty of various driving scenarios. However, traditional DRL has many disadvantages, such as low efficiency and poor stability. This study proposes an apprenticeship-reinforcement learning (A-RL) framework based on expert demonstration (ED) model embedding to improve DRL. First, the demonstration data, calculated by dynamic programming (DP), were collected, and domain adaptive meta-learning (DAML) was used to train the ED model with the adaptive capability of working conditions. Then combined apprenticeship learning (AL) with DRL, and the ED model was used to guide the DRL to output action. The method was validated on three HEV models, and the results show that the training convergence rate increases significantly under the framework. The average increase that the apprenticeship-deep deterministic policy gradient (A-DDPG) based method applied to three HEVs achieved was 34.9 %. Apprenticeship-twin delayed twin delayed deep deterministic policy gradient (A-TD3) achieved 23 % acceleration in the power-split HEV. Because A-DDPG's EMS is more forward-looking and can mimic ED to some extent, the frequency of engine operation in the high-efficiency range has increased. Therefore, A-DDPG can improve the fuel economy of the series hybrid electric bus (HEB) by 0.2–2.7 %, and improvements averaged to about 9.6 % in the series–parallel HEV while maintaining the final SOC. This study aims to improve the sampling efficiency and optimal performance of EMS-based DRL and provide a basis for the design and development of vehicle energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

This article reports on an ongoing study that investigates the development of expertise in rugby league and Australian football by Indigenous Australians as a socially and culturally situated process of learning. Focused on the sampling phase of the Development Model of Participation in Sport (6–12 years of age), it combines narrative inquiry and grounded theory methodologies to identify the important role that participation in a range of different sports and in informal games plays in the participants’ development of expertise, as a process of socialisation. [ABSTRACT FROM PUBLISHER]

Copyright of HAU: Journal of Ethnographic Theory is the property of University of Chicago Press 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.)

We propose a novel spectrum decision scheme (i.e., channel selection and handoff) for wireless mesh networks (WMN) which use multiple channels and nodes equipped with multi-beam directional antennas. Our scheme has the following features: (i) It performs spectrum decision by considering various WMN parameters, including the channel quality, beam orientation, antenna-caused deafness and capture effects, and application priority level. (ii) It uses the reinforcement learning (RL)-based spectrum decision process to achieve the optimal quality of multimedia transmission in the long term. However, a newly-joined WMN node could take a long time to make a correct spectrum decision due to the difficult choice of initial RL parameters. Therefore, our scheme uses the apprenticeship learning in conjunction with the RL model, to speed up the spectrum decision process by choosing a suitable neighboring node (called “expert”) to teach a newly-joined node (called “apprentice”). Our experiments demonstrate that the proposed spectrum decision scheme improves the network performance and multimedia transmission quality. [ABSTRACT FROM PUBLISHER]

Aircraft detection has attracted increasing attention in the field of remote sensing image analysis. Complex background, illumination change and variations of aircraft kind and size in remote sensing images make the task challenging. In our work, we propose an effective aircraft detection framework based on reinforcement learning and a convolutional neural network (CNN) model. Aircraft in remote sensing images can be accurately and robustly located with the help of the searching mechanism that the candidate region is dynamically reduced to the correct location of aircraft, which is implemented through reinforcement learning. The detection framework overcomes the difficulties that the current detection methods based on reinforcement learning are only able to detect a fixed number of objects. Specifically, we adopt the restricted EdgeBoxes that generate the high-quality candidate boxes through the prior aircraft knowledge at first. Then, we train an intelligent detection agent through reinforcement learning and apprenticeship learning. The detection agent accurately locates the aircraft in the candidate boxes within several actions, and it even performs better than the greed strategy in apprenticeship learning. During the final detection step, we carefully design the CNN model that predicts the probability that the localization result generated by the detection agent is an aircraft. Comparative experiments demonstrate the accuracy and efficiency of our aircraft detection framework.

Background: Elite athletes often take part in group trainings and use teammates as learning resources. Despite this, research on the training and learning of elite athletes tends to characterise this training and learning as primarily individual. Purpose: This study, explores interrelated learning processes among elite athletes by exploring the performance-related learning that takes place between elite trampoline athletes in their training environment. The case will be made that such learning may be described most accurately as apprenticeship learning. Participants: The research focuses on a case study involving two Danish synchronised trampoline jumpers, Daniel and Peter, and their training as part of the Danish national trampolining team. Data collection: The data were generated through participant observation. During 10 days of observations, the principal researcher held informal talks with the athletes and the coach and took descriptive field notes. At the conclusion of the observation period, each athlete submitted to an individual, semi-structured interview. Data analysis: A theoretical reading of the data was carried out to facilitate interpretations that went beyond observations and the athletes' own descriptions, in order to reach a deeper understanding of how practice facilitates learning. Results: We encircle the athletes' interrelated learning processes by introducing the training environment of the national team and situations in which the athletes guide each other verbally or by jumping together. Discussion: We argue that the practice of the Danish national trampolining team can be considered a community of practice. Taking point of departure in our theoretical perspective we discuss how verbal and tacit bodily exchanges between athletes become opportunities for learning. Conclusion: In a practice containing varied resources for learning, we show that athletes can be each others' performance analysts, guiding each other through the use of metaphors and cues which disclose the practical meaning of how to overcome specific practical challenges. We also show that the athletes can be each others' sparring partners when they perform their sport together. In this form of interaction they directly feel and impact the other's performance, which means that both athletes encounter opportunities for learning. [ABSTRACT FROM AUTHOR]