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Purpose The purpose of this study is to apply the motivation–opportunity–ability (MOA) framework to investigate the relationships between ambiguity tolerance (AT), reflective thinking (RT) and performance in a complex task to predict knowledge-sharing intent. Design/methodology/approach In this study, 190 subjects performed a complex scheduling task in which they were randomly assigned to either participate in RT or not. Findings Results show that factors of the MOA framework positively predicted knowledge-sharing intent. In addition, RT significantly increased intention to share for individuals with low performance or with low AT. Research limitations/implications More research is needed to determine relationships between complex task performance and knowledge sharing, and the role of learning strategies, particularly self-directed ones such as RT. Future studies may use a larger sample size for more complex analysis. Practical implications RT may be used to create a sustainable and low-cost method of increasing knowledge sharing in complex tasks, without which those with low AT or low performance may not have participated. Originality/value The study supports the importance of contextual influences and points to how organizations can use RT in addition to individual motivation and ability to encourage knowledge sharing.

The aim of this study was to examine whether there are differences in the performance on simple and complex mathematical tasks depending on the personality traits and the presence of an audience. After completing the personality questionnaire, within the first experimental session, participants (N=70) solved one set of simple and one set of complex mathematical tasks. In the second session participants solved another set of simple and another set of complex tasks. In one of the sessions, participants were solving tasks in front of the audience, while in the other session the audience was absent. The results indicate that presence of an audience facilitates performance of those participants low on neuroticism, but only when they are solving simple tasks.

In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. For instance, size of the pre-trained VGG16 model trained on the ImageNet dataset is more than 500 MB. Resource-constrained devices such as mobile phones and internet of things devices have limited memory and less computation power. For real-time applications, the trained models should be deployed on resource-constrained devices. Popular convolutional neural network models have millions of parameters that leads to increase in the size of the trained model. Hence, it becomes essential to compress and accelerate these models before deploying on resource-constrained devices while making the least compromise with the model accuracy. It is a challenging task to retain the same accuracy after compressing the model. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. We have also discussed the challenges of the existing techniques and have provided future research directions in the field.

Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users. Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start. In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations. In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject. We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties. We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subject.

This meta-analysis investigates the direction and strength of the relationship between diversity in culturally diverse teams and team creativity/innovation. We distinguish the effects of two diversity levels (i.e., surface level vs. deep level) in culturally diverse teams and examine the moderators suggested by the socio-technical systems framework (i.e., team virtuality and task characteristics in terms of task interdependence, complexity, and intellectiveness). Surface-level diversity in culturally diverse teams is not related to team creativity/innovation, whereas deep-level diversity in culturally diverse teams is positively related to team creativity/innovation. Moreover, surfacelevel diversity in culturally diverse teams and team creativity/innovation are negatively related for simple tasks but unrelated for complex tasks. Deep-level diversity in culturally diverse teams and team creativity/innovation is positively related for collocated teams and interdependent tasks but unrelated for noncollocated teams and independent tasks. We discuss the theoretical and practical implications.

Peer feedback is introduced as an effective learning strategy, especially in large-size classes where teachers face high workloads. However, for complex tasks such as writing an argumentative essay, without support peers may not provide high-quality feedback since it requires a high level of cognitive processing, critical thinking skills, and a deep understanding of the subject. With the promising developments in Artificial Intelligence (AI), particularly after the emergence of ChatGPT, there is a global argument that whether AI tools can be seen as a new source of feedback or not for complex tasks. The answer to this question is not completely clear yet as there are limited studies and our understanding remains constrained. In this study, we used ChatGPT as a source of feedback for students’ argumentative essay writing tasks and we compared the quality of ChatGPT-generated feedback with peer feedback. The participant pool consisted of 74 graduate students from a Dutch university. The study unfolded in two phases: firstly, students’ essay data were collected as they composed essays on one of the given topics; subsequently, peer feedback and ChatGPT-generated feedback data were collected through engaging peers in a feedback process and using ChatGPT as a feedback source. Two coding schemes including coding schemes for essay analysis and coding schemes for feedback analysis were used to measure the quality of essays and feedback. Then, a MANOVA analysis was employed to determine any distinctions between the feedback generated by peers and ChatGPT. Additionally, Spearman’s correlation was utilized to explore potential links between the essay quality and the feedback generated by peers and ChatGPT. The results showed a significant difference between feedback generated by ChatGPT and peers. While ChatGPT provided more descriptive feedback including information about how the essay is written, peers provided feedback including information about identification of the problem in the essay. The overarching look at the results suggests a potential complementary role for ChatGPT and students in the feedback process. Regarding the relationship between the quality of essays and the quality of the feedback provided by ChatGPT and peers, we found no overall significant relationship. These findings imply that the quality of the essays does not impact both ChatGPT and peer feedback quality. The implications of this study are valuable, shedding light on the prospective use of ChatGPT as a feedback source, particularly for complex tasks like argumentative essay writing. We discussed the findings and delved into the implications for future research and practical applications in educational contexts.

The field of computational models of argument is emerging as an important aspect of artificial intelligence research. The reason for this is based on the recognition that if we are to develop robust intelligent systems, then it is imperative that they can handle incomplete and inconsistent information in a way that somehow emulates the way humans tackle such a complex task. And one of the key ways that humans do this is to use argumentation either internally, by evaluating arguments and counterarguments, or externally, by for instance entering into a discussion or debate where arguments are exchanged. As we report in this review, recent developments in the field are leading to technology for artificial argumentation, in the legal, medical, and e‐government domains, and interesting tools for argument mining, for debating technologies, and for argumentation solvers are emerging.

Artificial neural network (ANN) is the backbone of machine learning, specifically deep learning. The interpolating and learning ability of an ANN makes it an ideal tool for modelling, control and various other complex tasks. Fractional calculus (FC) involving derivatives and integrals of arbitrary non-integer order has recently been popular for its capability to model memory-type systems. There have been many attempts to explore the possibilities of combining these two fields, the most popular combination being the use of fractional derivative in the learning algorithm. This paper reviews the use of fractional calculus in various artificial neural network architectures, such as radial basis functions, recurrent neural networks, backpropagation NNs, and convolutional neural networks. These ANNs are popularly known as fractional-order artificial neural networks (FANNs). A detailed review of the various concepts related to FANNs, including activation functions, training algorithms based on fractional derivative, stability, synchronization, hardware implementations of FANNs, and real-world applications of FANNs, is presented. The study also highlights the advantage of combining fractional derivatives with ANN, the impact of fractional derivative order on performance indices like mean square error, the time required for training and testing FANN, stability, and synchronization in FANN. The survey reports interesting observations: combining FC to an ANN endows it with the memory feature; Caputo definition of fractional derivative is the most commonly used in FANNs; fractional derivative-based activation functions in ANN provide additional adjustable hyperparameters to the networks; the FANN has more degree of freedom for adjusting parameters compared to an ordinary ANN; use of multiple types of activation functions can be employed in FANN, and many more.

The manufacturing world is subject to ever-increasing cost optimization pressures. Maintenance adds to cost and disrupts production; optimized maintenance is therefore of utmost interest. As an autonomous learning mechanism reinforcement learning (RL) is increasingly used to solve complex tasks. While designing an optimal, model-free RL solution for predictive maintenance (PdM) is an attractive proposition, there are several key steps and design elements to be considered—from modeling degradation of the physical equipment to creating RL formulations. In this article, we survey how researchers have applied RL to optimally predict maintenance in diverse forms—from early diagnosis to computing a “health index” to directly suggesting a maintenance action. Contributions of this article include developing a taxonomy for PdM techniques in general and one specifically for RL applied to PdM. We discovered and studied unique techniques and applications by applying tf-idf (a text mining technique). Furthermore, we systematically studied how researchers have mathematically formulated RL concepts and included some detailed case-studies that help demonstrate the complete flow of applying RL to PdM. Finally, in Sect. 14, we summarize the insights for researchers, and for the industrial practitioner we lay out a simple approach for implementing RL for PdM.