Online medical consultation is one of important mobile services worldwide, in which patients can make consultation more conveniently through phones anytime and anywhere. However, expert-level online consultations are expensive due to the shortage of medical professionals, while AI models are unreliable because they have unpredictable risks. Therefore, we introduce human-machine collaboration to medical online consultation and focus on symptom inquiry, as the basis for disease diagnosis. There are two key issues: 1) how to design an intelligent assignment strategy that can determine which doctors or models participate in each turn? 2) how to design an effective execution strategy that can improve the machine's inquiry ability among considerable symptoms? To address the above issues, we propose the Human-AI Diagnostic Team (HADT) framework based on Hierarchical Reinforcement Learning, which aims to achieve high accuracy with low manpower. Specifically, HADT has two layers. The upper one is responsible for assignment, in which we propose a module called master that enables intelligent human-machine assignments through the masked reinforcement learning with reward shaping. The lower one is responsible for execution, consisting of a doctor and a proposed module called machine. This module can effectively ask about symptoms through the masked hierarchical reinforcement learning with bottom-up training. Experiments on the public datasets show that HADT can achieve up to 89.4% accuracy with only 10.9% human effort, as confirmed by real clinical doctors using the designed interface with mobile devices.

Collaborative agents, consisting of at least one human and one mobile robot agent working toward a common objective, are increasingly prevalent and effective in both social and industrial spheres, such as manufacturing. The inherent heterogeneity of these agents requires efficient and scalable Task Scheduling and Allocation (TSA) schemes that match individuals to tasks based on their abilities and meet specific temporal constraints, maximizing performance in less time. Existing works face challenges as exact methods rely on assumptions and deterministic models, which struggle to scale and infer time-varying, stochastic human task performance. While offline reinforcement learning shows promise, it is time-consuming and heavily dependent on training data that is often scarce in practical factory settings. To address these challenges, we formulate the TSA problem in mobile multi-agent teams as a temporal-constrained contextual decision-making process and propose the Bayesian Optimization-augmented Team coordination among Heterogeneous agents (BOTH), a novel scalable and training-free scheduling approach. The core idea is to use Gaussian Processes (GP) to iteratively infer agent dynamics in real-time, enabling the automatic derivation of a robust TSA solution that requires no prior data and adapts to varying problem sizes. We start by employing a heterogeneous graph-based encoder to extract representative context from the individual differences among team agents and tasks, considering strict temporal constraints. Following this, we propose a GP-driven Bayesian optimizer to intelligently explore and exploit optimal task assignments for each context, without making assumptions about the system. Experiments on synthetic and real datasets demonstrate that BOTH boosts accuracy and time efficiency compared to competing baselines, even within a few iterations.

Artificial Intelligence (AI) systems rapidly advance in online medical consultations, where doctors diagnose through online dialogue. Recent AI models have made significant progress in symptom inquiry; however, the disease diagnosis accuracy remains low and unreliable, failing to replace doctors’ role fully. Although some studies attempt to assist doctors by providing AI-generated advice, this advice often has high error rates and lacks complementarity, needing further improvement. Therefore, we aim to introduce a reliable and effective human-AI collaboration system. There are two key challenges. 1) How to design an advice strategy that improves the accuracy of the advice? 2) How to develop an optimal AI teammate for the human-AI team to enhance the overall team utility? To address these challenges, we propose the Human-AI collaboration diagnosis framework with Combination advice (HAComb). Specifically, to ensure the accuracy of advice, we introduce a human-AI combination advice that uses Bayesian methods to integrate doctors’ predicted labels with AI model outputs. To enhance team utility, we design a loss function that incorporates both AI loss and team utility loss. Experiments on four real-world datasets show that HAComb outperforms single human and other human-AI collaboration methods in diagnosis accuracy and team utility.

Recent studies indicate that human-AI collaboration performs better than either alone, particularly in medical diagnosis. Beyond collaboration methods that focus on assigning tasks to humans or AI, like deferral, combining human and AI decisions with their confidence scores is emerging as a promising strategy. Due to high cognitive load, doctors often struggle to provide confidence assessments, necessitating explicit human uncertainty evaluation through a limited number of additional expert predictions. There are two challenges. (1) how to actively collect limited yet representative expert predictions? (2) how to accurately evaluate human uncertainty with limited expert predictions? To address the challenges, we propose ActiveHAI, an active human-AI diagnosis method that reduces expert costs through a median-window sampling strategy that actively selects representative samples near the estimated median; and evaluate expert confidence through an evaluator module that integrates sample features and expert predictions, converting them into probability distributions. Experiments on three real-world datasets show that ActiveHAI surpasses doctor and other human-AI methods by 16.3% and 3.6% in accuracy, respectively. Furthermore, ActiveHAI reaches 97.2% relative accuracy, even with just eight expert predictions per class.

While artificial intelligence (AI) technology has advanced in real-world applications, there is a strong motivation to develop hybrid systems where AI algorithms and humans collaborate, promoting more human-centered approaches in AI system design. This has led to the emergence of a novel human-machine computing (HMC) paradigm, which combines human cognitive abilities with machine computational power to create a collaborative computing framework that meets the demands of large-scale, complex tasks and enables human-machine symbiosis. Human processing units (HPUs) are crucial computing resources in HMC-oriented systems, and efficient HPU resource provisioning is key to boosting system performance. However, existing schemes often fail to assign tasks to the most suitable HPUs and optimize HPU utility, as they either cannot quantitatively measure skills or overlook utility concerns during task assignment and scheduling. To address these challenges, this article proposes a fine-grained HPU virtualization (FingHV) approach, which leverages virtualization techniques to improve flexibility, fairness, and utility in the provisioning process. The core idea is to use a tree-based skill model to precisely measure the levels and correlations of multiple skills within individual HPUs, and to apply a mixed time/event-based scheduling policy to maximize HPU utility. Specifically, we begin by proposing a hierarchical multiskill tree to model HPU skills and their correlations. Next, we formulate the HPU virtualization problem and present a fine-grained virtualization method, which includes a quality-driven HPU assignment process and a mixed time/event-based scheduling policy to improve resource-sharing efficiency. Finally, we evaluate FingHV on a synthetic dataset with varying task sizes and a real-world case. The results demonstrate that FingHV improves global matching quality by up to 39.7% and increases HPU utility by 11.2% compared to the baselines.

Online disease diagnosis, gathering the patients’ symptoms and making diagnoses through online dialogue, grows rapidly worldwide. Manual-based approach, e.g., Haodaifu, employs real-world doctors, providing high-quality but high-cost medical services. In contrast, machine-based approach, e.g., 01bot, that utilizes machine learning models can make automatic diagnosis but lacks reliable accuracy. While some work has enabled human-AI collaboration in disease diagnosis, their collaboration pattern is simple and needs to be further improved. Therefore, we aim to introduce a doctor-enhanced and low-cost human-AI collaboration pattern. There are two key challenges. 1) How to utilize expert knowledge in doctor feedback to enhance AI’s capability? 2) How to design a collaboration workflow to achieve a low-cost doctor workload while ensuring accuracy? To address the above challenges, we propose the Human-AI collaboration framework for disease diagnosis via doctor-enhanced transformer, called HAIformer. Specifically, to enhance AI’s capability, we propose a machine module that leverages doctors’ medical knowledge through doctor-enhanced attention, using a graph attention-based matrix; to reduce doctor workload, we propose an activation module that uses two units in a cascading manner for human-AI allocation. Experiments on four real-world datasets show that HAIformer can achieve up to 91.2% accuracy with only 18.9% human effort and one-third of dialogue turns. Further real-world clinic study highlights its advantages in practical applications.

As AI-enabled machines become increasingly prevalent, there is a strong impetus to harness the complementary strengths of humans and machines to enhance productivity and reduce costs in collaborative workspaces such as manufacturing and warehouses [1]. However, efficient team coordination remains challenging due to the heterogeneity of team agents and the dynamic nature of human agents. Existing exact methods often rely on assumptions and mathematical models, which struggle to scale and accurately predict time-varying human performance [2]. While offline Reinforcement Learning (RL) demonstrates potential, it is time-consuming and heavily reliant on training data, often limited in practical factory settings [3]. Therefore, a scalable and data-efficient team coordination method that considers the varying capabilities of heterogeneous agents in collaborative systems is urgently needed to facilitate effective human-machine partnerships.

Medical online consultation is important to healthcare worldwide, with hundreds of millions of participants each year. However, expert-level online consultations are expensive due to the shortage of medical professionals, while AI models are unreliable because they have unpredictable risks. Therefore, we introduce human-machine collaboration to medical online consultation and focus on symptom inquiry, as the basis for disease diagnosis. There are two key issues: 1) how to design an intelligent assignment strategy that can determine whether doctors or models participate in each turn? 2) how to design an effective execution strategy that can improve the machine's inquiry ability among considerable symptoms? To address the above issues, we propose the Human-AI Diagnostic Team (HADT) framework based on Hierarchical Reinforcement Learning (HRL), which aims to achieve high accuracy with low manpower. Specifically, HADT has two layers. The upper one is responsible for assignment, in which we propose a module called master that enables intelligent human-machine assignments through the masked RL with reward shaping. The lower one is responsible for execution, consisting of a doctor and a proposed module called machine. This module can effectively ask about symptoms through the masked HRL with bottom-up training. Experiments on the public datasets show that HADT can achieve up to 89.4% accuracy with only 10.9% human effort, as confirmed by real clinical doctors using our online interface.

With rapid advancements in artificial intelligence (AI) technologies, AI-powered machines are increasingly capable of collaborating with humans to enhance decision-making in various human–machine collaboration scenarios, e.g., medical diagnosis, criminal justice, and autonomous driving. As a result, human–machine computing (HMC) has emerged as a promising computing paradigm that integrates the expertise of humans with the reliable data processing capabilities of machines. Using HMC to facilitate the processing of domain-specific tasks has a lot of potential, but is limited in system-level scalability, i.e., there is no one common easy-to-use interface. In this article, we present human-machine operating system(hmOS), an open extensible platform for researchers to experiment with HMC for investigating system-centric human–machine collaboration problems.hmOSsupports flexible human–machine collaboration on the strength of the quality-aware task decomposition and allocation. To achieve that, the underlying system architecture and runtime environment are first developed to build a foundational abstraction for the kernel ofhmOS. Second,hmOSfacilitates flexible human–machine collaboration through a suitability-based task allocation mechanism, quality estimation guided by fuzzy rules, and iterative feedback on result tuning. We implement the newly proposedhmOSin a prototype featuring interactive interfaces. Finally, we conduct extensive and realistic experiments to validate the effectiveness of our platform across diverse tasks, showcasing the broad feasibility ofhmOS.

Code translation, i.e., translating one kind of code language to another, plays an important role in scenarios such as application modernization and multi-language versions of applications on different platforms. Even the most advanced machine-based code translation methods can not guarantee an error-free result. Therefore, the participance of software engineer is necessary. Considering both accuracy and efficiency, it is suggested to work in a human-machine collaborative way. However, in many realistic scenarios, human and machine collaborate ineffectively - model translates first and then human makes further editing, without any interaction. To solve this problem, we propose hmCodeTrans, a novel method that achieves code translation in aninteractive human-machine collaborative way. It can (1) save the human effort by introducing two novel human-machine collaboration patterns: prefix-based and segment-based ones, which feed the software engineer's sequential or scattered editing back to model and thus enabling the model to make a better retranslation; (2) reduce the response time based on two proposed modules: attention cache module that avoids duplicate prefix inference with cached attention information, and suffix splicing module that reduces invalid suffix inference by splicing a predefined suffix. The experiments are conducted on two real datasets. Results show that compared with the baselines, our approach can effectively save the human effort and reduce the response time. Last but not least, a user study involving five real software engineers is given, which validates that the proposed approach owns the lowest human effort and shows the users’ satisfaction towards the approach.

Abstract Program translation aims to translate one kind of programming language to another, e.g., from Python to Java. Due to the inefficiency of translation rules construction with pure human effort (software engineer) and the low quality of machine translation results with pure machine effort, it is suggested to implement program translation in a human–machine cooperative way. However, existing human–machine program translation methods fail to utilize the human’s ability effectively, which require human to post-edit the results (i.e., statically modified directly on the model generated code). To solve this problem, we propose HMPT (Human-Machine Program Translation), a novel method that achieves program translation based on human–machine cooperation. It can (1) reduce the human effort by introducing a prefix-based interactive protocol that feeds the human’s edit into the model as the prefix and regenerates better output code, and (2) reduce the interactive response time resulted by excessive program length in the regeneration process from two aspects: avoiding duplicate prefix generation with cache attention information, as well as reducing invalid suffix generation by splicing the suffix of the results. The experiments are conducted on two real datasets. Results show compared to the baselines, our method reduces the human effort up to 73.5% at the token level and reduces the response time up to 76.1%.

Knowledge-enhanced dialogue systems aim at generating factually correct and coherent responses by reasoning over knowledge sources, which is a promising research trend. The truly harmonious human-agent dialogue systems need to conduct engaging conversations from three aspects as humans, namely (1) stating factual contents (e.g., records in Wikipedia), (2) conveying subjective and informative opinions about objects (e.g., user discussions on Twitter), and (3) impressing interlocutors with diverse expression styles (e.g., personalized expression habits). The existing knowledge base is a standardized and unified coding for factual knowledge, which could not portray the other two kinds of knowledge to make responses more informative and expressive diverse. To address this, we present CrowdDialog , a crowd intelligence knowledge-enhanced dialogue system, which takes advantage of “crowd intelligence knowledge” extracted from social media (with rich subjective descriptions and diversified expression styles) to promote the performance of dialogue systems. Firstly, to thoroughly mine and organize the crowd intelligence knowledge underlying large-scale and unstructured online contents, we elaborately design the C rowd I ntelligence K nowledge G raph ( CIKG ) structure, including the domain commonsense subgraph, descriptive subgraph, and expressive subgraph. Secondly, to reasonably integrate heterogeneous crowd intelligence knowledge into responses while ensuring logicality and fluency, we propose the G ated F usion with D ynamic Knowledge- D ependent ( GFDD ) model, which generates responses from the semantic and syntactic perspective with the context-aware knowledge gate and dynamic knowledge decoding. Finally, extensive experiments over both Chinese and English dialogue datasets demonstrate that our approach GFDD outperforms competitive baselines in terms of both automatic evaluation and human judgments. Besides, ablation studies indicate that the proposed CIKG has the potential to promote dialogue systems to generate fluent, informative, and diverse dialogue responses.

As an enabler of sixth-generation communication technology (6G), Federated Learning (FL) triggers a paradigm shift from "connected things" to "connected intelligence". FL implements on-device learning, where massive end devices jointly and locally train a model without private data leakage. However, FL suffers from problems of low accuracy and convergence rate when no data is shared to the central server and the data distribution is non-IID. In recent years, attempts have been made on hybrid FL, where very small amounts of data (e.g., less than 1%) is shared from the participants. With the opportunities brought by shared data, we notice that the server is capable of receiving the data in order to assist the FL process and mitigate the challenge of non-IID. Notably, existing hybrid FL only applies the model-level technologies belonging to the traditional FL and does not make full use of the characteristics of shared data to make targeted improvements. In this paper, we propose FedAux, a novel hybrid FL method at knowledge-level, which utilizes shared data to construct an auxiliary model and then transfer general knowledge to traditional aggregated model or client model for enhancing the accuracy of global model and speeding up the convergence of global model. We also propose two specific knowledge transfer strategies named c-transfer and i-transfer. We conduct extensive analysis and evaluation of our methods against the well-known FL methods, FedAvg and Hybrid-FL protocol. The results indicate that FedAux shows higher accuracy (10.89%) and faster convergence rate compared with other methods.

We witnessed great advancement in Artificial Intelligence (AI) powered technologies in recent years, and yet, when applied to certain high-stake contexts, such as medical diagnosis, automatic driving and criminal justice, they are not qualified. This matter can be greatly settled by Human-Machine Computing (HMC), which is an effective computing paradigm that couples the expertise and demonstration abilities of humans with the high-performance computing power of machines. This work studies an optimal task scheduling problem for HMC systems, where various tasks are decomposed and dispatched to humans and AI-enabled machines to provide significantly better benefits compared to either type of computing resources in isolation. However, designing such optimal task scheduling is challenging because of the stochastic hybrid features of machines, as well as various human professional abilities. Considering the Quality of Service (QoS) and the heterogeneity of human-machine computing resources, we propose CoupHM, a feasible task scheduler using gradient based optimization for HMC systems. In particular, we firstly present the underlying architecture of HMC system and details of the task-driven workload model. On that basis, we then formulate the objective optimization problem to be solved and describe the composition of the CoupHM scheduler. Finally, the performance of our solution is evaluated by the simulation experiments, and the results indicate that the proposed scheduler has preferable performance both in balancing resources and guaranteeing QoS, which can serve as guidelines for future research on HMC systems.

Online medical diagnosis refers to diagnosing diseases and providing treatment suggestions on the websites. It develops rapidly and has become a new choice for patients to seek medical treatment. Although manual online medical diagnosis is reliable, it has problems such as low efficiency, heavy burden on doctors, and long waiting time for patients. Relying on machines for automatic disease diagnosis is highly efficient, which, however, has low accuracy and reliability. In general, online medical diagnosis usually has two stages: inquiry and diagnosis. Inquiry stage refers to asking about the patient’s physiological, where the questions are usually streamlined, and thus can be handled by the machine. Diagnosis stage is to diagnose the disease and provide medical recommendations, which has strict requirements for accuracy and safety, and thus should be handled by the human. Inspired by this, in the paper we propose a human-machine collaboration based online medical diagnosis system, i.e., HM-MDS. In inquiry stage, the system employs the machine. It uses the BERT+CRF to identify symptoms in the patient’s dialogue and uses a DQN-based method to ask about symptoms. In diagnosis stage, the system employs both the machine and the human. The machine generates a pre-diagnosis result by calculating disease probability. Then the human doctor gives the final diagnosis result by checking the pre-diagnosis result and revising it if necessary. Obviously, HM-MDS can effectively save human doctor’s time as well as patient’s time, while ensure the accuracy of the diagnosis result. We conduct experiments on a real-world dataset. The results show our approach improves the online medical diagnosis’s reliability as well as patient satisfaction, and ensures diagnosis accuracy. The time cost for a reliable medical diagnosis is reduced to 36% compared with pure manual work.

Crowd-inspired intelligent manufacturing space (CrowdIM) aims to leverage the aggregated power of heterogeneous human–machine–things (HMT) agents for improving the efficiency of intelligent manufacturing. A significant scientific problem in CrowdIM is how to improve individual skills and crowd intelligence through cooperation, complementation, competition, and confrontation among HMT agents. The emergence mechanism of biological crowd intelligence provides an inspiration to address this challenge. This article explores the mapping mechanisms between natural crowd intelligence and CrowdIM, from the aspects, such as collective dynamics, self-adaptive mechanism, crowd intelligence optimization, graph structure mapping model, evolutionary game dynamics, multiagent learning, and so on. We further propose a general model of CrowdIM and expound it through a typical case study.

Recently,fake newshas been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about the dynamic evolution of crowd opinions, and usually neglect the issue of uneven arrival of data in the real world. To address these issues, in this article, we focus on a kind of approach for fake news detection, namelyprogressive detection, which can be achieved by thedynamic Probabilistic Graphical Model. Based on the observation on real-world datasets, we adaptively improve the Kalman Filter to theLabeled Variable Dimension Kalman Filter(LVDKF) that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly. It can take sequential data as input, distill the dynamic evolution knowledge regarding a post, and utilize crowd wisdom from users’ responses to achieve progressive detection. Then we derive the formulas using the Forward, Backward, and EM Algorithm, and we design a dynamic detection algorithm using Bayes’ theorem. Finally, we design experimental scenarios simulating progressive detection and evaluate LVDKF on two public datasets. It outperforms the baseline methods in these experimental scenarios, which indicates that it is adequate for progressive detection.

The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Despite that various methods have integrated external features to enhance the effects, extant works fail to address complex feature-sequence couplings in the following aspects: weaken the inter-dependencies when processing heterogeneous data and ignore the cumulative and evolving situation of coupling relationships. To address these issues, we propose DeepExpress—a deep-learning-based express delivery sequence prediction model, which extends the classic seq2seq framework to learn feature-sequence couplings. DeepExpress leverages an express delivery seq2seq learning, a carefully designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively handle heterogeneity issues and capture feature-sequence couplings for accurate prediction. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.

Many realistic mobile social networks can be characterized by evolving bipartite graphs, in which dynamically added elements are divided into two entities and connected by links between these two entities, such as users and items in recommendation networks, authors and scientific topics in scholarly networks, male and female in dating social networks, etc. However, given the fact that connections between two entities are often weighted, how to mathematically model such weighted evolving bipartite relationships, along with quantitative characterizations, remains unexplored. Motivated by this, we develop a novel evolving bipartite model (EBM), which, based on empirically validated power-law distribution on multiple realistic mobile social networks, discloses that the distribution of total weights of incoming and outgoing edges in networks is determined by the weighting scale and bounded by certain ceilings and floors. Based on these theoretical results, for evolving bipartite networks whose degree follows power-law distribution, their overall weights of vertices can be predicted by EBM. To illustrate, in recommendation networks, the evaluation of items, i.e., total rating scores, can be estimated through the given bounds; in scholarly networks, the total numbers of publications under specific topics can be anticipated within a certain range; in dating social networks, the favorability of male/female can be roughly measured. Finally, we perform extensive experiments on 10 realistic datasets and a synthetic network with varying weights, i.e., rating scales, to further evaluate the performance of EBM, and experimental results demonstrate that given weighting scales, both the upper bound and the lower bound of total weights of vertices in mobile social networks can be properly predicted by the EBM.

App survival prediction is a significant task in mobile service development. It differs from existing prediction tasks in two aspects. First, rather than the traditional survival prediction in bioinformatics where all the patients’ survival probabilities decay in a similar way, apps’ survival pattern varies from each other. Second, affected by multiple factors, an app's popularity is time-varying and sequence-dependent, which makes existing short-term prediction methods not applicable due to error accumulation. These characteristics bring great difficulties in app survival prediction. In this paper, we propose AppLife, a framework that fuses multi-source influence factors and utilizes Multi-Task Learning (MTL) to combine the state information of mobile app for survival prediction. First, we analyze how the app survival is affected by multi-source factors, including download history, ratings, and reviews. Second, to overcome error accumulation in long-term prediction, we propose a novel MTL based approach. The approach estimates whether an app is surviving at each time interval during the life cycle of apps and leverages relatedness among tasks to improve the prediction performance. Last, we collect a large-scale dataset with more than 35,000 apps, based on which we evaluate our proposed framework and results show that it outperforms the seven state-of-the-art methods.

App popularity prediction is a significant task in mobile service development, which predicts an app's future popularity based on its current behaviors. It provides benefits from app development to targeted investment. Popularity is affected by two factors, i.e., internal ones like reviews and external ones like interaction among apps. However, most related studies only explore internal factors but neglect external ones. In fact, external factor plays an important role in popularity prediction modelling since it is the promoting and/or inhibiting influence resulted by app interaction. The app interaction has two major characteristics, i.e., interactivity and dynamicity, which brings challenges to app popularity prediction due to two reasons: 1) interactivity—it is hard to evaluate the existence and influence intensity of interactions; 2) dynamicity—the nature of interaction influence, e.g., promoting or inhibiting, and its intensity on popularity change with time. In this paper, we propose DeePOP, a popularity prediction model that innovatively leverages time-varying hierarchical interactions. First, we propose Hierarchical Interaction Graph, which is first studied in this work, to organically characterize the relationship and influence among apps. Second, DeePOP integrates internal factors and time-varying hierarchical interactions as inputs to build the prediction model. It develops multi-level modules based on Recurrent Neural Network with attention mechanism and generates multi-step time series predictions by fusing the outputs of modules. Experiments on a real-world dataset show that DeePOP outperforms state-of-the-art methods in prediction accuracy, effectively reducing the Root Mean Square Error (RMSE) to 0.088.

Automated Guided Vehicles (AGVs) have been widely used for material handling in flexible shop floors. Each product requires various raw materials to complete the assembly in production process. AGVs are used to realize the automatic handling of raw materials in different locations. Efficient AGVs task allocation strategy can reduce transportation costs and improve distribution efficiency. However, the traditional centralized approaches make high demands on the control center’s computing power and real-time capability. In this paper, we present decentralized solutions to achieve flexible and self-organized AGVs task allocation. In particular, we propose two improved multi-agent reinforcement learning algorithms, MAD-DPG-IPF (Information Potential Field) and BiCNet-IPF, to realize the coordination among AGVs adapting to different scenarios. To address the reward-sparsity issue, we propose a reward shaping strategy based on information potential field, which provides stepwise rewards and implicitly guides the AGVs to different material targets. We conduct experiments under different settings (3 AGVs and 6 AGVs), and the experiment results indicate that, compared with baseline methods, our work obtains up to 47% task response improvement and 22% training iterations reduction.

With the rapid spread of mobile devices, many traditional online social network services, like academic applications, start to develop mobile apps and improve their service quality through mobile sensing. A rich friendship among users can provide support for such applications, e.g., help to delegate sensing tasks in mobile crowd sensing. Therefore, studying friend recommendation that offers users' suggestions on who to connect to, so as to enrich the user's friendship, is necessary to Mobile Social Networks (MSNs). Most existing strategies utilize user's relationship, or similarity, to make the recommendation, which overlooks the existence of multi-type connections among users, e.g., common paper based and common topic based connections among authors in academic networks. To overcome such limitation, we characterize each type of connections by a corresponding network layer and then propose a novel algorithm for joint recommendations in multilayer MSNs. Particularly, two types of results are presented in the paper. (i) Our proposed algorithm, named as Cross-layer 2-hop Path (C2P) algorithm, implements the joint recommendation by suggesting a user establish connections to his cross-layer two-hop neighbors, i.e., those who link to the user by two-hop paths with the two hops belonging to two different layers, respectively. In doing so, each produced recommendation item is a combination of user relationships in both two layers and therefore can better meet user demands. (ii) By analytical derivations, along with further empirical validation on real datasets, we give the performance evaluation on our proposed algorithm. First, we prove that the algorithm is efficiently implementable with a constant complexity of each recommendation in most cases. Then, we evaluate its recommendation performance by two metrics, i.e., accuracy and diversity, where the former metric measures recommendation accuracy and the latter one measures an algorithm's capability to provide diverse recommendation items. Our results show that C2P algorithm is optimal in terms of accuracy and for diversity, its performance is no less than the algorithm that is applied in single layers. And finally, the effectiveness of the proposed algorithm is validated on both synthetic and large-scale real datasets, where it outperforms the baseline algorithms with an up to 32 percent accuracy gain and obtains an approximately 0.5 ratio of the algorithm's diversity to the theoretical upperbound.

Many practical applications have observed knowledge evolution, i.e., continuous born of new knowledge, with its formation influenced by the structure of historical knowledge. This observation gives rise to evolving knowledge graphs whose structure temporally grows over time. However, both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored. To this end, we propose EvolveKG, a framework that reveals cross-time knowledge interaction with desirable performance of storage and computation. The novelty of EvolveKG lies in Derivative Graph - a static weighted snapshot of evolution at a certain time. Particularly, each weight quantifies knowledge effectiveness with a temporarily decaying function of consistency and attenuation, two proposed factors depicting whether or not the effectiveness of a fact fades away with time. Thanks to the cross-time interaction, EvolveKG allows future knowledge prediction by virtue of the influence from the historical ones. Empirically tested under two real datasets, the superiority of EvolveKG is confirmed via its prediction accuracy.

In many real-world networks, entities of different types usually form an evolving network with hybrid interactions. However, how to theoretically model such networks, along with quantitive characterizations, remains unexplored. Motivated by this, we develop a novel evolving model, which, as validated by our empirical results, can well capture some basic properties such as power-law degree distribution, densification, shrinking diameter, and community structure embodied in most real datasets. Particularly, two types of results are presented in this paper. First, our proposed model, namely, evolving K-Graph, consists of K-node sets representing K different types of entities. The hybrid interactions among entities, based on whether they belong to the same type, are classified into inter-type and intra-type ones that are, respectively, characterized by two joint graphs evolving over time. Following our newly proposed mechanism called interactive-evolution, potential connections can be established among nodes with common features and further form a positive feedback. The superiorities of our model are three folded: good capture of realistic networks, mathematical tractability and efficient implementation. Second, by analytical derivations, along with empirical validation on real datasets, we disclose two aspects of network properties: basic ones as power-law degree distribution, densification, shrinking diameter and community structure, as well as a distinctive one, that is, positive correlation observed in real networks, implying that a hub in one inter-type relationship network also has many neighbors in another one. An additional interesting finding is that through further comparison of models with or without interactive-evolution, the former one leads to an even earlier occurrence of network connectivity.

Social recommendation has been widely applied to offer users suggestions on who to connect to, where most existing strategies overlook the existence of multi-type connections among users. To overcome such limitation, we characterize each type of connections by a corresponding network layer and then propose a novel algorithm for joint recommendations in cross-layer social networks. Particularly, two types of results are presented in the paper. (i) Our proposed algorithm, named as Cross-layer 2-hop Path (C2P) algorithm, implements the joint recommendation by suggesting a user establish connections to his cross-layer two-hop neighbors, i.e., those who link to the user by two-hop paths with the two hops belonging to two different layers, respectively. In doing so, each produced recommendation item is a combination of user relationships in both two layers and thus can better meet user demands. (ii) By analytical derivations, along with further empirical validation on real datasets, we give the performance evaluation on our proposed algorithm. Firstly, we prove that the algorithm is efficiently implementable with a constant complexity in each recommendation. Then, we evaluate its recommendation performance by two metrics, i.e., acceptance and diversity, where the former metric measures recommendation accuracy and the latter one measures an algorithm's capability to provide diverse recommendation items. Our results show that C2P algorithm is optimal in terms of acceptance and for diversity, its performance is in the same order of the theoretical upperbound. And finally, the effectiveness of the proposed algorithm is validated by our simulations on three real datasets, where it outperforms baseline algorithms with an up to 38% acceptance gain and obtains an around 0.5 diversity ratio to the theoretical upperbound.

Many realistic wireless social networks are evolving over time. While network evolution has its important influence on network performances, it is nevertheless overlooked in most existing studies on information diffusion. Motivated by this, in this paper, we investigate the delivery accuracy of interest-aware information diffusion in evolving social networks. In doing so, we adopt a model, named affiliation networks, to characterize network evolution from three aspects, i.e., the arrival of new users, the generation of new interests, and the creation of new links between them. Based on that, we consider a publishing based information diffusion mechanism that widely exists in wireless networking services such as Facebook, Twitter, and Sina Weibo, where a user receives data items from his friends and then republishes the ones he is interested in to all his friends. Under the above network model, we study how the performance metric such as delivery accuracy is affected by the network evolution. The publishing based information diffusion mechanism is a blind targeting one that may suffer a low delivery accuracy. However, our analytical results demonstrate a contrary finding that the delivery accuracy is improved over time, and even more surprisingly, we disclose that with a sufficiently long evolving time, the delivery accuracy can achieve a perfect state where those who receive the data are exactly the ones that are interested in it. In addition, our theoretical findings are verified by experimental measurements through a social network dataset from Facebook.

In many realistic networks, entities of different types usually form an evolving network with hybrid interactions. However, how to mathematically model such networks remains unexplored. Motivated by this, we develop a novel evolving model, which, as validated by our empirical results, can well capture some basic features such as power-law distribution, densification and shrinking diameter. Particularly, in our proposed model, named Evolving K-Graph, the hybrid interactions among entities are classified into inter-type and intra-type connections that are respectively characterized by two joint graphs evolving over time. By empirical validation, we disclose two new network properties: a positive correlation of any two layers of the network, and an earlier occurrence of network connectivity resulted by our model.

Social characteristics have been observed to significantly affect the communications in wireless social networks, especially that within a group following the multicast manner. To model the multicast group in wireless social networks, we should incorporate two important social characteristics, i.e., social relationship and group size. In most existing works, the modeling of social relationship only considers geographic factor. However, such models fail to well characterize wireless social networks, since unlike that in traditional wireless networks, geographic distance is no longer the major factor that affects people's communications and some non-geographic factors, such as user popularity, become more and more important. Moreover, group size is always assumed to be known a priori in previous works, which cannot fully meet the realistic condition. Therefore, in this paper, we model the multicast group from a combination of geographic and non-geographic (GN) perspectives. Specifically, we propose the GN Model to characterize social relationship and the independently-selected model to characterize group size. In addition to the geographic distance considered in the modeling of social relationship, we also introduce user popularity which reflects the influence of each user on others. Then, we assume that the source transmits the data packet to all his friends following the multicast manner. Based on the proposed model, we calculate transmission distance and network traffic load, and then discuss how they are influenced by both geographic and GN factors. Moreover, our proposed models are verified through experimental measurements based on real datasets.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference generated by D2D communications is usually viewed as an obstacle to cellular communications. However, in this paper, we present a new perspective on the role of D2D interference by taking security issues into consideration. We consider a large-scale D2D-enabled cellular network with eavesdroppers overhearing cellular communications. Using stochastic geometry, we model such a network and analyze the signal-to-interference-plus-noise ratio (SINR) distributions, connection probabilities and secrecy probabilities of both the cellular and D2D links. We propose two criteria for guaranteeing performances of secure cellular communications, namely the strong and weak performance guarantee criteria. Based on the obtained analytical results of link characteristics, we design optimal D2D link scheduling schemes under these two criteria respectively. Both analytical and numerical results show that the interference from D2D communications can enhance physical layer security of cellular communications and at the same time create extra transmission opportunities for D2D users.