Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing

Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely

Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts

Team Composition in PES2018 using Submodular Function Optimization

With the development of computer game technologies, gameplay becomes very realistic in many sports games, therefore providing appealing play experience to game players. To get the victory in a football pitch, the team composition is pretty important. There is little research on the automatic team composition in sports games particularly in a popular game of Pro Evolution Soccer (PES). In this paper, we consider the team composition as one team player recommendation problem since a team is composed of several players in a game. Subsequently, we aim to recommend a list of sufficiently good football players to game players. We convert the team player recommendation into one optimization problem and resort to the greedy algorithm-based solutions. We propose a coverage function that quantifies the degree of soccer skills to be covered by the selected players. In addition, we prove the submodularity of the coverage function and improve a greedy algorithm to solve the function optimization problem. We demonstrate the performance of our techniques in PES2018.</p

Optimization opportunities in human in the loop computational paradigm

An emerging trend is to leverage human capabilities in the computational loop at different capacities, ranging from tapping knowledge from a richly heterogeneous pool of knowledge resident in the general population to soliciting expert opinions. These practices are, in general, termed human-in-the-loop (HITL) computations. A HITL process requires holistic treatment and optimization from multiple standpoints considering all stakeholders: a. applications, b. platforms, c. humans. In application-centric optimization, the factors of interest usually are latency (how long it takes for a set of tasks to finish), cost (the monetary or computational expenses incurred in the process), and quality of the completed tasks. Platform-centric optimization studies throughput, or revenue maximization, while human-centric optimization deals with the characteristics of the human workers, referred to as human factors, such as their skill improvement and learning, to name a few. Finally, fairness and ethical consideration are also of utmost importance in these processes./p\u3e This dissertation aims to design solutions for each of the aforementioned stakeholders. The first contribution of this dissertation is the study of recommending deployment strategies for applications consistent with task requesters’ deployment parameters. From the worker’s standpoint, this dissertation focuses on investigating online group formation where members seek to increase their learning potential via collaboration. Finally, it studies how to consolidate preferences from different workers/applications in a fair manner, such that the final order is both consistent with individual preferences and complies with a group fairness criteria. The technical contributions of this dissertation are to rigorously study these problems from theoretical standpoints, present principled algorithms with theoretical guarantees, and conduct extensive experimental analysis using large-scale real-world datasets to demonstrate their effectiveness and scalability

Self-Organizing Teams in Online Work Settings

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates