Optimal Team Recruitment Strategies for Collaborative Mobile Crowdsourcing Systems

• URL: http://arxiv.org/abs/2004.11512v2
• Date: Tue, 28 Apr 2020 02:51:36 GMT
• Title: Optimal Team Recruitment Strategies for Collaborative Mobile Crowdsourcing Systems
• Authors: Aymen Hamrouni, Hakim Ghazzai, Turki Alelyani, and Yehia Massoud
• Abstract summary: We develop two recruitment strategies for collaborative Mobile Crowdsourcing frameworks. The first proposed strategy is a platform-based approach which exploits the platform knowledge to form the team. The second one is a leader-based approach that uses team members' knowledge about their social network (SN) neighbors to designate a group leader that recruits its suitable team.
• Score: 1.4209473797379666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The wide spread of mobile devices has enabled a new paradigm of innovation called Mobile Crowdsourcing (MCS) where the concept is to allow entities, e.g., individuals or local authorities, to hire workers to help from the crowd of connected people, to execute a task or service. Some complex tasks require the collaboration of multiple workers to ensure its successful completion. In this context, the task requester needs to hire a group of socially connected and collaborative workers that, at the same time, have sufficient skills to accomplish the task. In this paper, we develop two recruitment strategies for collaborative MCS frameworks in which, virtual teams are formed according to four different criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. The first proposed strategy is a platform-based approach which exploits the platform knowledge to form the team. The second one is a leader-based approach that uses team members' knowledge about their social network (SN) neighbors to designate a group leader that recruits its suitable team. Both approaches are modeled as integer linear programs resulting in optimal team formation. Experimental results show a performance trade-off between the two virtual team grouping strategies when varying the members SN edge degree. Compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

Related papers

• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching [70.28786574064694]
  A reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams.
  arXiv  Detail & Related papers  (2023-04-08T14:32:12Z)
• Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition [3.828689444527739]
  We evaluate cooperative value-based methods in a mixed cooperative-competitive environment. We selected three training methods based on the centralised training and decentralised execution paradigm. For our experiments, we modified the StarCraft Multi-Agent Challenge environment to create competitive environments where both teams could learn and compete simultaneously.
  arXiv  Detail & Related papers  (2022-11-21T22:25:55Z)
• Conditional Imitation Learning for Multi-Agent Games [89.897635970366]
  We study the problem of conditional multi-agent imitation learning, where we have access to joint trajectory demonstrations at training time. We propose a novel approach to address the difficulties of scalability and data scarcity. Our model learns a low-rank subspace over ego and partner agent strategies, then infers and adapts to a new partner strategy by interpolating in the subspace.
  arXiv  Detail & Related papers  (2022-01-05T04:40:13Z)
• Learning Connectivity-Maximizing Network Configurations [123.01665966032014]
  We propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. After training, our system produces connected configurations 2 orders of magnitude faster than the optimization-based scheme for teams of 10-20 agents.
  arXiv  Detail & Related papers  (2021-12-14T18:59:01Z)
• Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks [2.798697306330988]
  Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people. We investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers.
  arXiv  Detail & Related papers  (2021-06-01T18:24:02Z)
• Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition [88.26752130107259]
  In real-world multiagent systems, agents with different capabilities may join or leave without altering the team's overarching goals. We propose COPA, a coach-player framework to tackle this problem. We 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.
  arXiv  Detail & Related papers  (2021-05-18T17:27:37Z)
• Multi-Agent Coordination in Adversarial Environments through Signal Mediated Strategies [37.00818384785628]
  Team members can coordinate their strategies before the beginning of the game, but are unable to communicate during the playing phase of the game. In this setting, model-free RL methods are oftentimes unable to capture coordination because agents' policies are executed in a decentralized fashion. We show convergence to coordinated equilibria in cases where previous state-of-the-art multi-agent RL algorithms did not.
  arXiv  Detail & Related papers  (2021-02-09T18:44:16Z)
• Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams [0.0]
  We develop a competitive multi agent environment called FortAttack in which two teams compete against each other. We observe a natural emergence of heterogeneous behavior amongst homogeneous agents when such behavior can lead to the team's success. We propose ensemble training, in which we utilize the evolved opponent strategies to train a single policy for friendly agents.
  arXiv  Detail & Related papers  (2020-07-06T22:35:56Z)
• A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing [1.4209473797379666]
  We develop an algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces time and produces reasonable performance results.
  arXiv  Detail & Related papers  (2020-04-28T22:44:37Z)
• On Emergent Communication in Competitive Multi-Agent Teams [116.95067289206919]
  We investigate whether competition for performance from an external, similar agent team could act as a social influence. Our results show that an external competitive influence leads to improved accuracy and generalization, as well as faster emergence of communicative languages.
  arXiv  Detail & Related papers  (2020-03-04T01:14:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.