Team Formation amidst Conflicts

• URL: http://arxiv.org/abs/2403.00859v1
• Date: Thu, 29 Feb 2024 20:15:13 GMT
• Title: Team Formation amidst Conflicts
• Authors: Iasonas Nikolaou, Evimaria Terzi
• Abstract summary: In this work, we formulate the problem of team formation amidst conflicts. The goal is to assign individuals to tasks, with given capacities, taking into account individuals' task preferences and the conflicts between them. Using dependent rounding schemes as our main toolbox, we provide efficient approximation algorithms.
• Score: 4.197110761923661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we formulate the problem of team formation amidst conflicts. The goal is to assign individuals to tasks, with given capacities, taking into account individuals' task preferences and the conflicts between them. Using dependent rounding schemes as our main toolbox, we provide efficient approximation algorithms. Our framework is extremely versatile and can model many different real-world scenarios as they arise in educational settings and human-resource management. We test and deploy our algorithms on real-world datasets and we show that our algorithms find assignments that are better than those found by natural baselines. In the educational setting we also show how our assignments are far better than those done manually by human experts. In the human resource management application we show how our assignments increase the diversity of teams. Finally, using a synthetic dataset we demonstrate that our algorithms scale very well in practice.

Related papers

• A Human-Centered Approach for Improving Supervised Learning [0.44378250612683995]
  This paper shows how we can strike a balance between performance, time, and resource constraints. Another goal of this research is to make Ensembles more explainable and intelligible using the Human-Centered approach.
  arXiv  Detail & Related papers  (2024-10-14T10:27:14Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake? [62.59699229202307]
  Despite advances in AI, it remains a significant challenge to develop interactive task guidance systems. We created a new multimodal benchmark dataset, Watch, Talk and Guide (WTaG) based on natural interaction between a human user and a human instructor. We leveraged several foundation models to study to what extent these models can be quickly adapted to perceptually enabled task guidance.
  arXiv  Detail & Related papers  (2023-11-01T15:13:49Z)
• Improving Generalization of Alignment with Human Preferences through Group Invariant Learning [56.19242260613749]
  Reinforcement Learning from Human Feedback (RLHF) enables the generation of responses more aligned with human preferences. Previous work shows that Reinforcement Learning (RL) often exploits shortcuts to attain high rewards and overlooks challenging samples. We propose a novel approach that can learn a consistent policy via RL across various data groups or domains.
  arXiv  Detail & Related papers  (2023-10-18T13:54:15Z)
• Designing Closed-Loop Models for Task Allocation [36.04165658325371]
  We exploit weak prior information on human-task similarity to bootstrap model training. We show that the use of such a weak prior can improve task allocation accuracy, even when human decision-makers are fallible and biased.
  arXiv  Detail & Related papers  (2023-05-31T13:57:56Z)
• 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)
• Improving Learning-to-Defer Algorithms Through Fine-Tuning [1.066048003460524]
  We work to improve learning-to-defer algorithms when paired with specific individuals. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance. We suggest future work that uses robust semi-supervised to improve learning.
  arXiv  Detail & Related papers  (2021-12-18T19:57:16Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning [78.13740204156858]
  We show that we can reuse prior data to extend new skills simply through dynamic programming. We demonstrate the effectiveness of our approach by chaining together several behaviors seen in prior datasets for solving a new task. We train our policies in an end-to-end fashion, mapping high-dimensional image observations to low-level robot control commands.
  arXiv  Detail & Related papers  (2020-10-27T17:57:29Z)
• Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves [0.0]
  We introduce a novel resource coordination that enables robots to explore the relative strengths and learning abilities of their human teammates. We generate and evaluate a robust schedule while discovering the latest individual worker proficiency. Results indicate that scheduling strategies favoring exploration tend to be beneficial for human-robot collaboration.
  arXiv  Detail & Related papers  (2020-07-03T19:44:22Z)
• Forming Diverse Teams from Sequentially Arriving People [9.247294820004146]
  Collaborative work often benefits from having teams or organizations with heterogeneous members. We present a method to form such diverse teams from people arriving sequentially over time. We show that, in practice, the algorithm leads to large gains in team diversity.
  arXiv  Detail & Related papers  (2020-02-25T07:00:07Z)

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.