Finding teams that balance expert load and task coverage
- URL: http://arxiv.org/abs/2011.04428v1
- Date: Tue, 3 Nov 2020 18:04:15 GMT
- Title: Finding teams that balance expert load and task coverage
- Authors: Sofia Maria Nikolakaki, Mingxiang Cai, Evimaria Terzi
- Abstract summary: In this paper we consider a problem where each task consists of required and optional skills.
We show that the BalancedTA problem (and its variant) is NP-hard and design efficients for solving it in practice.
- Score: 7.831410227443101
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rise of online labor markets (e.g., Freelancer, Guru and Upwork) has
ignited a lot of research on team formation, where experts acquiring different
skills form teams to complete tasks. The core idea in this line of work has
been the strict requirement that the team of experts assigned to complete a
given task should contain a superset of the skills required by the task.
However, in many applications the required skills are often a wishlist of the
entity that posts the task and not all of the skills are absolutely necessary.
Thus, in our setting we relax the complete coverage requirement and we allow
for tasks to be partially covered by the formed teams, assuming that the
quality of task completion is proportional to the fraction of covered skills
per task. At the same time, we assume that when multiple tasks need to be
performed, the less the load of an expert the better the performance. We
combine these two high-level objectives into one and define the BalancedTA
problem. We also consider a generalization of this problem where each task
consists of required and optional skills. In this setting, our objective is the
same under the constraint that all required skills should be covered. From the
technical point of view, we show that the BalancedTA problem (and its variant)
is NP-hard and design efficient heuristics for solving it in practice. Using
real datasets from three online market places, Freelancer, Guru and Upwork we
demonstrate the efficiency of our methods and the practical utility of our
framework.
Related papers
- Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework [0.0]
Home repair and installation services require technicians to visit customers and resolve tasks of different complexity.
geographical spread of customers makes achieving perfect matches between technician skills and task requirements impractical.
We propose a state-dependent balance of these factors via reinforcement learning.
arXiv Detail & Related papers (2024-09-03T11:56:58Z) - Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives [54.14429346914995]
Chain-of-Thought (CoT) has become a pivotal method for solving complex problems.
Large language models (LLMs) often struggle to accurately decompose domain-specific tasks.
This paper introduces the Re-TASK framework, a novel theoretical model that revisits LLM tasks from the perspectives of capability, skill, and knowledge.
arXiv Detail & Related papers (2024-08-13T13:58:23Z) - Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed
Bandits [6.802315212233411]
This paper explores mobile crowdsensing, which leverages mobile devices and their users for collective sensing tasks under the coordination of a central requester.
The primary challenge here is the variability in the sensing capabilities of individual workers, which are initially unknown and must be progressively learned.
We propose a novel model that enhances task diversity over the rounds by dynamically adjusting the weight of tasks in each round based on their frequency of assignment.
arXiv Detail & Related papers (2023-12-25T13:54:58Z) - Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners [74.92558307689265]
We propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad')
We optimize this matching process during the training of a single model.
Experiments on the Taskonomy dataset with 13 vision tasks and the PASCAL-Context dataset with 5 vision tasks show the superiority of our approach.
arXiv Detail & Related papers (2022-12-15T18:59:52Z) - Learning Options via Compression [62.55893046218824]
We propose a new objective that combines the maximum likelihood objective with a penalty on the description length of the skills.
Our objective learns skills that solve downstream tasks in fewer samples compared to skills learned from only maximizing likelihood.
arXiv Detail & Related papers (2022-12-08T22:34:59Z) - Design of Negative Sampling Strategies for Distantly Supervised Skill
Extraction [19.43668931500507]
We propose an end-to-end system for skill extraction, based on distant supervision through literal matching.
We observe that using the ESCO taxonomy to select negative examples from related skills yields the biggest improvements.
We release the benchmark dataset for research purposes to stimulate further research on the task.
arXiv Detail & Related papers (2022-09-13T13:37:06Z) - Combining Modular Skills in Multitask Learning [149.8001096811708]
A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks.
In this work, we assume each task is associated with a subset of latent discrete skills from a (potentially small) inventory.
We find that the modular design of a network significantly increases sample efficiency in reinforcement learning and few-shot generalisation in supervised learning.
arXiv Detail & Related papers (2022-02-28T16:07:19Z) - Towards Collaborative Question Answering: A Preliminary Study [63.91687114660126]
We propose CollabQA, a novel QA task in which several expert agents coordinated by a moderator work together to answer questions that cannot be answered with any single agent alone.
We make a synthetic dataset of a large knowledge graph that can be distributed to experts.
We show that the problem can be challenging without introducing prior to the collaboration structure, unless experts are perfect and uniform.
arXiv Detail & Related papers (2022-01-24T14:27:00Z) - Discovering Generalizable Skills via Automated Generation of Diverse
Tasks [82.16392072211337]
We propose a method to discover generalizable skills via automated generation of a diverse set of tasks.
As opposed to prior work on unsupervised discovery of skills, our method pairs each skill with a unique task produced by a trainable task generator.
A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective.
The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.
arXiv Detail & Related papers (2021-06-26T03:41:51Z)
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.