TAIP: an anytime algorithm for allocating student teams to internship programs

• URL: http://arxiv.org/abs/2005.09331v1
• Date: Tue, 19 May 2020 09:50:38 GMT
• Title: TAIP: an anytime algorithm for allocating student teams to internship programs
• Authors: Athina Georgara, Carles Sierra, Juan A. Rodr\'iguez-Aguilar
• Abstract summary: We focus on the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally. We propose TAIP, a algorithm that generates an initial team allocation which later on attempts to improve in an iterative process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic algorithm that generates an initial team allocation which later on attempts to improve in an iterative process. Moreover, we conduct a systematic evaluation to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.

Related papers

• A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation [61.08720171136229]
  Coalition structure generation is a fundamental computational problem in multiagent systems. We develop SALDAE, a multiagent path finding algorithm for CSG that operates on a graph of coalition structures.
  arXiv  Detail & Related papers  (2025-02-14T15:21:27Z)
• Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems [5.896440476510869]
  Assignment problems are a classic optimization problem in which a group of agents is assigned to a group of tasks. In many modern-day applications such as satellite, power grids, and mobile robot scheduling, assignment problems unfold over time. We apply multi-agent reinforcement learning to this problem, learning the value of assignments by bootstrapping from a known RL-time greedy solver. We demonstrate that our algorithm is theoretically justified and avoids pitfalls experienced by other algorithms in this setting.
  arXiv  Detail & Related papers  (2024-12-20T05:10:34Z)
• 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)
• Reinforcement Learning with Success Induced Task Prioritization [68.8204255655161]
  We introduce Success Induced Task Prioritization (SITP), a framework for automatic curriculum learning. The algorithm selects the order of tasks that provide the fastest learning for agents. We demonstrate that SITP matches or surpasses the results of other curriculum design methods.
  arXiv  Detail & Related papers  (2022-12-30T12:32:43Z)
• Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members [13.643650155415484]
  We show how cooperative game-theoretic solutions can be distilled into a learned model by training neural networks. Our approach creates models that can generalize to games far from the training distribution. An important application of our framework is Explainable AI.
  arXiv  Detail & Related papers  (2022-08-18T12:33:09Z)
• JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding [74.12405417718054]
  This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM) Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
  arXiv  Detail & Related papers  (2022-06-13T17:03:52Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games [123.76716667704625]
  We focus on the problem of finding an optimal strategy for a team of two players that faces an opponent in an imperfect-information zero-sum extensive-form game. In that setting, it is known that the best the team can do is sample a profile of potentially randomized strategies (one per player) from a joint (a.k.a. correlated) probability distribution at the beginning of the game. We provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile.
  arXiv  Detail & Related papers  (2020-09-21T17:51:57Z)
• 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)
• 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.