The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems

• URL: http://arxiv.org/abs/2201.10453v1
• Date: Tue, 25 Jan 2022 16:55:33 GMT
• Title: The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
• Authors: Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Yingqian Zhang, Tom Catshoek, Dani\"el Vos, Sicco Verwer, Fynn Schmitt-Ulms, Andr\'e Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel L\'opez-Ib\'a\~nez, Ekhine Irurozki
• Abstract summary: This paper reports on the first international competition on AI for the traveling salesman problem (TTSP) at the 2021 International Conference on Artificial Intelligence (IJCAI-21) The competition asked the participants to develop algorithms to solve a time-dependent orienteering problem with weights and time windows (TD-OPSWTW) The winning methods described in this work have advanced the state-of-the-art in AI for routing problems using AI.
• Score: 10.388013100067266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical combinatorial optimization problems, with many variants inspired by real-world applications. This first competition asked the participants to develop algorithms to solve a time-dependent orienteering problem with stochastic weights and time windows (TD-OPSWTW). It focused on two types of learning approaches: surrogate-based optimization and deep reinforcement learning. In this paper, we describe the problem, the setup of the competition, the winning methods, and give an overview of the results. The winning methods described in this work have advanced the state-of-the-art in using AI for stochastic routing problems. Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers. The simulator of the problem has been made open-source and can be used by other researchers as a benchmark for new AI methods.

Related papers

• OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI [73.75520820608232]
  We introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. Our evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration.
  arXiv  Detail & Related papers  (2024-06-18T16:20:53Z)
• Learning to Make Adherence-Aware Advice [8.419688203654948]
  This paper presents a sequential decision-making model that takes into account the human's adherence level. We provide learning algorithms that learn the optimal advice policy and make advice only at critical time stamps.
  arXiv  Detail & Related papers  (2023-10-01T23:15:55Z)
• Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO [50.58083807719749]
  We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems. We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.
  arXiv  Detail & Related papers  (2023-08-30T07:16:11Z)
• Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows [5.4807970361321585]
  We propose a novel machine learning pipeline that incorporates an optimization layer. We apply this pipeline to a dynamic vehicle routing problem with waves, which was recently promoted in the EURO Meets NeurIPS Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem.
  arXiv  Detail & Related papers  (2023-04-03T08:23:09Z)
• A Novel Approach for Auto-Formulation of Optimization Problems [66.94228200699997]
  In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers. In this paper, we present the solution of our team. Our proposed methods have achieved the F1-score of 0.931 in subtask 1 and the accuracy of 0.867 in subtask 2, which won the fourth and third places respectively in this competition.
  arXiv  Detail & Related papers  (2023-02-09T13:57:06Z)
• NeurIPS 2022 Competition: Driving SMARTS [60.948652154552136]
  Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts. The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods.
  arXiv  Detail & Related papers  (2022-11-14T17:10:53Z)
• Solving the vehicle routing problem with deep reinforcement learning [0.0]
  This paper focuses on the application of RL for the Vehicle Routing Problem (VRP), a famous problem that belongs to the class of NP-Hard problems. In a second phase, the neural architecture behind the Actor and Critic has been established, choosing to adopt a neural architecture based on the Convolutional neural networks. Experiments performed on a wide range of instances show that the algorithm has good generalization capabilities and can reach good solutions in a short time.
  arXiv  Detail & Related papers  (2022-07-30T12:34:26Z)
• Retrospective on the 2021 BASALT Competition on Learning from Human Feedback [92.37243979045817]
  The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
  arXiv  Detail & Related papers  (2022-04-14T17:24:54Z)
• The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights [59.93939636422896]
  The ML4CO aims at improving state-of-the-art optimization solvers by replacing key components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate routing configuration.
  arXiv  Detail & Related papers  (2022-03-04T17:06:00Z)
• A Game AI Competition to foster Collaborative AI research and development [5.682875185620577]
  We present the Geometry Friends Game AI Competition. The concept of the game is simple, though its solving has proven to be difficult. We discuss the competition and the challenges it brings, and present an overview of the current solutions.
  arXiv  Detail & Related papers  (2020-10-17T23:03:06Z)

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.