International Conference on Automated Planning and Scheduling最新文献_第7页

Timed Partial Order Inference Algorithm 定时偏序推理算法

International Conference on Automated Planning and Scheduling Pub Date : 2023-02-05 DOI: 10.48550/arXiv.2302.02501

Kandai Watanabe, Bardh Hoxha, D. Prokhorov, Georgios Fainekos, Morteza Lahijanian, Sriram Sankaranarayana, Tomoya Yamaguchi

{"title":"Timed Partial Order Inference Algorithm","authors":"Kandai Watanabe, Bardh Hoxha, D. Prokhorov, Georgios Fainekos, Morteza Lahijanian, Sriram Sankaranarayana, Tomoya Yamaguchi","doi":"10.48550/arXiv.2302.02501","DOIUrl":"https://doi.org/10.48550/arXiv.2302.02501","url":null,"abstract":"In this work, we propose the model of timed partial orders (TPOs) for specifying workflow schedules, especially for modeling manufacturing processes. TPOs integrate partial orders over events in a workflow, specifying ``happens-before'' relations, with timing constraints specified using guards and resets on clocks -- an idea borrowed from timed-automata specifications. TPOs naturally allow us to capture event ordering, along with a restricted but useful class of timing relationships. Next, we consider the problem of mining TPO schedules from workflow logs, which include events along with their time stamps. We demonstrate a relationship between formulating TPOs and the graph-coloring problem, and present an algorithm for learning TPOs with correctness guarantees.\u0000We demonstrate our approach on synthetic datasets, including two datasets inspired by real-life applications of aircraft turnaround and gameplay videos of the Overcooked computer game. Our TPO mining algorithm can infer TPOs involving hundreds of events from thousands of data-points within a few seconds. We show that the resulting TPOs provide useful insights into the dependencies and timing constraints for workflows.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123050282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Task Phasing: Automated Curriculum Learning from Demonstrations 任务分阶段:从演示中自动学习课程

International Conference on Automated Planning and Scheduling Pub Date : 2022-10-20 DOI: 10.48550/arXiv.2210.10999

Vaibhav Bajaj, Guni Sharon, P. Stone

{"title":"Task Phasing: Automated Curriculum Learning from Demonstrations","authors":"Vaibhav Bajaj, Guni Sharon, P. Stone","doi":"10.48550/arXiv.2210.10999","DOIUrl":"https://doi.org/10.48550/arXiv.2210.10999","url":null,"abstract":"Applying reinforcement learning (RL) to sparse reward domains is notoriously challenging due to insufficient guiding signals. \u0000Common RL techniques for addressing such domains include (1) learning from demonstrations and (2) curriculum learning. While these two approaches have been studied in detail, they have rarely been considered together. This paper aims to do so by introducing a principled task-phasing approach that uses demonstrations to automatically generate a curriculum sequence. Using inverse RL from (suboptimal) demonstrations we define a simple initial task. Our task phasing approach then provides a framework to gradually increase the complexity of the task all the way to the target task, while retuning the RL agent in each phasing iteration. Two approaches for phasing are considered: (1) gradually increasing the proportion of time steps an RL agent is in control, and (2) phasing out a guiding informative reward function. We present conditions that guarantee the convergence of these approaches to an optimal policy. Experimental results on 3 sparse reward domains demonstrate that our task-phasing approaches outperform state-of-the-art approaches with respect to asymptotic performance.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116379624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Small Solution Hypothesis for MAPF on Strongly Connected Directed Graphs Is True 强连通有向图上MAPF的小解假设成立

International Conference on Automated Planning and Scheduling Pub Date : 2022-10-10 DOI: 10.1609/icaps.v33i1.27208

B. Nebel

引用次数: 2

Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach 动态控制器综合的探索策略:一种强化学习方法

International Conference on Automated Planning and Scheduling Pub Date : 2022-10-07 DOI: 10.1609/icaps.v33i1.27238

Tom'as Delgado, Marco S'anchez Sorondo, V. Braberman, Sebastián Uchitel

{"title":"Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach","authors":"Tom'as Delgado, Marco S'anchez Sorondo, V. Braberman, Sebastián Uchitel","doi":"10.1609/icaps.v33i1.27238","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27238","url":null,"abstract":"Controller synthesis is in essence a case of model-based planning for non-deterministic environments in which plans (actually “strategies”) are meant to preserve system goals indefinitely. In the case of supervisory control environments are specified as the parallel composition of state machines and valid strategies are required to be “non-blocking” (i.e., always enabling the environment to reach certain marked states) in addition to safe (i.e., keep the system within a safe zone). Recently, On-the-fly Directed Controller Synthesis techniques were proposed to avoid the exploration of the entire -and exponentially large- environment space, at the cost of non-maximal permissiveness, to either find a strategy or conclude that there is none. The incremental exploration of the plant is currently guided by a domain-independent human-designed heuristic.\u0000In this work, we propose a new method for obtaining heuristics based on Reinforcement Learning (RL). The synthesis algorithm is thus framed as an RL task with an unbounded action space and a modified version of DQN is used. With a simple and general set of features that abstracts both states and actions, we show that it is possible to learn heuristics on small versions of a problem that generalize to the larger instances, effectively doing zero-shot policy transfer. Our agents learn from scratch in a highly partially observable RL task and outperform the existing heuristic overall, in instances unseen during training.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133064124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uniform Machine Scheduling with Predictions 统一机器调度与预测

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19827

Tianming Zhao, Wei Li, Albert Y. Zomaya

引用次数: 1

Solving Job-Shop Scheduling Problems with QUBO-Based Specialized Hardware 用基于qubo的专用硬件解决作业车间调度问题

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19826

Jiachen Zhang, Giovanni Lo Bianco, J. Christopher Beck

引用次数: 1

New Refinement Strategies for Cartesian Abstractions 笛卡尔抽象的新细化策略

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19819

David Speck, Jendrik Seipp

引用次数: 0

Optimal Mixed Strategies for Cost-Adversarial Planning Games 成本对抗计划博弈的最优混合策略

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19797

Rostislav Horcík, Á. Torralba, Pavel Rytír, L. Chrpa, S. Edelkamp

引用次数: 0

Crossword Puzzle Resolution via Monte Carlo Tree Search 纵横字谜解决方案通过蒙特卡洛树搜索

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19783

Lihan Chen, Jingping Liu, Sihang Jiang, Chao Wang, Jiaqing Liang, Yanghua Xiao, Shenmin Zhang, Rui Song

引用次数: 3

Planning Satellite Swarm Measurements for Earth Science Models: Comparing Constraint Processing and MILP Methods 规划地球科学模型的卫星群测量:比较约束处理和MILP方法

International Conference on Automated Planning and Scheduling Pub Date : 2022-06-13 DOI: 10.1609/icaps.v32i1.19833

R. Levinson, Samantha Niemoeller, S. Nag, V. Ravindra

{"title":"Planning Satellite Swarm Measurements for Earth Science Models: Comparing Constraint Processing and MILP Methods","authors":"R. Levinson, Samantha Niemoeller, S. Nag, V. Ravindra","doi":"10.1609/icaps.v32i1.19833","DOIUrl":"https://doi.org/10.1609/icaps.v32i1.19833","url":null,"abstract":"We compare two planner solutions for a challenging Earth science application to plan coordinated measurements (observations) for a constellation of satellites. This problem is combinatorially explosive, involving many degrees of freedom for planner choices. Each satellite carries two different sensors and is maneuverable to 61 pointing angle options. The sensors collect data to update the predictions made by a high-fidelity global soil moisture prediction model. Soil moisture is an important geophysical variable whose knowledge is used in applications such as crop health monitoring and predictions of floods, droughts, and fires.\u0000 The global soil-moisture model produces soil-moisture predictions with associated prediction errors over the globe represented by a grid of 1.67 million Ground Positions (GPs). The prediction error varies over space and time and can change drastically with events like rain/fire. The planner's goal is to select measurements which reduce prediction errors to improve future predictions. This is done by targeting high-quality observations at locations of high prediction-error. Observations can be made in multiple ways, such as by using one or more instruments or different pointing angles; the planner seeks to select the way with the least measurement-error (higher observation quality).\u0000 In this paper we compare two planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. DCP uses domain heuristics to find solutions within a reasonable time for our application but cannot be proven optimal, while the MILP produces provably optimal solutions. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124545218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1