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ICAPS 2027: International Conference on Automated Planning and Scheduling

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投稿締切日:
2026-12-07 残り 43 日
通知日:
2027-02-26
開催日:
2027-06-27
開催地:
Columbia, South Carolina, USA
開催回数:
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CCF: B   ICORE: A*   QUALIS: A2   閲覧: 170822   フォロー: 101   参加: 13
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ICAPS
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会伴インデックス (CP-I)

89.7 / 100
全 5,759 件中 第 78 位 · 上位 2%

人工知能・機械学習 分野 751 件中 第 8 位

学術的評価 (35%)
100
投稿の選択性 (20%)
74
開催回数 (20%)
98
コミュニティの注目度 (10%)
75
公開情報の充実度 (15%)
85

使用した入力: 収録ランク：CCF B, QUALIS A2 · 採択率：33.3%（記録のある 5 回の平均） · 確認できる開催回数：37 · 会伴でフォローしている研究者：101 人 · 過去 24 か月にこのページを開いた研究者：56 人

公開情報で不足しているもの: 最優秀論文の記録 (+2.3)
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論文募集

ICAPS 2027 (International Conference on Automated Planning and Scheduling) is a CCF B / ICORE A* / QUALIS A2 conference held in Columbia, South Carolina, USA on 2027-06-27. The paper submission deadline is 2026-12-07. Acceptance notifications are sent on 2027-02-26.

The International Conference on Automated Planning and Scheduling (ICAPS) is the premier forum for research on the theory, algorithms, and applications of automated planning and scheduling technology. The 37th edition of the ICAPS conference series will be held in Columbia, SC, USA from June 27, 2027 to July 2, 2027. In addition to the main track, ICAPS-27 will feature a special track on “Planning Under a Different Name.” Papers submitted to either track will follow the submission and formatting requirements described below. Work that is naturally framed and evaluated within the existing planning and scheduling community, including planning methods that incorporate machine learning or foundation-model components, belongs in the main track. The special track is intended primarily for work rooted in neighboring AI communities that makes a substantive contribution to planning or deliberation but is framed using different terminology, literature, benchmarks, or methodological conventions. Additional scope and evaluation criteria are described on the special track page. Scope ICAPS-27 welcomes paper submissions on all aspects of automated planning and scheduling. Planning and scheduling are interpreted broadly to encompass a wide range of decision-making, optimization, and reasoning problems involving the selection, sequencing, coordination, and timing of actions. This includes domain modeling, plan and schedule synthesis, execution and monitoring, failure diagnosis, model repair, and associated learning, representation, and reasoning problems. ICAPS welcomes work on deterministic and stochastic sequential decision-making, under full or partial observability, and with factored or non-factored state representations. Topics in scope include (but are definitely not limited to): Planning and Scheduling Problems: Classical planning and theoretical foundations of planning Temporal planning, scheduling, and routing Planning under uncertainty, including MDPs, POMDPs, nondeterministic planning, and planning with sensing Planning with incomplete models, incomplete information, and belief states Hierarchical planning Real-time, online, and lifelong planning Multi-agent and distributed planning Motion/path planning, task and motion planning, and planning for hybrid systems Human-aware planning, scheduling, and execution Planning Across the Decision-Making Lifecycle: Knowledge engineering and representation for planning, including reasoning about actions, knowledge, and belief Activity and plan recognition Plan execution, monitoring, and diagnosis Replanning, plan repair, and model repair Planning under execution uncertainty Planning Methods and Computational Techniques: Search methods for planning and scheduling SAT, constraint programming, and model checking Mathematical programming and optimization Local search, evolutionary algorithms, and other heuristic optimization methods Learning-based planning, including planning methods that use or integrate foundation models (LLMs, VLMs, VLAs, etc.) Decomposition, abstraction, and other methods for scalable planning and scheduling Planning Applications and Emerging Directions: Description and modeling of novel application domains Engineering issues in using, deploying, and scaling planning and scheduling techniques User interface design, visualization, or human-system collaboration for a planning and scheduling application Evaluation, testing, and validation of planning and scheduling applications in societal or industrial environments Assessment of the impact of planning and scheduling systems on end users, customers, markets, or society at large Industry/application challenge problems in planning and scheduling (including benchmark instances) Contributions are welcome in each of the following categories: Theoretical papers, which broaden or improve the set of analytical tools used to study planning and scheduling problems and algorithms. Examples include complexity results, expressiveness, and new theoretical frameworks. Algorithmic papers, which describe novel perspectives and substantial (qualitative or quantitative) improvements for solving planning and scheduling problems. Examples include new optimizations or specializations of existing algorithms, new propagators, and new decomposition approaches. Modeling papers, which describe new representations of planning and scheduling problems and their solutions. Examples include new mathematical frameworks for existing problems, original descriptions of emerging problems, and refinements of existing frameworks for knowledge representation of actions, goals, states, or other rigorously defined concepts. Position papers, which contribute thoughtful critiques or bold new perspectives on the field. Such papers should articulate a clear thesis and support it through appropriate evidence, analysis, or synthesis of prior work. Examples include meta-analysis of research trends, descriptions of new challenge problems suitable for planning and scheduling, historical perspectives and analysis of the field, and technical discussions of various implementation techniques. Tool papers, which describe systems that are useful to and of interest to the planning and scheduling community, and which are built using novel algorithmic and engineering techniques. Examples include: integrated planning systems, model checkers and synthesis tools, libraries for constructing, managing, and transforming representations of planning and scheduling problems, and applications for visualizing, benchmarking, and comparing planners or other types of tools. Empirical and evaluation papers, which provide new scientific insights through careful experimental study of planning and scheduling methods, benchmarks, assumptions, or evaluation methodologies. Such papers should make a substantive contribution beyond applying established methods to additional problem instances. Application papers, which show how planning and scheduling methods can push the envelope for real-world problems. Papers that do not address problems related to automated planning or scheduling are out of scope and will be rejected without review. Work on reinforcement learning or other forms of sequential decision making is not in scope solely by virtue of addressing sequential decisions; it must make a substantive contribution to planning or scheduling. Where the relationship of the paper to planning and scheduling is not immediately obvious, authors should explain this relationship, and the relevance of the contribution to the ICAPS community, in the abstract and introduction of the paper.
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採択率

平均採択率: 33% 9 年間 (2002–2012).

年投稿数採択数採択率(%)
20121324534.1%
20111384734.1%
20101133732.7%
20091214133.9%
20071364331.6%
2006993333.3%
20041193731.1%
2003983030.6%
2002903235.6%

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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
CNeurocomputing6.5Elsevier0925-2312
CEngineering Applications of Artificial Intelligence9.0Elsevier0952-1976
BInformation Sciences6.0Elsevier0020-0255
BIEEE Transactions on Automation Science and Engineering6.4IEEE1545-5955
Applied Soft Computing7.8Elsevier1568-4946
AIEEE Transactions on Services Computing5.8IEEE1939-1374
CKnowledge-Based Systems7.2Elsevier0950-7051
BAutonomous Agents and Multi-Agent Systems2.6Springer1387-2532
BComputer Networks4.7Elsevier1389-1286
Journal of Scheduling1.8Springer1094-6136

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