会议信息

SoCS 2026: International Symposium on Combinatorial Search

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截稿日期:
2026-03-16
通知日期:
2026-05-16
会议日期:
2026-08-14
会议地点:
Bremerhaven, Germany
届数:
19
ICORE: B   浏览: 35   关注: 0   参加: 0

征稿

SoCS 2026 (International Symposium on Combinatorial Search) is a ICORE B conference held in Bremerhaven, Germany on 2026-08-14. The paper submission deadline is 2026-03-16. Acceptance notifications are sent on 2026-05-16.

Heuristic search and other forms of combinatorial search and optimization are highly active areas of research across artificial intelligence, robotics, planning, discrete optimization, and related fields in computer science and operations research. The International Symposium on Combinatorial Search (SoCS) aims to bring together researchers working in these areas to exchange ideas and foster cross-fertilization across disciplines. SoCS welcomes submissions from all research communities that employ combinatorial search techniques, including artificial intelligence, planning, robotics, constraint programming, meta-reasoning, operations research, navigation, and bioinformatics. We particularly encourage submissions that present real-world applications of heuristic search. Given its co-location with IJCAI 2026, SoCS especially welcomes papers that explore the interface between combinatorial search and other areas of AI research. Topics of interest: Topics of interest include but are not limited to the following: Adversarial search Analysis of search algorithms Automated synthesis of lower bounds Bidirectional search Bounding and pruning techniques Combinatorial optimization Combinatorial puzzles Continuous problem solving Constraint search Deep learning in heuristics and search External-memory and parallel search Heuristics Incremental and active learning in search Machine learning for or in search algorithms Metareasoning and search Methodology and critiques of current practice Model-based search Multi-agent pathfinding Pathfinding Portfolios of search algorithms Problem compilation Problem-solving using search Random vs. systematic search strategy selection Real-life applications Real-time search Reinforcement learning and search Search in goal-directed problem solving Search space discretization for continuous state-space problems Search-based diagnosis Search in Boolean satisfiability Search in machine learning and big data analytics Search methods in robotics Search for Large Language models (LLMs) Self-configuring and self-tuning algorithms Symmetry handling Time, memory, and solution quality trade-offs Tools, Software frameworks, and benchmarks
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