Proceedings of the International Symposium on Combinatorial Search
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Proceedings of the International Symposium on Combinatorial Search

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Explore the latest research and advancements in combinatorial search algorithms and techniques. Discover cutting-edge solutions from the International Symposium proceedings.

Proceedings of the International Symposium on Combinatorial Search Cover

Articles in this Journal

Position Paper: On the Impact of Direction-Selection in BAE*

BAE*, and the independently developed DIBBS, are state-of-the-art bidirectional heuristic search algorithms that exploit heuristic consistency to efficiently prove solution optimality. Historically, BAE* has been studied with various direction-select...

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Extracting Problem Structure with LLMs for Optimized SAT Local Search

Encoding combinatorial problems in terms of propositional satisfiability (SAT) enables utilization of highly efficient SAT solvers for combinatorial search. Local search preprocessing accelerates the SAT solver's search by providing high-quality star...

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Should Multi-Agent Path Finding Algorithms Coordinate Target Arrival Times?

Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configuration. The Lifelong MAPF (LMAPF) problem is a well-studied online version of MAPF in which an agent rec...

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Multi-armed Bandit Algorithms for the Boolean Satisfiability Problem: A Survey

This paper provides a survey of recent literature on the use of multi-armed bandit algorithms to solve the Boolean satisfiability problem (SAT), a well-known NP-complete problem with broad applications in academia and industry. The application of ban...

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Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities

Multi-Agent Path Finding (MAPF) aims to arrange collision-free goal-reaching paths for a group of agents. Anytime MAPF solvers based on large neighborhood search (LNS) have gained prominence recently due to their flexibility and scalability, leading...

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Augmenting Exploration with Locally Greedy Probes

Enhancing Greedy Best First Search (GBFS) with stochastic exploration will often greatly improve search performance. In this work, we show that one way exploration does so is by helping the search find states that are "easy" for standard GBFS without...

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Decoupling Generation and Evaluation for Parallel Greedy Best-First Search

In order to understand and control the search behavior of parallel search, recent work has proposed a class of constrained parallel greedy best-first search algorithms which only expands states that satisfy some constraint. However, enforcing such co...

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Real-Time LaCAM for Real-Time MAPF

The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too long and be impractical in real-world applications. Instead, real-time pl...

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Guiding the Search for the Euclidean Shortest Path Problem

We consider the problem of reducing the search space of algorithms which solve the Euclidean Shortest Path Problem by traversing a precomputed navigation mesh. Heuristics can be used to guide this traversal. We show how upper and lower bounds to the...

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On Path Selection for Reduction-Based Solving of Multi-Agent Pathfinding Using Graph Pruning

Multi-agent pathfinding is the task of navigating a set of mobile agents in a shared environment such that they avoid collisions. Finding an optimal solution in terms of the length of the plan is known to be a computationally hard problem (NP-Hard)....

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Using Action-Policy Testing in RL to Reduce the Number of Bugs

Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior work has devised action-policy testing methodology, that identifies so-called bug states where policy p...

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Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties

This paper investigates a Traveling Salesman Problem with Time Windows and Vacant Penalties (TSP-TW-VP), which plans a path to service a set of machines at different locations within their respective time windows while minimizing two objective functi...

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Real-time Cost-algebraic Heuristic Search

Planning under time pressure arises in many situations. Real-time heuristic search, in which an agent must compute its next action within a prespecified time bound, has proven to be a useful model of real-time planning. However, it is laborious to pr...

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Task and Motion Planning Using Infinite Completion Tree and Agnostic Skills

This work builds upon existing task and motion planning (TAMP) frameworks by integrating pre-trained Sequencing Task-Agnostic Policies (STAP) and Effort Level Search (ELS) to create a hierarchical approach that decouples high-level task decisions fro...

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Heuristics for Bounded-Suboptimal Search

In heuristic search, it is well-established that different types of heuristics are suited for optimal heuristic search (OHS) and unbounded suboptimal search (USS). In OHS, the heuristic should minimize the error in estimating the true cost of the sho...

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Showing 16 to 30 of 47 results

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