Multi-Directional Heuristic Search

Atzmon, Dor; Li, Jiaoyang; Felner, Ariel; Nachmani, Eliran; Shperberg, Shahaf S.; Sturtevant, Nathan R.; Koenig, Sven

doi:10.24963/ijcai.2020/562

Cited by 10 publications

(13 citation statements)

References 17 publications

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“…For large problem instances, this method may become memory and run-time expensive, due to the maintenance of large meeting tables. We may consider incorporating an algorithm such as the recently-proposed MM* algorithm [28], for the Multi-Agent Meeting problem. Furthermore, we may couple meetings and paths planning, and handle conflicts during the search for a meeting.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

Greshler¹,

Gordon²,

Salzman³

et al. 2021

Preprint

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We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-based algorithm for solving the problem optimally for a wide set of Co-MAPF problems. Co-CBS uses a cooperationplanning module integrated into CBS such that cooperation planning is decoupled from path planning. Finally, we present empirical results on several MAPF benchmarks demonstrating our algorithm's properties.

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Section: Discussion and Future Workmentioning

confidence: 99%

Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

Greshler¹,

Gordon²,

Salzman³

et al. 2021

Preprint

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“…The choice of the problem (min-sum, min-max, or others) depends on the purpose of using meeting points. Note that we can utilize the existing pruning techniques for A ms and/or A mm developed in the literature (Yan, Zhao, and Ng 2015;Atzmon et al 2020); The worst-case complexity of the solver is known to be O(|U | 2 + |V ||U |). We denote by T (A) be the complexity of the algorithm.…”

Section: Preliminary Finding Meeting Points On Graphsmentioning

confidence: 99%

“…Although we have various problems, we focus on the problem of finding meeting points (OMP) on graphs (Li et al 2015;Yan, Zhao, and Ng 2015;Atzmon et al 2020), as its formal definition is given later. This is because 1) transportation is a basic function in cities and residents, 2) it is along with road networks, and 3) finding a meeting point is an essential task for designing city functions or trips (Shang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Planning with Explanations for Finding Desired Meeting Points on Graphs

Otaki

2022

AAAI

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Combinatorial optimization problems are ubiquitous for decision making in planning social infrastructures. In real-world scenarios, a decision-maker needs to solve his/her problem iteratively until he/she satisfies solutions, but such an iterative process remains challenging. This paper studies a new explainable framework, particularly for finding meeting points, which is a key optimization problem for designing facility locations. Our framework automatically fills the gap between its input instance and instances from which a user could obtain the desired outcome, where computed solutions are judged by the user. The framework also provides users with explanations, representing the difference of instances for deeply understanding the process and its inside. Explanations are clues for users to understand their situation and implement suggested results in practice (e.g., designing a coupon for free travel). We experimentally demonstrate that our search-based framework is promising to solve instances with generating explanations in a sequential decision-making process.

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“…There are also some other routing problems, such as routing of hydraulic systems (Chambon and Tollenaere, 1991), routing of ship pipes (Kang, Sehyun, and Hah, 1999), routing of urban water systems (Christodoulou and Ellinas, 2010;Grayman, Clark, and Males, 1988), and routing of city logistics (Barceló, Grzybowska, and Pardo, 2007;Ehmke, Steinert, and Mattfeld, 2012). Multi-agent path finding, a task similar to circuit routing, has been exhaustively studied in robotics and video games (Silver, 2005;Sturtevant, 2014;Babayan, Uchida, and Gershman, 2018;Atzmon et al, 2020). But in a multi-agent path finding task, paths are allowed to intersect as long as the agents do not appear in the same place at the same time.…”

Section: Other Routing Tasksmentioning

confidence: 99%

Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization

Huang,

Wang,

et al. 2021

Preprint

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Circuit routing has been a historically challenging problem in designing electronic systems such as very large-scale integration (VLSI) and printed circuit boards (PCBs). The main challenge is that connecting a large number of electronic components under specific design rules involves a very large search space. Early solutions are typically designed with hard-coded heuristics, which suffer from problems of non-optimal solutions and lack of flexibility for new design needs. Although a few learning-based methods have been proposed recently, they are typically cumbersome and hard to extend to large-scale applications. In this work, we propose a new algorithm for circuit routing, named as Ranking Cost, which innovatively combines search-based methods (i.e., A* algorithm) and learningbased methods (i.e., Evolution Strategies) to form an efficient and trainable router. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global objective. We also train a ranking parameter, which can produce the ranking order and further improve the performance of our method. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration. In the experiments, we compare with the sequential A* algorithm and a canonical reinforcement learning approach, and results show that our method outperforms these baselines with higher connectivity rates and better scalability.Preprint. Under review.

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Multi-Directional Heuristic Search

Cited by 10 publications

References 17 publications

Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

Planning with Explanations for Finding Desired Meeting Points on Graphs

Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization

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