scholarly journals Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search

Author(s):  
Jiaoyang Li ◽  
Ariel Felner ◽  
Eli Boyarski ◽  
Hang Ma ◽  
Sven Koenig

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependencies between agents. Empirically, CBS with either new heuristic significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50.

Author(s):  
Jiaoyang Li ◽  
Pavel Surynek ◽  
Ariel Felner ◽  
Hang Ma ◽  
T. K. Satish Kumar ◽  
...  

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a twolevel tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. Geometric agents are referred to as “large” agents because they can occupy multiple points at the same time. In this paper, we formalize and study LAMAPF, i.e., MAPF for large agents. We first show how CBS can be adapted to solve LA-MAPF. We then present a generalized version of CBS, called Multi-Constraint CBS (MCCBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. We introduce three different approaches to choose such constraints as well as an approach to compute admissible heuristics for the high-level search. Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. The best variant also outperforms EPEA* (a state-of-the-art A*-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.


Author(s):  
Pavel Surynek

We unify search-based and compilation-based approaches to multi-agent path finding (MAPF) through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph to given goal vertices so that they do not collide. We rephrase Conflict-Based Search (CBS), one of the state-of-the-art algorithms for optimal MAPF solving, in the terms of SMT. This idea combines SAT-based solving known from MDD-SAT, a SAT-based optimal MAPF solver, at the low-level with conflict elimination of CBS at the high-level. Where the standard CBS branches the search after a conflict, we refine the propositional model with a disjunctive constraint. Our novel algorithm called SMT-CBS hence does not branch at the high-level but incrementally extends the propositional model. We experimentally compare SMT-CBS with CBS, ICBS, and MDD-SAT.


Author(s):  
Hang Ma ◽  
Glenn Wagner ◽  
Ariel Felner ◽  
Jiaoyang Li ◽  
T. K. Satish Kumar ◽  
...  

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.


2014 ◽  
Vol 13 (03) ◽  
pp. 133-153 ◽  
Author(s):  
Luo Biao ◽  
Wan Liang ◽  
Liang Liang

The high level of complexity of tourism supply chain and the inherent risks that exist in the demand and supply of resources are viewed as major limiting factors in achieving high level performance. Though emerging literature on risk management in tourism industry or its equivalent exists, progress in this area is uneven, as most research focuses on this problem from the traditional single business risk management perspective, without considering the entire range of different suppliers involved in the provision and consumption of tourism products. This study applies risk management theory to a new research perspective, which is tourism supply chain management (SCM). This paper develops a framework for the design of a multi-agent-based decision support system (DSS) based on multi-agent theory and technique, in order to manage disruptions and mitigate risks in tourism supply chain.


Author(s):  
Krishna N. Jha ◽  
Andrea Morris ◽  
Ed Mytych ◽  
Judith Spering

Abstract Designing aircraft parts requires extensive coordination among multiple distributed design groups. Achieving such a coordination is time-consuming and expensive, but the cost of ignoring or minimizing it is much higher in terms of delayed and inferior quality products. We have built a multi-agent-based system to provide the desired coordination among the design groups, the legacy applications, and other resources during the preliminary design (PD) process. A variety of agents are used to model the various design and control functionalities. The agent-representation includes a formal representation of the task-structures. A web-based user-interface provides high-level interface to the users. The agents collaborate to achieve the design goals.


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