scholarly journals Dynamic Multi-Agent Path Finding based on Conflict Resolution using Answer Set Programming

2020 ◽  
Vol 325 ◽  
pp. 223-229
Author(s):  
Basem Atiq ◽  
Volkan Patoglu ◽  
Esra Erdem
Keyword(s):  
Conflict Resolution ◽  
Answer Set Programming ◽  
Path Finding ◽  
Multi Agent ◽  
Answer Set

scholarly journals Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 974-989
Author(s):  
AYSU BOGATARKAN ◽  
ESRA ERDEM
Keyword(s):  
Answer Set Programming ◽  
Search Problem ◽  
Path Finding ◽  
Combinatorial Search ◽  
Transportation Modes ◽  
Optimal Resource ◽  
Multi Agent ◽  
Nonexistence Of Solutions ◽  
Velocity Constraints ◽  
Answer Set

AbstractThe multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.

scholarly journals Generalized Target Assignment and Path Finding Using Answer Set Programming

2017 ◽  
Author(s):  
Van Nguyen ◽  
Philipp Obermeier ◽  
Tran Cao Son ◽  
Torsten Schaub ◽  
William Yeoh
Keyword(s):  
Scale Up ◽  
Answer Set Programming ◽  
Specific Information ◽  
Path Finding ◽  
Practical Applications ◽  
Target Assignment ◽  
Specific Order ◽  
Multi Agent ◽  
Unequal Number ◽  
Answer Set

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.

scholarly journals A Declarative Method for Dynamic Multi-Agent Path Finding

10.29007/cnzw ◽  
2019 ◽  
Author(s):  
Aysu Bogatarkan ◽  
Volkan Patoglu ◽  
Esra Erdem
Keyword(s):  
Answer Set Programming ◽  
New Method ◽  
Search Problem ◽  
Path Finding ◽  
Combinatorial Search ◽  
Multiple Agents ◽  
New Agents ◽  
Two Agents ◽  
Multi Agent ◽  
Answer Set

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents such that no two agents collide with each other. We study a dynamic variant of MAPF, called D-MAPF, which allows changes in the environment (e.g., some existing obstacles may be removed from the environment or moved to some other location, or new obstacles may be included in the environment), and/or changes in the team (e.g., some existing agents may leave and some new agents may join the team) at different times. We introduce a new method to solve D-MAPF, using answer set programming.

scholarly journals Solving distributed constraint optimization problems using logic programming

2017 ◽  
Vol 17 (4) ◽  
pp. 634-683
Author(s):  
TIEP LE ◽  
TRAN CAO SON ◽  
ENRICO PONTELLI ◽  
WILLIAM YEOH
Keyword(s):  
Logic Programming ◽  
Optimization Problems ◽  
Answer Set Programming ◽  
Constraint Optimization ◽  
Logic Programs ◽  
Distributed Constraint Optimization ◽  
Multi Agent ◽  
Constraint Optimization Problems ◽  
Answer Set

AbstractThis paper explores the use ofAnswer Set Programming (ASP)in solvingDistributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) it shows how one can formulate DCOPs as logic programs; (2) it introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) it experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) it demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

scholarly journals Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming

2020 ◽  
Vol 34 (06) ◽  
pp. 9867-9874
Author(s):  
Rodrigo N. Gómez ◽  
Carlos Hernández ◽  
Jorge A. Baier
Keyword(s):  
Experimental Evaluation ◽  
Answer Set Programming ◽  
Goal Location ◽  
Multi Agent ◽  
Satisfiability Solving ◽  
Answer Set

Solving a Multi-Agent Pathfinding (MAPF) problem involves finding non-conflicting paths that lead a number of agents to their goal location. In the sum-of-costs variant of MAPF, one is also required to minimize the total number of moves performed by agents before stopping at the goal. Not surprisingly, since MAPF is combinatorial, a number of compilations to Satisfiability solving (SAT) and Answer Set Programming (ASP) exist. In this paper, we propose the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Unlike existing compilations to ASP that we are aware of, our encoding is the first that, after grounding, produces a number of clauses that is linear on the number of agents. In addition, the representation of the optimization objective is also carefully written, such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search- and SAT-based sum-of-costs MAPF solvers when grids are congested with agents.

Answer set programming and agents

2018 ◽  
Vol 33 ◽  
Author(s):  
Abeer Dyoub ◽  
Stefania Costantini ◽  
Giovanni De Gasperis
Keyword(s):  
Potential Role ◽  
Relevant Literature ◽  
Answer Set Programming ◽  
General View ◽  
Multi Agent Systems ◽  
Computational Logic ◽  
Future Directions ◽  
Multi Agent ◽  
Answer Set

AbstractIn this paper, we discuss the potential role of answer set programming (ASP) in the context of approaches to the development of agents and multi-agent systems especially in the realm of Computational Logic. After shortly recalling the main (computational-logic-based) agent-oriented frameworks, we introduce ASP; then, we discuss the usefulness of a potential integration of the two paradigms in a modular heterogeneous framework, and the feasibility of such integration. This also in the more general view of improving and empowering flexibility of agent-oriented frameworks. Relevant literature will be mentioned and discussed. Possible future directions and potential developments will be outlined.

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