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.

scholarly journals Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning

2018 ◽  
Vol 18 (3-4) ◽  
pp. 571-588 ◽  
Author(s):  
TOBIAS KAMINSKI ◽  
THOMAS EITER ◽  
KATSUMI INOUE
Keyword(s):  
Experimental Evaluation ◽  
Answer Set Programming ◽  
Background Knowledge ◽  
Search Space ◽  
Combinatorial Search ◽  
Logic Programs ◽  
Search Problems ◽  
External Sources ◽  
Performance Gains ◽  
Answer Set

AbstractMeta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing Answer Set Programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and the need for grounding. To address these challenging issues, we encode MIL in the HEX-formalism, which is an extension of ASP that allows us to outsource the background knowledge, and we restrict the search space to compensate for a procedural bias in ASP. This way, the import of constants from the background knowledge can for a given type of meta-rules be limited to relevant ones. Moreover, by abstracting from term manipulations in the encoding and by exploiting the HEX interface mechanism, the import of such constants can be entirely avoided in order to mitigate the grounding bottleneck. An experimental evaluation shows promising results.

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 lpopt: A Rule Optimization Tool for Answer Set Programming

2020 ◽  
Vol 177 (3-4) ◽  
pp. 275-296
Author(s):  
Manuel Bichler ◽  
Michael Morak ◽  
Stefan Woltran
Keyword(s):  
Logic Programming ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Answer Set Programming ◽  
Weak Constraints ◽  
Rule Optimization ◽  
Theoretical Foundations ◽  
Arithmetic Expressions ◽  
Answer Set

State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant handtuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.

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.

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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