.png){kind=icon}
Return
Solving Hard Combinatorial Optimization Problems with PyQASP
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-15981-6_12.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Answer Set Programming with Quantifiers (ASP(Q)) extends classical ASP to naturally capture problems within the polynomial hierarchy (PH). Recently, the formalism has been enriched with weak constraints to express both local and global optimization criteria, enabling the modeling of problems in Delta(P)(n+1). In this paper, we present the first implementation of ASP(Q) with global weak constraints, built on top of the state-of-the-art ASP(Q) system PyQASP, based on an upper-bound improving strategy that effectively guides the search toward optimal solutions. Experiments demonstrate that our approach can be effectively applied to solve hard optimization problems.
Keywords:
Answer Set Programming
ASP with Quantifiers
Weak Constraints
Optimization
Journal
P
IF:
0
Papers:
12
Citations:
0
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSave
View more