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Found 11 papers, 2 papers with code
Solving Projected Model Counting by Utilizing Treewidth and its Limits
Inspired by the observation that the so-called "treewidth" is one of the most prominent structural parameters, our algorithm utilizes small treewidth of the primal graph of the input instance.
Determining ActionReversibility in STRIPS Using Answer Set and Epistemic Logic Programming
In particular, we leverage an existing translation from PDDL to Answer Set Programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL.
Structural Decompositions of Epistemic Logic Programs
Epistemic logic programs (ELPs) are a popular generalization of standard Answer Set Programming (ASP) providing means for reasoning over answer sets within the language.
selp: A Single-Shot Epistemic Logic Program Solver
Epistemic Logic Programs (ELPs) are an extension of Answer Set Programming (ASP) with epistemic operators that allow for a form of meta-reasoning, that is, reasoning over multiple possible worlds.
On Uniform Equivalence of Epistemic Logic Programs
Epistemic Logic Programs (ELPs) extend Answer Set Programming (ASP) with epistemic negation and have received renewed interest in recent years.
Exploiting Treewidth for Projected Model Counting and its Limits
It runs in time $O({2^{2^{k+4}} n^2})$ where k is the treewidth and n is the input size of the instance.
DynASP2.5: Dynamic Programming on Tree Decompositions in Action
In this paper, we describe underlying concepts of our new implementation (DynASP2. 5) that shows competitive behavior to state-of-the-art ASP solvers even for finding just one solution when solving problems as the Steiner tree problem that have been modeled in ASP on graphs with low treewidth.
Answer Set Solving with Bounded Treewidth Revisited
Parameterized algorithms are a way to solve hard problems more efficiently, given that a specific parameter of the input is small.
Counting Answer Sets via Dynamic Programming
While the solution counting problem for propositional satisfiability (#SAT) has received renewed attention in recent years, this research trend has not affected other AI solving paradigms like answer set programming (ASP).
lpopt: A Rule Optimization Tool for Answer Set Programming
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 Power of Non-Ground Rules in Answer Set Programming
In its traditional application, a fixed ASP program for a given problem is designed and the actual instance of the problem is fed into the program as a set of facts.
