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Found 22 papers, 13 papers with code
Model Checking Strategies from Synthesis Over Finite Traces
Our central result is that LTLf model checking of non-terminating transducers is \emph{exponentially harder} than that of terminating transducers.
Solving Quantum-Inspired Perfect Matching Problems via Tutte's Theorem-Based Hybrid Boolean Constraints
While general-purpose hybrid constraint solvers can be powerful, we show that direct encodings of the constrained-matching problem as hybrid constraints scale poorly and special techniques are still needed.
Synthesis from Satisficing and Temporal Goals
An alternative approach combining LTL synthesis with satisficing DS rewards (rewards that achieve a threshold) is sound and complete for integer discount factors, but, in practice, a fractional discount factor is desired.
DPER: Dynamic Programming for Exist-Random Stochastic SAT
Both MAP and ER-SSAT have the form $\operatorname{argmax}_X \sum_Y f(X, Y)$, where $f$ is a real-valued function over disjoint sets $X$ and $Y$ of variables.
DPO: Dynamic-Programming Optimization on Hybrid Constraints
Since a Bayesian network can be encoded as a literal-weighted CNF formula $\varphi$, we study Boolean MPE, a more general problem that requests a model $\tau$ of $\varphi$ with the highest weight, where the weight of $\tau$ is the product of weights of literals satisfied by $\tau$.
DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving
They lack, however, the ability to handle 1) (non-CNF) hybrid constraints, such as XORs and 2) generalized MaxSAT problems natively.
On Satisficing in Quantitative Games
Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games.
On Continuous Local BDD-Based Search for Hybrid SAT Solving
We explore the potential of continuous local search (CLS) in SAT solving by proposing a novel approach for finding a solution of a hybrid system of Boolean constraints.
LTLf Synthesis on Probabilistic Systems
Linear Temporal Logic over finite traces (LTLf) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties.
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time.
DPMC: Weighted Model Counting by Dynamic Programming on Project-Join Trees
We propose a unifying dynamic-programming framework to compute exact literal-weighted model counts of formulas in conjunctive normal form.
Parallel Weighted Model Counting with Tensor Networks
In this work, we explore the impact of multi-core and GPU use on tensor-network contraction for weighted model counting.
Data Structures and Algorithms
On the Power of Unambiguity in Büchi Complementation
In this work, we exploit the power of \emph{unambiguity} for the complementation problem of B\"uchi automata by utilizing reduced run directed acyclic graphs (DAGs) over infinite words, in which each vertex has at most one predecessor.
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints.
Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications
Our approach utilizes both explicit and symbolic representations of the state-space, and effectively leverages their complementary strengths.
Efficient Contraction of Large Tensor Networks for Weighted Model Counting through Graph Decompositions
We show that tree decompositions can be used both to find carving decompositions and to factor tensor networks with high-rank, structured tensors.
ADDMC: Weighted Model Counting with Algebraic Decision Diagrams
We present an algorithm to compute exact literal-weighted model counts of Boolean formulas in Conjunctive Normal Form.
On Hashing-Based Approaches to Approximate DNF-Counting
When the constraints are expressed as DNF formulas, Monte Carlo-based techniques have been shown to provide a fully polynomial randomized approximation scheme (FPRAS).
Symbolic LTLf Synthesis
LTLf synthesis is the process of finding a strategy that satisfies a linear temporal specification over finite traces.
Approximate Probabilistic Inference via Word-Level Counting
Techniques based on bit-level (or Boolean) hash functions require these problems to be propositionalized, making it impossible to leverage the remarkable progress made in SMT (Satisfiability Modulo Theory) solvers that can reason directly over words (or bit-vectors).
Distribution-Aware Sampling and Weighted Model Counting for SAT
We present a novel approach that works with a black-box oracle for weights of assignments and requires only an {\NP}-oracle (in practice, a SAT-solver) to solve both the counting and sampling problems.
The Complexity of Integer Bound Propagation
An important question is therefore whether strongly-polynomial algorithms exist that compute the common bound consistent fixpoint of a set of constraints.
