Search Results for author:
Found 17 papers, 5 papers with code
Natural Strategic Ability in Stochastic Multi-Agent Systems
We also give a 2NEXPTIME complexity result for NatPATL* with the same restriction.
Finding an $ε$-close Variation of Parameters in Bayesian Networks
This paper addresses the $\epsilon$-close parameter tuning problem for Bayesian Networks (BNs): find a minimal $\epsilon$-close amendment of probability entries in a given set of (rows in) conditional probability tables that make a given quantitative constraint on the BN valid.
Weighted Programming
We study weighted programming, a programming paradigm for specifying mathematical models.
Under-Approximating Expected Total Rewards in POMDPs
We consider the problem: is the optimal expected total reward to reach a goal state in a partially observable Markov decision process (POMDP) below a given threshold?
Convex Optimization for Parameter Synthesis in MDPs
The parameter synthesis problem is to compute an instantiation of these unspecified parameters such that the resulting MDP satisfies the temporal logic specification.
Fine-Tuning the Odds in Bayesian Networks
This paper proposes various new analysis techniques for Bayes networks in which conditional probability tables (CPTs) may contain symbolic variables.
Inductive Synthesis for Probabilistic Programs Reaches New Horizons
The method builds on a novel inductive oracle that greedily generates counter-examples (CEs) for violating programs and uses them to prune the family.
Probabilistic Data with Continuous Distributions
In (Grohe, Kaminski, Katoen, Lindner; PODS 2020) we extend the declarative probabilistic programming language Generative Datalog, proposed by (B\'ar\'any et al.~2017) for discrete probability distributions, to continuous probability distributions and show that such programs yield generative models of continuous probabilistic databases.
Probabilistic Programming
Databases
Bayesian Inference by Symbolic Model Checking
This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks.
Verification of indefinite-horizon POMDPs
This paper considers the verification problem for partially observable MDPs, in which the policies make their decisions based on (the history of) the observations emitted by the system.
Simple Strategies in Multi-Objective MDPs (Technical Report)
That is, strategies that are pure (no randomization) and have bounded memory.
Counterexample-Driven Synthesis for Probabilistic Program Sketches
Probabilistic programs are key to deal with uncertainty in e. g. controller synthesis.
Shepherding Hordes of Markov Chains
This paper considers large families of Markov chains (MCs) that are defined over a set of parameters with finite discrete domains.
The Partially Observable Games We Play for Cyber Deception
Then, the deception problem is to compute a strategy for the deceiver that minimizes the expected cost of deception against all strategies of the infiltrator.
Synthesis in pMDPs: A Tale of 1001 Parameters
This paper considers parametric Markov decision processes (pMDPs) whose transitions are equipped with affine functions over a finite set of parameters.
Strategy Synthesis in POMDPs via Game-Based Abstractions
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications.
Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction
Probabilistic model checking is used to predict the human's behavior.
