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Found 21 papers, 7 papers with code
Approximate Dec-POMDP Solving Using Multi-Agent A*
Our experiments demonstrate the efficacy and scalability of the approach.
Imprecise Probabilities Meet Partial Observability: Game Semantics for Robust POMDPs
Partially observable Markov decision processes (POMDPs) rely on the key assumption that probability distributions are precisely known.
Factored Online Planning in Many-Agent POMDPs
However, the challenges of value estimation and belief estimation have only been tackled individually, which prevents existing methods from scaling to settings with many agents.
Learning Formal Specifications from Membership and Preference Queries
Active learning is a well-studied approach to learning formal specifications, such as automata.
Efficient Sensitivity Analysis for Parametric Robust Markov Chains
As our main contribution, we present an efficient method to compute these partial derivatives.
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking.
Abstraction-Refinement for Hierarchical Probabilistic Models
The key ideas to accelerate analysis of such programs are (1) to treat the behavior of the subroutine as uncertain and only remove this uncertainty by a detailed analysis if needed, and (2) to abstract similar subroutines into a parametric template, and then analyse this template.
Safe Reinforcement Learning via Shielding under Partial Observability
Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment.
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation
Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety.
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.
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.
Robust Finite-State Controllers for Uncertain POMDPs
(3) We linearize this dual problem and (4) solve the resulting finite linear program to obtain locally optimal solutions to the original problem.
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.
Enforcing Almost-Sure Reachability in POMDPs
Partially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information.
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.
Safe Reinforcement Learning via Probabilistic Shields
This paper targets the efficient construction of a safety shield for decision making in scenarios that incorporate uncertainty.
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.
