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Found 17 papers, 3 papers with code
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
Verifiable Reinforcement Learning Systems via Compositionality
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Task-Guided IRL in POMDPs that Scales
First, they require an excessive amount of data due to the information asymmetry between the expert and the learner.
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse.
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.
Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach
Specifically, we study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expressed in a recently-proposed language called graph temporal logic (GTL).
Verifiable and Compositional Reinforcement Learning Systems
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Task-Guided Inverse Reinforcement Learning Under Partial Information
Nevertheless, the resulting formulation is still nonconvex due to the intrinsic nonconvexity of the so-called forward problem, i. e., computing an optimal policy given a reward function, in POMDPs.
Polynomial-Time Algorithms for Multi-Agent Minimal-Capacity Planning
We develop an algorithm that solves this graph problem in time that is \emph{polynomial} in the number of agents, target locations, and size of the consumption Markov decision process.
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.
Synthesis of Provably Correct Autonomy Protocols for Shared Control
We design the autonomy protocol to ensure that the resulting robot behavior satisfies given safety and performance specifications in probabilistic temporal logic.
Reward-Based Deception with Cognitive Bias
In this paper, we consider deceiving adversaries with bounded rationality and in terms of expected rewards.
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.
Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraints
Such a policy minimizes the predictability of the paths it generates, or dually, maximizes the exploration of different paths in an MDP while ensuring the satisfaction of a temporal logic specification.
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.
Verification of Markov Decision Processes with Risk-Sensitive Measures
We develop a method for computing policies in Markov decision processes with risk-sensitive measures subject to temporal logic constraints.
Synthesis of Shared Control Protocols with Provable Safety and Performance Guarantees
We formalize synthesis of shared control protocols with correctness guarantees for temporal logic specifications.
