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Found 20 papers, 2 papers with code
Guaranteed Reachability on Riemannian Manifolds for Unknown Nonlinear Systems
The results are general enough to apply on systems that operate on any complete Riemannian manifold.
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
The rise of foundation models trained on multiple tasks with impressively large datasets from a variety of fields has led researchers to believe that these models may provide common sense reasoning that existing planners are missing.
A Moral Imperative: The Need for Continual Superalignment of Large Language Models
This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs).
ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent obtains its true state information from the sensors -- are limited, presents a significant challenge.
Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments.
Viability under Degraded Control Authority
In this work, we solve the problem of quantifying and mitigating control authority degradation in real time.
Identifying Single-Input Linear System Dynamics from Reachable Sets
This paper is concerned with identifying linear system dynamics without the knowledge of individual system trajectories, but from the knowledge of the system's reachable sets observed at different times.
Losing Control of your Network? Try Resilience Theory
To assess system vulnerability, we establish resilience conditions for networks with a subsystem enduring a loss of control authority over some of its actuators.
Delayed resilient trajectory tracking after partial loss of control authority over actuators
After the loss of control authority over thrusters of the Nauka module, the International Space Station lost attitude control for 45 minutes with potentially disastrous consequences.
Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs
Motivated by the problem of maintenance and inspection of a group of infrastructure components with independent dynamics, this paper presents an algorithm to find the optimal policy for a multi-component budget-constrained POMDP.
Resilience of Linear Systems to Partial Loss of Control Authority
Relying on Lyapunov theory we derive analytical bounds on the reach times of the nominal and malfunctioning systems in order to quantify their resilience.
Quantitative Resilience of Linear Systems
Actuator malfunctions may have disastrous consequences for systems not designed to mitigate them.
Quantitative Resilience of Generalized Integrators
By definition, a system is resilient if it can still reach a target after a partial loss of control authority.
Efficient Strategy Synthesis for MDPs with Resource Constraints
We consider qualitative strategy synthesis for the formalism called consumption Markov decision processes.
Quantitative Resilience of Linear Driftless Systems
By definition, a system is resilient if it can still reach a target after a loss of control authority.
Systems and Control Systems and Control 93-06
Designing Resilient Linear Driftless Systems
In contrast with the settings of robust control and fault-tolerant control, we consider undesirable but observable inputs of the same magnitude as controls since they are produced by a faulty actuator of the system.
Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation
This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments.
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.
Deception in Optimal Control
In this paper, we consider an adversarial scenario where one agent seeks to achieve an objective and its adversary seeks to learn the agent's intentions and prevent the agent from achieving its objective.
Control-Oriented Learning on the Fly
This paper focuses on developing a strategy for control of systems whose dynamics are almost entirely unknown.
