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Found 8 papers, 0 papers with code
Scheduling for Urban Air Mobility using Safe Learning
This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines.
Safe Multi-Agent Reinforcement Learning via Shielding
Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e. g., no unsafe states are ever visited) during the learning process. Unfortunately, current MARL methods do not have safety guarantees.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Constrained Active Classification Using Partially Observable Markov Decision Processes
The proposed framework relies on assigning a classification belief (a probability distribution) to the attributes of interest.
Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology
We study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively.
Near-Optimal Reactive Synthesis Incorporating Runtime Information
We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric.
Online Synthesis for Runtime Enforcement of Safety in Multi-Agent Systems
In this approach, which is fundamentally decentralized, the shield on every agent has two components: a pathfinder that corrects the behavior of the agent and an ordering mechanism that dynamically modifies the priority of the agent.
Reward-Based Deception with Cognitive Bias
In this paper, we consider deceiving adversaries with bounded rationality and in terms of expected rewards.
Distributed Synthesis of Surveillance Strategies for Mobile Sensors
We study the problem of synthesizing strategies for a mobile sensor network to conduct surveillance in partnership with static alarm triggers.
