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Found 5 papers, 2 papers with code
Explainable Multi-Agent Reinforcement Learning for Temporal Queries
As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments.
Toward Policy Explanations for Multi-Agent Reinforcement Learning
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving.
Multi-Objective Controller Synthesis with Uncertain Human Preferences
Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which concerns the problem of computing an optimal controller subject to multiple (possibly conflicting) criteria.
Towards Personalized Explanation of Robot Path Planning via User Feedback
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration.
Towards Transparent Robotic Planning via Contrastive Explanations
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust.
