1 citations · 4 across the 7 of their papers we have counts for
7 papers
Explaining Decentralized Multi-Agent Reinforcement Learning Policies
Kayla Boggess, Sarit Kraus, Lu Feng
Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, m…
Evaluating Human Trust in LLM-Based Planners: A Preliminary Study
Shenghui Chen, Yunhao Yang, Kayla Boggess +3
Large Language Models (LLMs) are increasingly used for planning tasks, offering unique capabilities not found in classical planners such as generating explanations and iterative re…
Explainable Multi-Agent Reinforcement Learning for Temporal Queries
Kayla Boggess, Sarit Kraus, Lu Feng
As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors o…
Toward Policy Explanations for Multi-Agent Reinforcement Learning
Kayla Boggess, Sarit Kraus, Lu Feng
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 drivi…
Multi-Objective Controller Synthesis with Uncertain Human Preferences
Shenghui Chen, Kayla Boggess, David Parker +1
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 control…
Towards Personalized Explanation of Robot Path Planning via User Feedback
Kayla Boggess, Shenghui Chen, Lu Feng
Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaborati…
Towards Transparent Robotic Planning via Contrastive Explanations
Shenghui Chen, Kayla Boggess, Lu Feng
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanati…