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cs.AI2025

Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance

Yotam Amitai, Reuth Mirsky, Ofra Amir

In human-agent teams, openly sharing goals is often assumed to enhance planning, collaboration, and effectiveness. However, direct communication of these goals is not always feasib…

cs.AI2025

Interactive Explanations for Reinforcement-Learning Agents

Yotam Amitai, Ofra Amir, Guy Avni

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL)…

cs.AI2023

Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes

Yotam Amitai, Yael Septon, Ofra Amir

Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, see…

cs.AI2023

ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents

Yotam Amitai, Guy Avni, Ofra Amir

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL)…

cs.AI2021

"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison

Yotam Amitai, Ofra Amir

With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires users to understand the agent…