13 citations · 49 across the 33 of their papers we have counts for
21 papers · 1 filter
Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?
Gabriele La Malfa, Nitay Alon, Emanuele La Malfa +2
Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI techniques are increasingly…
The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processes
Benedikt Hornig, Reuth Mirsky
In shared autonomy, a critical tension arises when an automated assistant must choose between obeying a human's instruction and deliberately overriding it to prevent harm. This saf…
Proceedings of the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind
Nitay Alon, Joseph M. Barnby, Reuth Mirsky +1
This volume includes a selection of papers presented at the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2026 in Singapore on 26th January…
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord acc…
GRAIL: Goal Recognition Alignment through Imitation Learning
Osher Elhadad, Felipe Meneguzzi, Reuth Mirsky
Understanding an agent's goals from its behavior is fundamental to aligning AI systems with human intentions. Existing goal recognition methods typically rely on an optimal goal-or…
Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates
Reuth Mirsky
Artificial intelligence has made remarkable strides in recent years, achieving superhuman performance across a wide range of tasks. Yet despite these advances, most cooperative AI…
GRAML: Goal Recognition As Metric Learning
Matan Shamir, Reuth Mirsky
Goal Recognition (GR) is the problem of recognizing an agent's objectives based on observed actions. Recent data-driven approaches for GR alleviate the need for costly, manually cr…
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…
General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning
Osher Elhadad, Owen Morrissey, Reuth Mirsky
Understanding an agent's goal through its behavior is a common AI problem called Goal Recognition (GR). This task becomes particularly challenging in dynamic environments where goa…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming
Myke C. Cohen, David A. Grimm, Reuth Mirsky +1
Animal-Human-Machine (AHM) teams are a type of hybrid intelligence system wherein interactions between a human, AI-enabled machine, and animal members can result in unique capabili…
All You Need is Sally-Anne: ToM in AI Strongly Supported After Surpassing Tests for 3-Year-Olds
Nitay Alon, Joseph Barnby, Reuth Mirsky +1
Theory of Mind (ToM) is a hallmark of human cognition, allowing individuals to reason about others' beliefs and intentions. Engineers behind recent advances in Artificial Intellige…
Shared Control with Black Box Agents using Oracle Queries
Inbal Avraham, Reuth Mirsky
Shared control problems involve a robot learning to collaborate with a human. When learning a shared control policy, short communication between the agents can often significantly…
ODGR: Online Dynamic Goal Recognition
Matan Shamir, Osher Elhadad, Matthew E. Taylor +1
Traditionally, Reinforcement Learning (RL) problems are aimed at optimization of the behavior of an agent. This paper proposes a novel take on RL, which is used to learn the policy…
Stubborn: An Environment for Evaluating Stubbornness between Agents with Aligned Incentives
Ram Rachum, Yonatan Nakar, Reuth Mirsky
Recent research in multi-agent reinforcement learning (MARL) has shown success in learning social behavior and cooperation. Social dilemmas between agents in mixed-sum settings hav…
Proceedings of the AI-HRI Symposium at AAAI-FSS 2022
Zhao Han, Emmanuel Senft, Muneeb I. Ahmad +12
The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration on AI theory and methods aimed at HRI since…
Goal Recognition as Reinforcement Learning
Leonardo Rosa Amado, Reuth Mirsky, Felipe Meneguzzi
Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key…
Expected Value of Communication for Planning in Ad Hoc Teamwork
William Macke, Reuth Mirsky, Peter Stone
A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been rec…
Session Analysis using Plan Recognition
Reuth Mirsky, Ya'akov Gal, David Tolpin
This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a co…
Sequential Plan Recognition
Reuth Mirsky, Roni Stern, Ya'akov +2
Plan recognition algorithms infer agents' plans from their observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that th…
SLIM: Semi-Lazy Inference Mechanism for Plan Recognition
Retuh Mirsky, Ya'akov, Gal
Plan Recognition algorithms require to recognize a complete hierarchy explaining the agent's actions and goals. While the output of such algorithms is informative to the recognizer…