240 citations · 1.1k across the 39 of their papers we have counts for
33 papers · 1 filter
The Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended Workflows
Moumita Choudhury, Amit Sarker, Shlomo Zilberstein
LLM agents are increasingly deployed in collaborative settings where they pursue separate tasks while sharing resources and operating under review or oversight processes. Such sett…
Knowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM Negotiation
Amit Sarker, Moumita Choudhury, Saaduddin Mahmud +1
Large language model (LLM) agents increasingly participate in negotiations on behalf of individuals and organizations, yet they remain vulnerable to manipulation, particularly in r…
Training Language Models to Cooperate with Inference-Time Controllers
Moumita Choudhury, Vanshaj Khattar, Jing Liu +4
Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reasoning. Existing post-training…
Persuasion Propagation: Does Persuasion Change AI Agent Behavior?
Hyejun Jeong, Amir Houmansadr, Shlomo Zilberstein +1
AI agents combine normal conversation with autonomous task execution, allowing earlier context to shape how later tasks are executed. Yet studying this possibility is challenging b…
Terrarium: Revisiting the Blackboard for Multi-Agent Safety, Privacy, and Security Studies
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires inter-agent collaboration. LLMs enable n…
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…
Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models
Saaduddin Mahmud, Dorian Benhamou Goldfajn, Shlomo Zilberstein
Distributed Constraint Optimization Problems (DCOPs) offer a powerful framework for multi-agent coordination but often rely on labor-intensive, manual problem construction. To addr…
Causal Explanations for Sequential Decision Making Under Uncertainty
Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman +1
We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reaso…
Mitigating Negative Side Effects via Environment Shaping
Sandhya Saisubramanian, Shlomo Zilberstein
Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the s…
Helpfulness as a Key Metric of Human-Robot Collaboration
Richard G. Freedman, Steven J. Levine, Brian C. Williams +1
As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions. One such dimension is effectiveness: people wi…
Improving Competence for Reliable Autonomy
Connor Basich, Justin Svegliato, Kyle Hollins Wray +2
Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios…
Learning to Optimize Autonomy in Competence-Aware Systems
Connor Basich, Justin Svegliato, Kyle Hollins Wray +3
Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows…
Responsive Planning and Recognition for Closed-Loop Interaction
Richard G. Freedman, Yi Ren Fung, Roman Ganchin +1
Many intelligent systems currently interact with others using at least one of fixed communication inputs or preset responses, resulting in rigid interaction experiences and extensi…
Minimizing the Negative Side Effects of Planning with Reduced Models
Sandhya Saisubramanian, Shlomo Zilberstein
Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to re…
Planning in Stochastic Environments with Goal Uncertainty
Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda +1
We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a general framework to model path planning and decision making in stochastic environments with goal uncert…
An Anytime Algorithm for Task and Motion MDPs
Siddharth Srivastava, Nishant Desai, Richard Freedman +1
Integrated task and motion planning has emerged as a challenging problem in sequential decision making, where a robot needs to compute high-level strategy and low-level motion plan…
Generalizing the Role of Determinization in Probabilistic Planning
Luis Pineda, Shlomo Zilberstein
The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning. The computational hardness of SSPs has sparked interest in determinization-based…
Robust Optimization for Tree-Structured Stochastic Network Design
Xiaojian Wu, Akshat Kumar, Daniel Sheldon +1
Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation pla…
A Bilinear Programming Approach for Multiagent Planning
Marek Petrik, Shlomo Zilberstein
Multiagent planning and coordination problems are common and known to be computationally hard. We show that a wide range of two-agent problems can be formulated as bilinear program…
Policy Iteration for Decentralized Control of Markov Decision Processes
Daniel S. Bernstein, Christopher Amato, Eric A. Hansen +1
Coordination of distributed agents is required for problems arising in many areas, including multi-robot systems, networking and e-commerce. As a formal framework for such problems…
The Complexity of Decentralized Control of Markov Decision Processes
Daniel S Bernstein, Shlomo Zilberstein, Neil Immerman
Planning for distributed agents with partial state information is considered from a decision- theoretic perspective. We describe generalizations of both the MDP and POMDP models th…
Symbolic Generalization for On-line Planning
Zhengzhu Feng, Eric A. Hansen, Shlomo Zilberstein
Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line pl…
Region-Based Incremental Pruning for POMDPs
Zhengzhu Feng, Shlomo Zilberstein
We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dyna…
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
Daniel Szer, Francois Charpillet, Shlomo Zilberstein
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) wit…