16 citations · 32 across the 19 of their papers we have counts for
8 papers · 1 filter
AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
Harish Karthikeyan, Yue Guo, Leo de Castro +5
As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls are insuffici…
PADME: Procedure Aware DynaMic Execution
Deepeka Garg, Sihan Zeng, Annapoorani L. Narayanan +2
Learning to autonomously execute long-horizon procedures from natural language remains a core challenge for intelligent agents. Free-form instructions such as recipes, scientific p…
Downside Risk-Aware Equilibria for Strategic Decision-Making
Oliver Slumbers, Benjamin Patrick Evans, Sumitra Ganesh +1
Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently…
Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis
Sihan Zeng, Benjamin Patrick Evans, Sujay Bhatt +3
We study policy optimization in Stackelberg mean field games (MFGs), a hierarchical framework for modeling the strategic interaction between a single leader and an infinitely large…
Modelling bounded rational decision-making through Wasserstein constraints
Benjamin Patrick Evans, Leo Ardon, Sumitra Ganesh
Modelling bounded rational decision-making through information constrained processing provides a principled approach for representing departures from rationality within a reinforce…
Generating Structured Plan Representation of Procedures with LLMs
Deepeka Garg, Sihan Zeng, Sumitra Ganesh +1
In this paper, we address the challenges of managing Standard Operating Procedures (SOPs), which often suffer from inconsistencies in language, format, and execution, leading to op…
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines
Leo Ardon, Daniel Furelos-Blanco, Roko Parac +1
Reward machines (RMs) are an effective approach for addressing non-Markovian rewards in reinforcement learning (RL) through finite-state machines. Traditional RMs, which label edge…
ADAGE: A generic two-layer framework for adaptive agent based modelling
Benjamin Patrick Evans, Sihan Zeng, Sumitra Ganesh +1
Agent-based models (ABMs) are valuable for modelling complex, potentially out-of-equilibria scenarios. However, ABMs have long suffered from the Lucas critique, stating that agent…