7 papers · 1 filter
Monte Carlo Query Search: Active Capability Assessment of AI Agents
Daniel Bramblett, Rushang Karia, Adrian Ciotinga +3
Black-box AI (BBAI) systems, including foundation-model agents, are increasingly used for sequential decision making. Safe deployment requires methods for characterizing what such…
Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions
Rashmeet Kaur Nayyar, Naman Shah, Siddharth Srivastava
Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisions about continuous action param…
Belief-State Query Policies for User-Aligned POMDPs
Daniel Bramblett, Siddharth Srivastava
Planning in real-world settings often entails addressing partial observability while aligning with users' requirements. We present a novel framework for expressing users' constrain…
Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks
Rushang Karia, Daniel Bramblett, Daksh Dobhal +1
This paper presents AutoEval, a novel benchmark for scaling Large Language Model (LLM) assessment in formal tasks with clear notions of correctness, such as truth maintenance in tr…
Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and Planning
Rashmeet Kaur Nayyar, Siddharth Srivastava
Abstraction is key to scaling up reinforcement learning (RL). However, autonomously learning abstract state and action representations to enable transfer and generalization remains…
AI Planning: A Primer and Survey (Preliminary Report)
Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava +2
Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations researc…