1 citations · 1 across the 10 of their papers we have counts for
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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…
Autonomous Assessment of Generalizability of AI Agent Capabilities
Daniel Bramblett, Rushang Karia, Adrian Ciotinga +3
Safe deployment of black-box AI (BBAI) systems such as foundation model agents requires methods for evaluating their capabilities in novel settings. We define an agent's capability…
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…
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…
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…
Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings
Rushang Karia, Pulkit Verma, Alberto Speranzon +1
This paper introduces a new approach for continual planning and model learning in relational, non-stationary stochastic environments. Such capabilities are essential for the deploy…