5 papers
Wild Guesses and Mild Guesses in Active Concept Learning
Anirudh Chari, Neil Pattanaik
Human concept learning is typically active: learners choose which instances to query or test in order to reduce uncertainty about an underlying rule or category. Active concept lea…
Pheromone-based Learning of Optimal Reasoning Paths
Anirudh Chari, Aditya Tiwari, Richard Lian +2
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through chain-of-thought prompting, yet discovering effective reasoning methods for complex problem…
MINDSTORES: Memory-Informed Neural Decision Synthesis for Task-Oriented Reinforcement in Embodied Systems
Anirudh Chari, Suraj Reddy, Aditya Tiwari +2
While large language models (LLMs) have shown promising capabilities as zero-shot planners for embodied agents, their inability to learn from experience and build persistent mental…
Constrained Bandwidth Observation Sharing for Multi-Robot Navigation in Dynamic Environments via Intelligent Knapsack
Anirudh Chari, Rui Chen, Han Zheng +1
Multi-robot navigation is increasingly crucial in various domains, including disaster response, autonomous vehicles, and warehouse and manufacturing automation. Robot teams often m…
Space-Time Conflict Spheres for Constrained Multi-Agent Motion Planning
Anirudh Chari, Rui Chen, Changliu Liu
Multi-agent motion planning (MAMP) is a critical challenge in applications such as connected autonomous vehicles and multi-robot systems. In this paper, we propose a space-time con…