Author name cluster

Lakshmi Nair

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options

Lakshmi Nair
Ian Trase
J. Mark Kim

We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). Flow-of-Options enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic framework developed for autonomously solving Machine Learning (ML) tasks. FoO enforces diversity in LLM solutions through compressed and interpretable task representations, resulting in improvements of 38. 2% - 69. 2% on standard data science tasks, and 37. 4% - 47. 9% on therapeutic chemistry tasks, as compared to state-of-the-art baselines. With an overall operation cost under $1 per task, our framework is well-suited for cost-sensitive applications. Going beyond tabular classification and regression, we show the broader applicability of our FoO-based agentic system to tasks such as reinforcement learning and image generation. Our code is open-sourced at: https: //github. com/flagshippioneering/Flow-of-Options.

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IJCAI Conference 2023 Conference Paper

Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework (Extended Abstract)

Evana Gizzi
Lakshmi Nair
Sonia Chernova
Jivko Sinapov

Creative Problem Solving (CPS) is a sub-area within artificial intelligence that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning in AI, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways, remains a challenge. To stimulate further research in CPS, we contribute a definition and a framework of CPS, which we use to categorize existing AI methods in this field. We conclude our survey with open research questions, and suggested future directions.

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JAIR Journal 2022 Journal Article

Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework

Evana Gizzi
Lakshmi Nair
Sonia Chernova
Jivko Sinapov

Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.

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AAMAS Conference 2018 Conference Paper

Action Categorization for Computationally Improved Task Learning and Planning

Lakshmi Nair
Sonia Chernova

This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic, abstract data representation that maps objects to action categories (groups of actions), inspired by the psychological concept of action codes. We validate our approach in StarCraft and Lightworld domains; our results demonstrate several benefits of ACR relating to improved computational performance of planning and RL, by reducing the action space for the agent.

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