4 citations · 8 across the 16 of their papers we have counts for
17 papers
Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
James Oswald, Daniel Obolensky, Volodymyr Varha +5
The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work sugges…
Evaluating Joinable Column Discovery Approaches for Context-Aware Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
Joinable Column Discovery is a critical challenge in automating enterprise data analysis. While existing approaches focus on syntactic overlap and semantic similarity, there remain…
QueryGym: Step-by-Step Interaction with Relational Databases
Haritha Ananthakrishnan, Harsha Kokel, Kelsey Sikes +4
We introduce QueryGym, an interactive environment for building, testing, and evaluating LLM-based query planning agents. Existing frameworks often tie agents to specific query lang…
Seemingly Simple Planning Problems are Computationally Challenging: The Countdown Game
Michael Katz, Harsha Kokel, Sarath Sreedharan
There is a broad consensus that the inability to form long-term plans is one of the key limitations of current foundational models and agents. However, the existing planning benchm…
TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinabl…
Make Planning Research Rigorous Again!
Michael Katz, Harsha Kokel, Christian Muise +2
In over sixty years since its inception, the field of planning has made significant contributions to both the theory and practice of building planning software that can solve a nev…
ACPBench Hard: Unrestrained Reasoning about Action, Change, and Planning
Harsha Kokel, Michael Katz, Kavitha Srinivas +1
The ACPBench dataset provides atomic reasoning tasks required for efficient planning. The dataset is aimed at distilling the complex plan generation task into separate atomic reaso…
Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains
Nikhilesh Prabhakar, Ranveer Singh, Harsha Kokel +2
Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environme…
Can Cross Encoders Produce Useful Sentence Embeddings?
Haritha Ananthakrishnan, Julian Dolby, Harsha Kokel +2
Cross encoders (CEs) are trained with sentence pairs to detect relatedness. As CEs require sentence pairs at inference, the prevailing view is that they can only be used as re-rank…
ACPBench: Reasoning about Action, Change, and Planning
Harsha Kokel, Michael Katz, Kavitha Srinivas +1
There is an increasing body of work using Large Language Models (LLMs) as agents for orchestrating workflows and making decisions in domains that require planning and multi-step re…
Automating Thought of Search: A Journey Towards Soundness and Completeness
Daniel Cao, Michael Katz, Harsha Kokel +2
Large language models (LLMs) are being used to solve planning problems that require search. Most of the literature uses LLMs as world models to define the search space, forgoing so…
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz +7
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful…
Large Language Models as Planning Domain Generators
James Oswald, Kavitha Srinivas, Harsha Kokel +3
Developing domain models is one of the few remaining places that require manual human labor in AI planning. Thus, in order to make planning more accessible, it is desirable to auto…
Thought of Search: Planning with Language Models Through The Lens of Efficiency
Michael Katz, Harsha Kokel, Kavitha Srinivas +1
Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for t…
LakeBench: Benchmarks for Data Discovery over Data Lakes
Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz +6
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to…
Human-guided Collaborative Problem Solving: A Natural Language based Framework
Harsha Kokel, Mayukh Das, Rakibul Islam +10
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication. Our framework consists of three components --…
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…