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Vedant Khandelwal

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6 papers

2 author rows

PRL Workshop 2025 Workshop Paper

Inductive Logic Programming for Heuristic Search

Rojina Panta
Vedant Khandelwal
Celeste Veronese
Amit Sheth
Daniele Meli
Forest Agostinelli

Pathfinding problems are found through computing, chemistry, mathematics, and robotics. Solving pathfinding problems is typically achieved through heuristic search, which is guided by a heuristic function that can be learned using deep neural networks. However, since deep neural networks are typically not explainable, the extraction of new knowledge from these learned heuristic functions is cumbersome. On the other hand, to the best of our knowledge, it has yet to be shown how heuristic functions represented as logic programs, which have been shown to be explainable, can be learned. In this work, we present an algorithm to learn heuristic functions represented as logic programs using dynamic programming and inductive logic programming. Furthermore, we build on dynamic programming concepts to improve the learned logic programs by reusing predicates learned for solving simpler pathfinding problem instances to solve more complex instances. We use the 8-puzzle to demonstrate the effectiveness of our algorithm. Code — https: //github. com/Rojina99/HeurSearchILP

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IS Journal 2025 Journal Article

NeuroSymbolic Knowledge-Grounded Planning and Reasoning in Artificial Intelligence Systems

Amit Sheth
Vedant Khandelwal
Kaushik Roy
Vishal Pallagani
Megha Chakraborty

Decision-support systems in AI-assisted health care require robust, interpretable, and user-centric processes that effectively handle natural language inputs. While large language models (LLMs) excel at generating coherent text, they struggle with complex reasoning and multistep planning tasks. In response, we propose a neurosymbolic framework that integrates LLMs with symbolic knowledge graphs, graph-based reasoners, and constraint-aware planning modules. This hybrid approach leverages LLMs for initial plan formulation while refining outcomes with structured, domain-specific representations that enforce safety standards, ensure regulatory compliance, and maintain logical consistency. Demonstrated through examples in health care and manufacturing, our method bridges the gap between unstructured language generation and formal reasoning, enhancing reliability in high-stakes applications and supporting dynamic, context-aware decision-making. The framework offers a scalable, trustworthy solution for complex, constraint-driven environments. By combining generative creativity with formal logic, our approach addresses the key limitations of LLMs, making it suitable for diverse, high-impact domains.

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AAAI Conference 2024 System Paper

GEAR-Up: Generative AI and External Knowledge-Based Retrieval: Upgrading Scholarly Article Searches for Systematic Reviews

Kaushik Roy
Vedant Khandelwal
Valerie Vera
Harshul Surana
Heather Heckman
Amit Sheth

This paper addresses the time-intensive nature of systematic reviews (SRs) and proposes a solution leveraging advancements in Generative AI (e.g., ChatGPT) and external knowledge augmentation (e.g., Retrieval-Augmented Generation). The proposed system, GEAR-Up, automates query development and translation in SRs, enhancing efficiency by enriching user queries with context from language models and knowledge graphs. Collaborating with librarians, qualitative evaluations demonstrate improved reproducibility and search strategy quality. Access the demo at https://youtu.be/zMdP56GJ9mU.

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ICAPS Conference 2024 Conference Paper

Specifying Goals to Deep Neural Networks with Answer Set Programming

Forest Agostinelli
Rojina Panta
Vedant Khandelwal

Recently, methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion. However, such methods either assume a predetermined goal or assume that goals will be given as fully-specified states. Therefore, specifying a set of goal states to these learned heuristic functions is often impractical. To address this issue, we introduce a method of training a heuristic function that estimates the distance between a given state and a set of goal states represented as a set of ground atoms in first-order logic. Furthermore, to allow for more expressive goal specification, we introduce techniques for specifying goals as answer set programs and using answer set solvers to discover sets of ground atoms that meet the specified goals. In our experiments with the Rubik

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AAAI Conference 2023 System Paper

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case

Kaushik Roy
Vedant Khandelwal
Raxit Goswami
Nathan Dolbir
Jinendra Malekar
Amit Sheth

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In addition, Alleviate's modular design and explainable decision-making lends itself to robust and continued feedback-based refinements to its design. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better to facilitate optimal care strategies.

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AAAI Conference 2022 System Paper

ALLURE: A Multi-Modal Guided Environment for Helping Children Learn to Solve a Rubik’s Cube with Automatic Solving and Interactive Explanations

Kausik Lakkaraju
Thahimum Hassan
Vedant Khandelwal
Prathamjeet Singh
Cassidy Bradley
Ronak Shah
Forest Agostinelli
Biplav Srivastava

Modern artificial intelligence (AI) methods have been used to solve problems that many humans struggle to solve. This opens up new opportunities for knowledge discovery and education. We demonstrate ALLURE, a collaborative educational AI system for learning to solve the Rubik’s cube that is designed to help students improve their problem solving skills. ALLURE can both find its own strategies for solving the Rubik’s cube and explain those strategies to humans. In the future, ALLURE will also be able to collaborate with humans by building on user-provided strategies for solving the Rubik’s cube and as well as generalize to other search and automated planning problems. Interaction between AI and user is facilitated by visual and natural language modalities.

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