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Pulkit Verma

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10 papers

1 author row

HAXP Workshop 2025 Workshop Paper

A Collaborative Numeric Task Planning Framework based on Constraint Translations using LLMs

Anthony Favier
Ngoc La
Pulkit Verma
Julie Shah

Automated planning systems require formal constraint specifications that create significant barriers for domain experts not familiar with those formal specifications, thereby limiting the practical adoption of powerful planning tools in collaborative planning settings. To overcome this challenge, we propose an LLM-based pipeline to translate human natural language constraints into formal hard-trajectory constraints. The initial user input is first refined and decomposed into more explicit natural language constraints, both preparing constraints for formal encoding and offering a chance for the human to review and correct any misinterpretation. Then, the decomposed constraints are encoded into PDDL3. By integrating this with an automated planner, a graphical interface, and PDSim, we created a closed loop where the human gets plan simulations as feedback to their natural language constraints. This innovative collaborative planning framework enables users to leverage their intuition and expertise to intuitively guide automated planning without time-consuming programming expert interventions. Through an ablation study, we demonstrate how our approach significantly improves the syntax and semantic accuracy of the translations compared to direct LLM translations. Our results demonstrate the potential of collaborative planning without technical expert interventions for higher-quality automated solving. On the other hand, our negative results seem to highlight the limitations of using PDDL3 constraints to leverage human high-level guidance as we expected, raising interesting reflections and potential discussions.

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PRL Workshop 2025 Workshop Paper

AI Planning: A Primer and Survey (Preliminary Report)

Dillon Ze Chen
Pulkit Verma
Siddharth Srivastava
Michael Katz
Sylvie Thiebaux

Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations research, among others. Despite recent efforts to “bridge the gaps” between these communities, there remain many insights that have not yet transcended the boundaries. Our goal in this paper is to provide a brief and non-exhaustive primer on ideas well-known in AP, but less so in other subdisciplines. We do so by introducing the classical AP problem and representation, and extensions that handle uncertainty and time through the Markov Decision Process formalism. Next, we survey state-of-the-art techniques and ideas for solving AP problems, focusing on their ability to exploit problem structure. Lastly, we cover subfields within AP for learning structure from unstructured inputs and learning to generalise to unseen scenarios and situations.

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AAAI Conference 2024 Short Paper

Data Efficient Paradigms for Personalized Assessment of Black-Box Taskable AI Systems

Pulkit Verma

The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. My dissertation focuses on developing paradigms that enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response interface is sufficient to efficiently derive a user-interpretable model of a system's capabilities.

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NeurIPS Conference 2023 Conference Paper

Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings

Pulkit Verma
Rushang Karia
Siddharth Srivastava

It is essential for users to understand what their AI systems can and can't do in order to use them safely. However, the problem of enabling users to assess AI systems with sequential decision-making (SDM) capabilities is relatively understudied. This paper presents a new approach for modeling the capabilities of black-box AI systems that can plan and act, along with the possible effects and requirements for executing those capabilities in stochastic settings. We present an active-learning approach that can effectively interact with a black-box SDM system and learn an interpretable probabilistic model describing its capabilities. Theoretical analysis of the approach identifies the conditions under which the learning process is guaranteed to converge to the correct model of the agent; empirical evaluations on different agents and simulated scenarios show that this approach is few-shot generalizable and can effectively describe the capabilities of arbitrary black-box SDM agents in a sample-efficient manner.

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IJCAI Conference 2023 Conference Paper

Sample Efficient Paradigms for Personalized Assessment of Taskable AI Systems

Pulkit Verma

The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop paradigms that would enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system's capabilities in fully observable settings.

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AAAI Conference 2022 Conference Paper

Differential Assessment of Black-Box AI Agents

Rashmeet Kaur Nayyar
Pulkit Verma
Siddharth Srivastava

Much of the research on learning symbolic models of AI agents focuses on agents with stationary models. This assumption fails to hold in settings where the agent’s capabilities may change as a result of learning, adaptation, or other post-deployment modifications. Efficient assessment of agents in such settings is critical for learning the true capabilities of an AI system and for ensuring its safe usage. In this work, we propose a novel approach to differentially assess black-box AI agents that have drifted from their previously known models. As a starting point, we consider the fully observable and deterministic setting. We leverage sparse observations of the drifted agent’s current behavior and knowledge of its initial model to generate an active querying policy that selectively queries the agent and computes an updated model of its functionality. Empirical evaluation shows that our approach is much more efficient than re-learning the agent model from scratch. We also show that the cost of differential assessment using our method is proportional to the amount of drift in the agent’s functionality.

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KR Conference 2022 Conference Paper

Discovering User-Interpretable Capabilities of Black-Box Planning Agents

Pulkit Verma
Shashank Rao Marpally
Siddharth Srivastava

Several approaches have been developed for answering users' specific questions about AI behavior and for assessing their core functionality in terms of primitive executable actions. However, the problem of summarizing an AI agent's broad capabilities for a user is comparatively new. This paper presents an algorithm for discovering from scratch the suite of high-level "capabilities" that an AI system with arbitrary internal planning algorithms/policies can perform. It computes conditions describing the applicability and effects of these capabilities in user-interpretable terms. Starting from a set of user-interpretable state properties, an AI agent, and a simulator that the agent can interact with, our algorithm returns a set of high-level capabilities with their parameterized descriptions. Empirical evaluation on several game-based scenarios shows that this approach efficiently learns descriptions of various types of AI agents in deterministic, fully observable settings. User studies show that such descriptions are easier to understand and reason with than the agent's primitive actions.

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AAMAS Conference 2022 Conference Paper

JEDAI: A System for Skill-Aligned Explainable Robot Planning

Naman Shah
Pulkit Verma
Trevor Angle
Siddharth Srivastava

This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated task and motion planning and explainable AI. JEDAI helps users create high-level, intuitive plans while ensuring that they will be executable by the robot. It also provides users customized explanations about errors and helps improve their understanding of AI planning as well as the limits and capabilities of the underlying robot system.

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AAAI Conference 2021 Conference Paper

Asking the Right Questions: Learning Interpretable Action Models Through Query Answering

Pulkit Verma
Shashank Rao Marpally
Siddharth Srivastava

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent’s internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.

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IJCAI Conference 2021 Conference Paper

Data Efficient Algorithms and Interpretability Requirements for Personalized Assessment of Taskable AI Systems

Pulkit Verma

The vast diversity of internal designs of black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop algorithms and requirements of interpretability that would enable a user to assess and understand the limits of an AI system's safe operability. We develop an assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system in stationary, fully observable, and deterministic settings.

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