53 citations · 232 across the 44 of their papers we have counts for
18 papers · 1 filter
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)
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 sequent…
Hierarchical Decomposition and Analysis for Generalized Planning
Siddharth Srivastava
This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of general…
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava
Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path Problems (SSPs). However, the computational complexity of solving SSPs makes…
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…
JEDAI: A System for Skill-Aligned Explainable Robot Planning
Naman Shah, Pulkit Verma, Trevor Angle +1
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 ta…
Learning Causal Models of Autonomous Agents using Interventions
Pulkit Verma, Siddharth Srivastava
One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavio…
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…
Planning for Proactive Assistance in Environments with Partial Observability
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
This paper addresses the problem of synthesizing the behavior of an AI agent that provides proactive task assistance to a human in settings like factory floors where they may coexi…
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Sarath Sreedharan, Utkarsh Soni, Mudit Verma +2
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allo…
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 paradig…
Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities. In this paper, we consider…
Anytime Integrated Task and Motion Policies for Stochastic Environments
Naman Shah, Deepak Kala Vasudevan, Kislay Kumar +2
In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstra…
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice
Sarath Sreedharan, Siddharth Srivastava, David Smith +1
Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations…
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Yi Wu, Siddharth Srivastava, Nicholas Hay +2
Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine disc…
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations
Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati
There is a growing interest within the AI research community to develop autonomous systems capable of explaining their behavior to users. One aspect of the explanation generation p…
An Anytime Algorithm for Task and Motion MDPs
Siddharth Srivastava, Nishant Desai, Richard Freedman +1
Integrated task and motion planning has emerged as a challenging problem in sequential decision making, where a robot needs to compute high-level strategy and low-level motion plan…
A Unified Framework for Planning in Adversarial and Cooperative Environments
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial…
Learning Generalized Reactive Policies using Deep Neural Networks
Edward Groshev, Maxwell Goldstein, Aviv Tamar +2
We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem insta…