Research

Generalized Planning

"Be wise, generalize!"

Planning is well known to be a hard problem. We are developing methods for acquiring useful knowledge while computing plans for small problem instances. This knowledge is then used to aid planning in larger, more difficult problems.

Often, our approaches can extract algorithmic, generalized plans that solve efficiently large classes of similar problems as well as problems with uncertainty in the quantities of objects that the agent needs to work with. The generalized plans we compute are easier to understand and are generated with proofs of correctness.

PR2 does the laundry using a generalized planner with our integrated task and motion planning system.

Papers:

Planning and Reasoning Under Uncertainty

A real robot never has perfect sensors or actuators. Instead, an intelligent robot needs to be able to solve the tasks assigned to it while handling uncertainty about the environment as well as about the effects of its own actions. This is a challenging computational problem, but also one that humans solve on a routine basis (we don't have perfect sensors or actuators either!).

We are developing new methods for efficiently expressing and solving problems where the agent has limited, incomplete information about the quantities and identities of the objects that it may encounter.

Open-universe tiger POMDP with an unknown number of moving tigers.

Papers:

JEDAI: An Educational System for AI Planning and Reasoning

The objective of this project is to introduce AI planning concepts using mobile manipulator robots. It uses a visual programming interface to make these concepts easier to grasp. Users can get the robot to accomplish desired tasks by dynamically populating puzzle shaped blocks encoding the robot's possible actions. This allows users to carry out navigation, planning and manipulation by connecting blocks instead of writing code. AI explanation techniques are used to inform a user if their plan to achieve a particular goal fails. This helps them better grasp the fundamentals of AI planning.

Get JEDAI

The JEDAI system (JEDAI Explains Decision-Making Artificial Intelligence) in action.

Papers:

Autonomous Agents That Are Easy to Understand and Safe to Work With

How would a non-expert assess what their AI system can or can’t do safely? Today’s AI systems require experts to evaluate them, which limits the deployability and safe usability of AI systems.

We are developing approaches for autonomous, user-driven assessment of the capabilities of black-box taskable AI systems, even as the AI systems learn and adapt. These methods would enable users to continually evaluate and understand their AI systems in their own idiosyncratic deployments. They would prevent performance failures and accidents that can arise when AI systems are used beyond their dynamic envelopes of safe applicability. We are also designing approaches for computing user-aligned explanations of AI behavior. Together, these approaches improve the safety and usability of AI systems and enable autonomous, on-the-fly training paradigms for AI systems.

Examples of capability estimation and explanation.

Papers:

Synthesis and Analysis of Abstractions for Autonomy

In order to solve complex, long-horizon tasks such as doing the laundry, a robot needs to compute high-level strategies (e.g., would it be useful to put all the dirty clothes in a basket first?) as well as the joint movements that it should execute. Unfortunately, approaches for high-level planning rely on task-planning abstractions that are lossy and can produce “solutions” that have no feasible executions.

We are developing new methods for computing safe task-planning abstractions and for dynamically refining the task-planning abstraction to produce combined task and motion plans that are guaranteed to be executable. We are also working on utilizing abstractions in sequential decision making (SDM) for evaluating the effect of abstractions on models for SDM, as well as to search for abstractions that would aid in solving a given SDM problem.

Critical region (green) labelling for one sample environment. These regions can be used as waypoint abstractions.

YuMi robot builds a 3π structure with Keva planks using our STAMP algorithm.

Papers:

News

Learning abstractions for RL in non-image-based tasks is hard.

We were surprised to find that learning a conditional abstraction tree (CAT) while doing RL can improve sample efficiency to the point where vanilla Q-learning can outperform SOTA RL methods. #UAI2023
🧵.../1 pic.twitter.com/v1RroUtPZy
— Siddharth Srivastava (@sidsrivast) June 15, 2023

Happy to share a new class of algorithms for generalized planning: https://t.co/wOLOj2F8sH
The paper addresses a key problem in computing general plans/policies: will a given generalized plan terminate/reach good states? This is unsolvable in general due to the halting problem. pic.twitter.com/aUXn2MfBNi
— Siddharth Srivastava (@sidsrivast) December 8, 2022

Our #NeurIPS2022 paper develops a new approach for few-shot learning of generalized policy automata (GPA) for relational stochastic shortest path planning problems.

The learned GPAs can be used to transfer learning and accelerate SSP solvers on much larger problem instances! pic.twitter.com/dRp5sZNeps
— Siddharth Srivastava (@sidsrivast) November 23, 2022

We’d like AI systems to continually learn and adapt, but how would a user figure out what their black-box AI (BBAI) system can safely do at any point?
This is difficult especially when the user and the BBAI use different representations. Our #KR2022 work addresses this problem. pic.twitter.com/0hzhk7QNZ0
— Siddharth Srivastava (@sidsrivast) July 31, 2022

Reliable planning and learning in problems without image-based state representations remains challenging. In our #IJCAI2022 work we found that doing an abstraction before learning results in generalized Q functions and zero-shot transfer to much larger problems!
w/ @KariaRushang pic.twitter.com/ksFt7FWksv
— Siddharth Srivastava (@sidsrivast) July 20, 2022

Consider submitting your recent work on generalization/transfer in all forms of planning and sequential decision making! Due ~today, May 20th in NeurIPS or IJCAI format https://t.co/1DHcFEcFIt
— Siddharth Srivastava (@sidsrivast) May 20, 2022

Congratulations to @shah_naman, @pulkit_verma, Trevor Angle and the entire dev team for creating JEDAI and winning the Best Demo Award @aamas2022! 🎉
JEDAI (JEDAI explains decision-making AI) is an interactive learning tool for AI+robotics that provides explanations autonomously.
— Siddharth Srivastava (@sidsrivast) May 14, 2022

Task and motion planning (TAMP) captures the essence of what we want robots to be able to do in a range of settings. But cobots need to communicate with humans to avoid potential conflicts. Our #ICRA2022 work develops a unified framework for integrating communication with TAMP. pic.twitter.com/ObVDkWSZtZ
— Siddharth Srivastava (@sidsrivast) April 21, 2022

Consider submitting a piece about your work on representations for generalization and transfer in all forms of AI planning/sdm! Links below. https://t.co/ogoNFMzvQo
— Siddharth Srivastava (@sidsrivast) April 19, 2022

We use hand-coded state and action abstractions extensively in AI planning. Where do these abstractions come from? Our #aamas2022 paper develops methods for learning such abstractions from scratch and for using them to solve robot planning problems efficiently and reliably.🗝️🥡👇 pic.twitter.com/lp3U8rpK6R
— Siddharth Srivastava (@sidsrivast) March 5, 2022

Can we assess what Black-Box #AI systems can and can’t do reliably as they change/adapt to changing situations?

In our #AAAI2022 paper, @pulkit_verma and @rashmeet_nayyar (= contributors) address this with the foundations for efficient /differential/ assessment of AI systems. pic.twitter.com/5xmOmSS8XL
— Siddharth Srivastava (@sidsrivast) December 3, 2021

Excited to be a part of the workshop on Generalization in Planning (GenPlan) at IJCAI '21!

We welcome current/recent work on the synthesis or learning of plans and policies with an emphasis on generalizability and transfer.

Submission deadline: May 9.https://t.co/bfwxUGXXqm
— Siddharth Srivastava (@sidsrivast) April 19, 2021

Congratulations to @pulkit_verma and Rushang Karia for their first first-authored papers! Pulkit’s work investigates how users may assess the limits and capabilities of their AI systems while Rushang’s develops self-training algorithms for speeding up AI planning. #aaai2021 (1/2)
— Siddharth Srivastava (@sidsrivast) December 16, 2020

Imagine a robot doing your laundry or making a cup of tea for you. #ASUEngineering @CIDSEASU assistant professor @sidsrivast is working to equip #artificialintelligence with the capability to do real-world tasks. #AI https://t.co/RrcfGUP0wr
— ASU Ira A. Fulton Schools of Engineering (@ASUEngineering) August 11, 2020

Can we use #DeepLearning to speed up robot planning while maintaining theoretical guarantees (correctness, probabilistic completeness)? Dan and Kislay's work indicates YES, but with a few changes to planning algorithms. Check it out at https://t.co/tu7TSRcwBT and @icra20 online! pic.twitter.com/fQQ13qyzwJ
— Siddharth Srivastava (@sidsrivast) May 31, 2020

Congratulations to Rashmeet for winning the Chambliss Medal for her research project on using #AI for reliable inference about intergalactic space! @asunow article featuring this interdisciplinary #ASUEngineering work: https://t.co/bkMLSxT5Wo
— Siddharth Srivastava (@sidsrivast) May 27, 2020

Our new “anytime” algorithm for stochastic task and motion planning computes better robot policies as it gets more time. Our YuMi uses this to autonomously build Keva structures! @icra2020
Personal favorite: https://t.co/BPti3sJp1E
Paper + videos: https://t.co/lsDf7So4gl pic.twitter.com/zWLCZlL71h
— Siddharth Srivastava (@sidsrivast) March 20, 2020

Congrats to Daniel, Naman, Kislay, Deepak & Pranav for their first papers, accepted at #ICRA2020! Their #NSF_CISE funded #AI research shows how to compute reliable robot plans more efficiently using (1)abstractions & (2)#DeepLearning. Camera-readies coming soon! #ICRA #NSFfunded
— Siddharth Srivastava (@sidsrivast) January 23, 2020

How can we train people to use adaptive #AI systems, whose behavior and functionality is expected to change from day to day? Our approach: make the AI system self-explaining! #ASUFoW https://t.co/VwFs30sJU2
— Siddharth Srivastava (@sidsrivast) September 14, 2019

Had a great conversation about #AI with Rami Kalla @pointintimes!

Audio podcast: https://t.co/xYeMtJHhzd

Video linked in the original post. https://t.co/swC01xIhii
— Siddharth Srivastava (@sidsrivast) August 7, 2019

As anyone who has talked to a 3yo knows, explaining why something can’t be done can be much harder than explaining a solution. Can #AI systems explain why they failed to solve a given problem? Sarath's new work takes first steps in explaining unsolvability https://t.co/lQuRSeKc4b
— Siddharth Srivastava (@sidsrivast) March 27, 2019

Dan and Kislay's new work on motion planning tries to get the best of both worlds: the learn and link planner (LLP) learns and improves with experience. It is also sound and probabilistically complete. Check out the results here: https://t.co/v21OfXJgtB
— Siddharth Srivastava (@sidsrivast) March 11, 2019

Our new work aims at allowing designers and users to choose whether their AI systems clarify, or protect information.

A Unified Framework for Planning in Adversarial and Cooperative Environments. Kulkarni et al. https://t.co/7ibzWE7KwC #AAAI2019 https://t.co/BQZW0umryL
— AAIR Lab (@AAIRLabASU) February 5, 2019

Alfred says Hello!! https://t.co/08H96AoUMq
— AAIR Lab (@AAIRLabASU) January 29, 2019

Name	Course	Graduated	Current Position
Kyle Joseph Atkinson	B.S.	Jul 2022	Graduate Student, Arizona State University
Kiran Prasad	M.S.	Jul 2022	Software Developer, Amazon Robotics
Shashank Rao Marpally	M.S.	May 2021	PhD Student, National University of Singapore
Deepak Kala Vasudevan	M.S.	Dec 2020	Software Developer, Amazon Robotics
Abhyudaya Srinet	M.S.	Aug 2020	CTO, MentR-Me and MiM-Essay.com
Kislay Kumar	M.S.	Dec 2019	Software Developer, Amazon Robotics AI
Chirav Dave	M.S.	Dec 2019	Technical Lead, A10 Networks, Inc.
Daniel Molina	M.S.	May 2019	Machine Learning Engineer, State Farm
Midhun P. M.	M.C.S.	Dec 2018	Staff Software Engineer, Bazaarvoice
Julia Nakhleh	B.S.	May 2019	Graduate Student, University of Wisconsin-Madison
Ryan Christensen	B.S.	May 2018	Data Engineer, Tellic
Perry Wang	B.S.	May 2018

Publications

Show by area

Public Code Repository

Conditional Abstraction Trees for Sample-Efficient Reinforcement Learning.
Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava.
In Proceedings of UAI, 2023.
Learning State/Action Abstractions

Hierarchical Decompositions and Termination Analysis for Generalized Planning.
Siddharth Srivastava.
Journal of Artificial Intelligence Research (JAIR), 77 (2023) 1223-1256.
(An earlier version appeared as ArXiv:2212.02823, 2022)
Generalized Planning Plan Generalization and Transfer State/Action Abstractions

Autonomous Capability Assessment of Black-Box Sequential Decision-Making Systems.
Pulkit Verma, Rushang Karia, Siddharth Srivastava.
ICAPS 2023 Workshop on Knowledge Engineering for Planning and Scheduling, 2023.
AI Assessment State/Action Abstractions Assistive Planning Generalized Planning

Multi-Task Option Learning and Discovery for Stochastic Path Planning.
Naman Shah, Siddharth Srivastava.
ArXiv: 2210.00068, 2022. (In submission)
State/Action Abstractions Learning Plan Generalization and Transfer Mobile Manipulation Neuro-Symbolic AI

Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems.
Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava.
In Proceedings of NeurIPS, 2022.
Learning State/Action Abstractions Plan Generalization and Transfer Generalized Planning

Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems.
Rushang Karia, Siddharth Srivastava.
In Proceedings of IJCAI, 2022.
Learning State/Action Abstractions Plan Generalization and Transfer Generalized Planning Neuro-Symbolic AI

Discovering User-Interpretable Capabilities of Black-Box Planning Agents. [Extended version]
Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava.
In Proceedings of KR, 2022.
AI Assessment State/Action Abstractions Assistive Planning Generalized Planning

Joint Communication and Motion Planning for Cobots. [Extended version]
Mehdi Dadvar, Keyvan Majd, Elena Oikonomou, Georgios Fainekos, Siddharth Srivastava.
In Proceedings of ICRA, 2022.
Assistive Planning Partial Observability

Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations.
Sarath Sreedharan, Utkarsh Soni, Mudit Verma, Siddharth Srivastava, Subbarao Kambhampati.
In Proceedings of ICLR, 2022.
State/Action Abstractions Assistive Planning

Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning. [Extended version]
Naman Shah, Siddharth Srivastava.
In Proceedings of AAMAS, 2022.
State/Action Abstractions Learning Plan Generalization and Transfer Mobile Manipulation Neuro-Symbolic AI

JEDAI: A System for Skill-Aligned Explainable Robot Planning (Demonstration Track). [Winner of Best Demo Award]
Naman Shah^*, Pulkit Verma^*, Trevor Angle, Siddharth Srivastava.
In Proceedings of AAMAS, 2022.
Assistive Planning Mobile Manipulation

Differential Assessment of Black-Box AI Agents. [Extended version]
Rashmeet Kaur Nayyar^*, Pulkit Verma^*, Siddharth Srivastava.
In Proceedings of AAAI, 2022.
AI Assessment State/Action Abstractions Assistive Planning

An Anytime Hierarchical Approach for Stochastic Task and Motion Planning.
Naman Shah, Siddharth Srivastava.
ArXiv: 2108.12537, 2022. (In submission)
State/Action Abstraction Mobile Manipulation

Using State Abstractions to Compute Personalized Contrastive Explanations for AI Agent Behavior.
Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati.
Artificial Intelligence (AIJ), Vol. 301, Article 103570, 2021.
Assistive Planning State/Action Abstractions

Empirical Analysis of Abstractions for Learning Generalized Heuristic Networks.
Rushang Karia, Siddharth Srivastava.
IJCAI 2021 Workshop on Generalization in Planning, 2021.
Generalized Planning Learning State/Action Abstractions Neuro-Symbolic AI

Learning Causal Models of Autonomous Agents using Interventions.
Pulkit Verma, Siddharth Srivastava.
IJCAI 2021 Workshop on Generalization in Planning, 2021.
AI Assessment State/Action Abstractions Assistive Planning Generalized Planning

Planning for Proactive Assistance in Environments with Partial Observability.
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati.
ICAPS 2021 Workshop on Explainable AI Planning, 2021.
Assistive Planning Partial Observability

Learning and Using Abstractions for Robot Planning.
Naman Shah, Abhyudaya Srinet, Siddharth Srivastava.
ICAPS 2021 Workshop on Planning and Robotics, 2021.
State/Action Abstractions Learning Plan Generalization and Transfer Mobile Manipulation Neuro-Symbolic AI

Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning.
Rushang Karia, Siddharth Srivastava.
In Proceedings of AAAI, 2021.
Generalized Planning Learning State/Action Abstractions Neuro-Symbolic AI

Unifying Principles and Metrics for Safe and Assistive AI (Senior Member Track).
Siddharth Srivastava.
In Proceedings of AAAI, 2021.
AI Assessment Assistive Planning Learning Plan Generalization and Transfer

Asking the Right Questions: Learning Interpretable Action Models Through Query Answering. [Extended version]
Pulkit Verma, Shashank Rao Marpally, Siddharth Srivastava.
In Proceedings of AAAI, 2021.
AI Assessment State/Action Abstractions Assistive Planning Generalized Planning

Anytime Task and Motion Policies for Stochastic Environments.
Project Page
Naman Shah, Deepak Kala Vasudevan, Kislay Kumar, Pranav Kamojhalla, Siddharth Srivastava.
In Proceedings of ICRA, 2020.
State/Action Abstractions Mobile Manipulation

Learn and Link: Learning Critical Regions for Efficient Planning.
Project Page
Daniel Molina, Kislay Kumar, Siddharth Srivastava.
In Proceedings of ICRA, 2020.
State/Action Abstractions Learning Plan Generalization and Transfer Mobile Manipulation Neuro-Symbolic AI

TLdR: Policy Summarization for Factored SSP Problems Using Temporal Abstractions.
Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati.
In Proceedings of ICAPS, 2020.
State/Action Abstractions Assistive Planning

Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility (extended abstract).
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati.
In Proceedings of AAMAS, 2020.
Assistive Planning Partial Observability

Learning Generalized Models by Interrogating Black-Box Autonomous Agents.
Pulkit Verma, Siddharth Srivastava.
AAAI Workshop on Generalization in Planning, 2020.
AI Assessment State/Action Abstractions Assistive Planning Generalized Planning

Why Can't You Do That HAL? Explaining Unsolvability of Planning Tasks.
Sarath Sreedharan, Siddharth Srivastava, David Smith, Subbarao Kambhampati.
In Proceedings of IJCAI, 2019.
Assistive Planning State/Action Abstractions

Anytime Integrated Task and Motion Policies for Stochastic Environments.
Naman Shah, Siddharth Srivastava.
ICAPS 2019 Workshop on Planning and Robotics, 2019.
Mobile Manipulation State/Action Abstractions

Learning Critical Regions for Robot Planning Using Convolutional Neural Networks.
Daniel Molina, Kislay Kumar, Siddharth Srivastava.
ICAPS 2019 Workshop on Planning and Robotics, 2019.
Learning Plan Generalization and Transfer State/Action Abstractions Neuro-Symbolic AI

Automated Physics-based Detection and Identification of Intergalactic Clouds using Probabilistic Programming. [Poster] [Winner of Chambliss Medal]
Rashmeet Kaur Nayyar, Mansi Padave, Sanchayeeta Borthakur, Siddharth Srivastava.
In 234th Meeting of American Astronomical Society. Bulletin of the AAS, Vol. 51, No. 4, id. 203.05, 2019.
Probabilistic Inference Partial Observability

A Unified Framework for Planning in Adversarial and Cooperative Environments.
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati.
In Proceedings of AAAI, 2019.
Assistive Planning Partial Observability

Platform-Independent Benchmarks for Task and Motion Planning.
Fabien Lagriffoul, Neil T. Dantam, Caelan Garrett, Aliakbar Akbari, Siddharth Srivastava, Lydia E. Kavraki.
IEEE Robotics and Automation Letters (RA-L), Vol. 3, Issue 4, pp. 3765-3772, 2018.
Mobile Manipulation State/Action Abstractions

Discrete-Continuous Mixtures in Probabilistic Programming: Extended Semantics and General Inference Algorithms.
Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell.
In Proceedings of ICML, 2018.
Probabilistic Inference Learning

Hierarchical Expertise Level Modeling for User-Specific Contrastive Explanations. [Extended version]
Sarath Sreedharan, Siddharth Srivastava, Subbarao Kambhampati.
In Proceedings of IJCAI, 2018.
Assistive Planning State/Action Abstractions

Learning Generalized Reactive Policies using Deep Neural Networks.
Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivatava, Pieter Abbeel.
In Proceedings of ICAPS, 2018.
Older version, AAAI Spring Symposium on Integrating Representation, Reasoning, Learning and Execution (SIRLE '18).
Plan Generalization and Transfer Learning Neuro-Symbolic AI

Safe Goal-Directed Autonomy and the Need for Sound Abstractions.
Siddharth Srivastava.
In Proceedings of AAAI Spring Symposium on Integrating Representation, Reasoning, Learning and Execution (SIRLE '18).
State/Action Abstractions

Hierarchical Strategy Synthesis for Pursuit-Evasion Problems.
Rattanachai Ramaithitima, Siddharth Srivastava, Subhrajit Bhattacharya, Alberto Speranzon, Vijay Kumar.
In Proceedings of ECAI, 2016.
State/Action Abstractions Partial Observability

Markovian State and Action Abstractions for MDPs via Hierarchical MCTS.
Aijun Bai, Siddharth Srivastava, Stuart Russell.
In Proceedings of IJCAI, 2016.
State/Action Abstractions Partial Observability

Guided Search for Task and Motion Plans Using Learned Heuristics.
Rohan Chitnis, Dylan Hadfield-Menell, Abhishek Gupta, Siddharth Srivastava, Edward Groshev, Christopher Lin, Pieter Abbeel.
In Proceedings of ICRA, 2016.
Mobile Manipulation Learning

Metaphysics of Planning Domain Descriptions.
Siddharth Srivastava, Stuart Russell, Alessandro Pinto.
In Proceedings of AAAI, 2016.
State/Action Abstractions

Tractability of Planning with Loops.
Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell.
In Proceedings of AAAI, 2015.
Partial Observability Learning State/Action Abstractions Generalized Planning Plan Generalization and Transfer

First-Order Open-Universe POMDPs.
Siddharth Srivastava, Stuart Russell, Paul Ruan, Xiang Cheng.
In Proceedings of UAI, 2014.
Partial Observability Probabilistic Inference

Combined Task and Motion Planning Through an Extensible Planner-Independent Interface Layer.
Videos of experiments (PR2 + sim)
Siddharth Srivastava, Eugene Fang, Lorenzo Riano, Rohan Chitnis, Stuart Russell, Pieter Abbeel.
In Proceedings of ICRA, 2014.
Mobile Manipulation State/Action Abstractions

Using Classical Planners for Tasks with Continuous Operators in Robotics.
Siddharth Srivastava, Lorenzo Riano, Stuart Russell, Pieter Abbeel.
ICAPS Workshop on Planning and Robotics, 2013.
Mobile Manipulation

First-Order Open-Universe POMDPs: Formulation and Algorithms.
Siddharth Srivastava, Xiang Cheng, Stuart Russell, Avi Pfeffer
Technical Report EECS-2013-243, EECS Department, UC Berkeley, 2013.
Partial Observability Probabilistic Inference

Applicability Conditions for Plans with Loops: Computability Results and Algorithms.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
Artificial Intelligence (AIJ), Vol. 191-192, pp. 1-19, 2012.
Generalized Planning Plan Generalization and Transfer State/Action Abstractions

Qualitative Numeric Planning.
Siddharth Srivastava, Shlomo Zilberstein, Neil Immerman, Hector Geffner.
In Proceedings of AAAI, 2011.
Partial Observability Generalized Planning State/Action Abstractions Plan Generalization and Transfer

Termination and Correctness Analysis of Cyclic Control.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
In Proceedings of AAAI, 2011.
Partial Observability Generalized Planning State/Action Abstractions Plan Generalization and Transfer

Directed Search for Generalized Plans Using Classical Planners. [Video]
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein, Tianjiao (Cathy) Zhang.
In Proceedings of ICAPS, 2011.
Plan Generalization and Transfer Generalized Planning State/Action Abstractions

Foundations and Applications of Generalized Planning.
Siddharth Srivastava.
AI Communications, Vol. 24, pp. 349-351, 2011.
This article summarizes the contributions of Dr. Srivastava's thesis.
Plan Generalization and Transfer Generalized Planning State/Action Abstractions

A New Representation and Associated Algorithms for Generalized Planning.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
Artificial Intelligence (AIJ), Vol. 175, Issue 2, pp. 615-647, 2011.
Plan Generalization and Transfer State/Action Abstractions Generalized Planning

Computing Applicability Conditions for Plans with Loops. [Extended version] [Winner of Best Paper Award]
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
In Proceedings of ICAPS, 2010.
Generalized Planning State/Action Abstractions Plan Generalization and Transfer

Foundations and Applications of Generalized Planning (readable version).
Siddharth Srivastava.
Doctoral Dissertation, Department of Computer Science, University of Massachusetts Amherst, 2010. [Summary]
Plan Generalization and Transfer Generalized Planning State/Action Abstractions

Merging Example Plans into Generalized Plans for Non-deterministic Environments.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
In Proceedings of AAMAS, 2010.
Plan Generalization and Transfer Generalized Planning State/Action Abstractions

Abstract Planning with Unknown Object Quantities and Properties.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
Eighth Symposium on Abstraction, Reformulation and Approximation (SARA), 2009. [slides]
Plan Generalization and Transfer Generalized Planning State/Action Abstractions

Simulating Reachability using First-Order Logic with Applications to Verification of Linked Data Structures.
T. Lev-Ami, N. Immerman, T. Reps, M. Sagiv, S. Srivastava and G. Yorsh.
Logical Methods in Computer Science, 5(2), Paper 12, 2009.
State/Action Abstractions

Finding Plans with Branches, Loops and Preconditions.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
ICAPS 2009 Workshop on Verification and Validation of Planning and Scheduling Systems, 2009. [slides]
Generalized Planning State/Action Abstractions Plan Generalization and Transfer

Challenges in Finding Generalized Plans.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
ICAPS 2009 Workshop on Generalized Planning: Macros, Loops, Domain Control, 2009. [slides]
Generalized Planning State/Action Abstractions Plan Generalization and Transfer

Learning Generalized Plans Using Abstract Counting.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
In Proceedings of AAAI, 2008. [slides]
State/Action Abstractions Plan Generalization and Transfer Generalized Planning

Using Abstraction for Generalized Planning.
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
International Symposium on AI and Mathematics (ISAIM), 2008.
State/Action Abstractions Plan Generalization and Transfer Generalized Planning

Using Abstraction for Generalized Planning (Extended Version).
Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein.
Technical Report 07-41, Deptt. of Computer Science, University of Massachusetts, Amherst, 2007.
State/Action Abstractions Plan Generalization and Transfer Generalized Planning

Simulating Reachability Using First-Order Logic With Applications to Verification of Linked Data Structures.
T. Lev-Ami, N. Immerman, T. Reps, M. Sagiv, S. Srivastava and G. Yorsh.
International Conference on Automated Deduction, 2005.

State/Action Abstractions

Research

Generalized Planning

Planning and Reasoning Under Uncertainty

JEDAI: An Educational System for AI Planning and Reasoning

Get JEDAI

Autonomous Agents That Are Easy to Understand and Safe to Work With

Synthesis and Analysis of Abstractions for Autonomy

News

People

Siddharth Srivastava

Daniel Bramblett

Mehdi Dadvar

Rushang Karia

Rashmeet K Nayyar

Naman P Shah

Pulkit Verma

Dylan Fulop

Jayesh Nagpal

Alfred

HoShi-R

YuMi

Alumni

Spring 2023

Publications

Public Code Repository

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