# Publications

1. KR
##### Discovering User-Interpretable Capabilities of Black-Box Planning Agents.

In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022. (To appear)

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 has received little research attention. This is aggravated by the fact that users may not know which questions to ask in order to understand the limits and capabilities of a system. 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 relational state properties, an AI agent, and a simulator that the agent can interact with, using arbitrary decision-making paradigms over primitive operations (unknown to the user), our algorithm returns a set of high-level capabilities with capability descriptions in the user’s relational vocabulary. Empirical evaluation on several game-based scenarios shows that this approach efficiently learns interpretable descriptions of various types of AI agents in deterministic, fully observable settings. User studies show that such interpretable descriptions are easier to understand and reason with than the agent’s primitive actions.
@inproceedings{verma2022discovering,
author    = {Verma, Pulkit and Marpally, Shashank Rao and Srivastava, Siddharth},
title     = {Discovering User-Interpretable Capabilities of Black-Box Planning Agents},
booktitle = {Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning},
year      = {2022},
}
Older Version(s):

Discovering User-Interpretable Capabilities of Black-Box Planning Agents.
Pulkit Verma, Shashank Rao Marpally, and Siddharth Srivastava.
In AAAI 2022 Workshop on Explainable Agency in Artificial Intelligence, 2022.

Learning User-Interpretable Descriptions of Black-Box AI System Capabilities.
Pulkit Verma, Shashank Rao Marpally, and Siddharth Srivastava.
In ICAPS 2021 Workshop on Knowledge Engineering for Planning and Scheduling.

2. AAMAS
##### JEDAI: A System for Skill-Aligned Explainable Robot Planning.

In Proceedings of the Twenty-First International Conference on Autonomous Agents and MultiAgent Systems (Demonstration Track), 2022.
[Best Demonstration Award]

*Equal Contribution. Alphabetical order.

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.
@misc{shah2022jedai,
author        = {Naman Shah and Pulkit Verma and Trevor Angle and Siddharth Srivastava},
title         = { {JEDAI}: {A} System for Skill-Aligned Explainable Robot Planning},
booktitle     = {Proceedings of the Twenty-First International Conference on Autonomous Agents and MultiAgent Systems},
year          = {2022},
}
3. AAAI
##### Differential Assessment of Black-Box AI Agents.

In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022.
[Also appeared in AAAI 2022 Workshop on Artificial Intelligence Safety]

*Equal Contribution. Alphabetical order.

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.
@inproceedings{nayyar2022differential,
author    = {Nayyar, Rashmeet Kaur and Verma, Pulkit and Srivastava, Siddharth},
title     = {Differential Assessment of Black-Box AI Agents},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
year      = {2022},
}
4. Preprint
##### Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks.

In submission, 2022.

*Equal Contribution. Alphabetical order.

How can we measure the generalization of models to a variety of unseen tasks when provided with their language instructions? To facilitate progress in this goal, we introduce Natural-Instructions v2, a collection of 1,600+ diverse language tasks and their expert written instructions. More importantly, the benchmark covers 70+ distinct task types, such as tagging, in-filling, and rewriting. This benchmark is collected with contributions of NLP practitioners in the community and through an iterative peer review process to ensure their quality. This benchmark enables large-scale evaluation of cross-task generalization of the models – training on a subset of tasks and evaluating on the remaining unseen ones. For instance, we are able to rigorously quantify generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances, and model sizes. As a by-product of these experiments. we introduce Tk-Instruct, an encoder-decoder Transformer that is trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples) which outperforms existing larger models on our benchmark. We hope this benchmark facilitates future progress toward more general-purpose language understanding models.
@misc{wang2022benchmarking,
author        = {Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
title         = {Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks},
eprint        = {2204.07705},
year          = {2022},
archivePrefix = {arXiv},
primaryClass  = {cs.CL}
}

### 2022

1. GenPlan
##### Learning Causal Models of Autonomous Agents using Interventions.

In IJCAI 2021 Workshop on Generalization in Planning, 2021.

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 behavior of such systems. We extend the analysis of an agent 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. We show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable causal model of the system in stationary, fully observable, and deterministic settings. We also introduce dynamic causal decision networks (DCDNs) that capture the causal structure of STRIPS-like domains. A comparative analysis of different classes of queries is also presented in terms of the computational requirements needed to answer them and the efforts required to evaluate their responses to learn the correct model.
@inproceedings{verma2021learningcausal,
author    = {Verma, Pulkit and Srivastava, Siddharth},
title     = {Learning Causal Models of Autonomous Agents using Interventions},
booktitle = {IJCAI 2021 Workshop on Generalization in Planning},
year      = {2021},
}
2. AAAI
##### Asking the Right Questions: Learning Interpretable Action Models Through Query Answering.

In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021.

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 minimal query interface with the agent, and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent’s internal model in a vocabulary provided by the user. 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.
@inproceedings{verma2021asking,
author        = {Verma, Pulkit and Marpally, Shashank Rao and Srivastava, Siddharth},
title         = {Asking the Right Questions: Learning Interpretable Action Models Through Query Answering},
year          = {2021},
booktitle     = {Proceedings of the AAAI Conference on Artificial Intelligence},
}
Older Version(s):

Asking the Right Questions: Active Action-Model Learning.
Pulkit Verma, Shashank Rao Marpally, and Siddharth Srivastava.
In AAAI 2021 Workshop on Explainable Agency in Artificial Intelligence, 2021.

Learning Interpretable Models for Black-Box Agents.
Pulkit Verma, and Siddharth Srivastava.
In ICML 2020 Workshop on Human in the Loop Learning, 2020.

Learning Generalized Models by Interrogating Black-Box Autonomous Agents.
Pulkit Verma, and Siddharth Srivastava.
In AAAI 2020 Workshop on Generalization in Planning, 2020.

### 2021

1. ICSC
##### A Comparative Study of Resource Usage for Speaker Recognition Techniques.

In Proceedings of the 2016 International Conference on Signal Processing and Communication, 2016.

Resource usage of a software is an important factor to be taken into consideration while developing speaker recognition applications for mobile devices. Sometimes usage parameters are considered as important as accuracy of such systems. In this work, we analyze resource utilization in terms of power consumption, memory and space requirements of three standard speaker recognition techniques, viz. GMM-UBM framework, Joint Factor Analysis and i-vectors. Experiments are performed on the MIT MDSVC corpus using the Energy Measurement Library (EML). It is found that though i-vector approach requires more storage space, it is superior to the other two approaches in terms of memory and power consumption, which are critical factors for evaluating software performance in resource constrained mobile devices.
@inproceedings{verma2016comparative,
author    = {Verma, Pulkit and Das, Pradip K},
title     = {A Comparative Study of Resource Usage for Speaker Recognition Techniques},
booktitle = {Proceedings of the 2016 International Conference on Signal Processing and Communication},
pages     = {314–319},
year      = {2016},
publisher = {IEEE},
doi       = {10.1109/ICSPCom.2016.7980598},
url       = {https://doi.org/10.1109/ICSPCom.2016.7980598},
}

### 2016

1. IJST
##### i-Vectors in Speech Processing Applications: A Survey.

In International Journal of Speech Technology, 2015.

In the domain of speech recognition many methods have been proposed over time like Gaussian mixture models (GMM), GMM with universal background model (GMM-UBM framework), joint factor analysis, etc. i-Vector subspace modeling is one of the recent methods that has become the state of the art technique in this domain. This method largely provides the benefit of modeling both the intra-domain and inter-domain variabilities into the same low dimensional space. In this survey, we present a comprehensive collection of research work related to i-vectors since its inception. Some recent trends of using i-vectors in combination with other approaches are also discussed. The application of i-vectors in various fields of speech recognition, viz speaker, language, accent recognition, etc. is also presented. This paper should serve as a good starting point for anyone interested in working with i-vectors for speech processing in general. We then conclude the paper with a brief discussion on the future of i-vectors.
@article{verma2015ivectors,
author    = {Verma, Pulkit and Das, Pradip K},
title     = {i-{Vectors} in Speech Processing Applications: {A Survey}},
journal   = {International Journal of Speech Technology},
year      = {2015},
volume    = {18},
number    = {4},
pages     = {529–546},
publisher = {Springer Nature},
doi       = {10.1007/s10772-015-9295-3},
url       = {https://doi.org/10.1007/s10772-015-9295-3},
}
2. UIST
##### Investigating the “Wisdom of Crowds” at Scale.

In Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, 2015.

*Equal Contribution. Alphabetical order.

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon’s Mechanical Turk.
@inproceedings{mysore2015investigating,
author    = {Shankar Mysore, Alok and Yaligar, Vikas S. and Arrieta Ibarra, Imanol and
Simoiu, Camelia and Goel, Sharad and Arvind, Ramesh and Sumanth, Chiraag and Srikantan, Arvind
and HS, Bhargav and Pahadia, Mayank and Dobha, Tushar and Ahmed, Atif and Shankar, Mani and
Agarwal, Himani and Agarwal, Rajat and Anirudh-Kondaveeti, Sai and Arun-Gokhale, Shashank and
Attri, Aayush and Chandra, Arpita and Chilukur, Yogitha and Dharmaji, Sharath and Garg, Deepak
and Gupta, Naman and Gupta, Paras and Jacob, Glincy Mary and Jain, Siddharth and Joshi,
Shashank and Khajuria, Tarun and Khillan, Sameeksha and Konam, Sandeep and Kumar-Kolla, Praveen
and Loomba, Sahil and Madan, Rachit and Maharaja, Akshansh and Mathur, Vidit and Munshi, Bharat
and Nawazish, Mohammed and Neehar-Kurukunda, Venkata and Nirmal-Gavarraju, Venkat and
Parashar, Sonali and Parikh, Harsh and Paritala, Avinash and Patil, Amit and Phatak, Rahul and
Pradhan, Mandar and Ravichander, Abhilasha and Sangeeth, Krishna and
Sankaranarayanan, Sreecharan and Sehgal, Vibhor and Sheshan, Ashrith and Shibiraj, Suprajha and
Singh, Aditya and Singh, Anjali and Sinha, Prashant and Soni, Pushkin and Thomas, Bipin and
Varma-Dattada, Kasyap and Venkataraman, Sukanya and Verma, Pulkit and Yelurwar, Ishan},
title     = {Investigating the "{Wisdom of Crowds}" at Scale},
booktitle = {Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology},
pages     = {75–76},
year      = {2015},
isbn      = {9781450337809},
publisher = {Association for Computing Machinery},
doi       = {10.1145/2815585.2815725},
url       = {https://doi.org/10.1145/2815585.2815725},
}
3. AIR
##### A Mobile Agents based Distributed Speech Recognition Engine for Controlling Multiple Robots.

In Proceedings of the 2015 Conference on Advances In Robotics, 2015.

Interaction with a robot has been an active area of research since the inception of robotics. Talking to a robot has always been considered the most natural way to communicate with it. But it is not always possible to have a full-fledged, standalone speech processing engine to be present on a robot or on a single machine. A dedicated system to convert the commands from audio to text is needed. However, as the number of commands and robots increases, it becomes necessary to eliminate all the single-point failure points in the system. Thus, distributed speech engine comes into picture. Also users may want to talk to the robot in different languages. The approach proposed in this paper is distributed, fault tolerant and scalable, such that any new recognition algorithm or language support can be added and used without any changes to the existing system. The work has been demonstrated on a freely available mobile agents based Internet of Things platform. However, any platform can be used.
@inproceedings{gupta2015mobile,
author    = {Gupta, Mayank and Verma, Pulkit and Bhattacharya, Tuhin and Das, Pradip K},
title     = {A Mobile Agents based Distributed Speech Recognition Engine for Controlling Multiple Robots},
booktitle = {Proceedings of the 2015 Conference on Advances In Robotics},
pages     = {1–6},
year      = {2015},
isbn      = {9781450333566},
publisher = {Association for Computing Machinery},
doi       = {10.1145/2783449.2783477},
url       = {https://doi.org/10.1145/2783449.2783477},
}

### 2015

1. IC3I
##### Improving Services Using Mobile Agents-based IoT in a Smart City.

In Proceedings of the 2014 International Conference on Contemporary Computing and Informatics, 2014.

Modern-day devices like smart-phones, tablets, televisions etc. possess very powerful processors and huge storage capacities compared to what were available a few years ago. Most of these devices are also connected to the Internet. However, the full capabilities of these devices are not fully harnessed and thus, they are not as intelligent as they could be. These devices, together with the Internet, can be used as “Internet of Things” where each device can be both producer and consumer of information. This framework is realizable in a real dynamic system if there is an intelligent distributed layer above it which can cater to services of all heterogeneous devices as required. The existing solutions to this problem are either too hardware dependent, or too abstract. In this paper we present a concept of this layer using mobile agents which makes the system flexible and dynamically adaptable. This layer has been deployed using a publicly available Prolog-based mobile agent emulator (however, any other mobile agent framework can also be used). The proposed approach is capable of updating information like availability and usability of services dynamically. It also has speech processing modules to provide solutions using voice-based commands and prompts. The prototype is scalable and robust to partial network failures. The implementation details and performance analysis of this work are reported and discussed. This framework can be used to deploy systems which can enable people to search for services like health facilities, food services, transportation, law and order using a common interface including voice commands.
@inproceedings{verma2014improving,
author    = {Verma, Pulkit and Gupta, Mayank and Bhattacharya, Tuhin and Das, Pradip K},
title     = {Improving Services Using Mobile Agents-based {IoT} in a Smart City},
booktitle = {2014 International Conference on Contemporary Computing and Informatics},
pages     = {107–111},
year      = {2014},
publisher = {IEEE},
doi       = {10.1109/IC3I.2014.7019766},
url       = {https://doi.org/10.1109/IC3I.2014.7019766},
}