1. | Kritika Prakash; Fiza Hussain; Praveen Paruchuri; Sujit Gujar: How Private Is Your RL Policy? An Inverse RL Based Analysis Framework. In: 36th AAAI Conference on Artificial Intelligence , AAAI, Forthcoming. (Type: Conference | BibTeX | Tags: differential privacy, inverse reinforcement learning, Reinforcement Learning)@inproceedings{Kritika22,
title = {How Private Is Your RL Policy? An Inverse RL Based Analysis Framework},
author = {Kritika Prakash and Fiza Hussain and Praveen Paruchuri and Sujit Gujar},
year = {2022},
date = {2022-02-22},
booktitle = {36th AAAI Conference on Artificial Intelligence },
publisher = {AAAI},
keywords = {differential privacy, inverse reinforcement learning, Reinforcement Learning},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
2. | Bhavya Kalra; Sai Krishna Munnangi; Kushal Majmundar; Naresh Manwani; Praveen Paruchuri: Cooperative Monitoring of Malicious Activity in Stock Exchanges. Workshop on Data Assessment and Readiness for Artificial Intelligence, PAKDD 2021, 2021. (Type: Workshop | BibTeX | Tags: )@workshop{Bhavya-et.-al-2021,
title = {Cooperative Monitoring of Malicious Activity in Stock Exchanges},
author = {Bhavya Kalra and Sai Krishna Munnangi and Kushal Majmundar and Naresh Manwani and Praveen Paruchuri},
year = {2021},
date = {2021-05-15},
booktitle = {Workshop on Data Assessment and Readiness for Artificial Intelligence, PAKDD 2021},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
3. | Daksh Anand; Vaibhav Gupta; Praveen Paruchuri; Balaraman Ravindran: An Enhanced Advising Model in Teacher-Student Framework using State
Categorization. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9,
2021, pp. 6653–6660, AAAI Press, 2021. (Type: Conference | Links | BibTeX | Tags: )@inproceedings{DBLP:conf/aaai/AnandGPR21,
title = {An Enhanced Advising Model in Teacher-Student Framework using State
Categorization},
author = {Daksh Anand and Vaibhav Gupta and Praveen Paruchuri and Balaraman Ravindran},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/16823},
year = {2021},
date = {2021-01-01},
booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9,
2021},
pages = {6653--6660},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
4. | Vaibhav Gupta; Daksh Anand; Praveen Paruchuri; Akshat Kumar: Action Selection for Composable Modular Deep Reinforcement Learning. In: Dignum, Frank; Lomuscio, Alessio; Endriss, Ulle; é, Ann Now (Ed.): AAMAS '21: 20th International Conference on Autonomous Agents and
Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021, pp. 565–573, ACM, 2021. (Type: Conference | Links | BibTeX | Tags: )@inproceedings{DBLP:conf/atal/GuptaAPK21,
title = {Action Selection for Composable Modular Deep Reinforcement Learning},
author = {Vaibhav Gupta and Daksh Anand and Praveen Paruchuri and Akshat Kumar},
editor = {Frank Dignum and Alessio Lomuscio and Ulle Endriss and Ann Now \'{e}},
url = {https://dl.acm.org/doi/10.5555/3463952.3464022},
year = {2021},
date = {2021-01-01},
booktitle = {AAMAS '21: 20th International Conference on Autonomous Agents and
Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021},
pages = {565--573},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
5. | Praveen Paruchuri; Sreeja Kamishetty; Soumya Vadlamannati: System and method for controlling vehicular pollution concentration and providing maximum traffic flow throughput . 2020. (Type: Patent | BibTeX | Tags: patent)@patent{Paruchuri2020,
title = {System and method for controlling vehicular pollution concentration and providing maximum traffic flow throughput },
author = {Praveen Paruchuri and Sreeja Kamishetty and Soumya Vadlamannati},
year = {2020},
date = {2020-06-25},
keywords = {patent},
pubstate = {published},
tppubtype = {patent}
}
|
6. | Susobhan Ghosh; Sujit Gujar; Praveen Paruchuri; Easwar Subramanian; Sanjay P: Bidding in Smart Grid PDAs: Theory, Analysis and Strategy. In: AAAI, AAAI Press, 2020. (Type: Conference | Links | BibTeX | Tags: auctions, bidding, equilibrium, Game Theory)@inproceedings{ghosh20a,
title = {Bidding in Smart Grid PDAs: Theory, Analysis and Strategy},
author = {Susobhan Ghosh and Sujit Gujar and Praveen Paruchuri and Easwar Subramanian and Sanjay P},
url = {https://www.researchgate.net/publication/337337754_Bidding_in_Smart_Grid_PDAs_Theor},
year = {2020},
date = {2020-02-07},
booktitle = {AAAI},
publisher = {AAAI Press},
keywords = {auctions, bidding, equilibrium, Game Theory},
pubstate = {published},
tppubtype = {inproceedings}
}
|
7. | Sreeja Kamishetty; Soumya Vadlamannati; Praveen Paruchuri: Towards a better management of urban traffic pollution using a Pareto max flow approach. In: Transportation Research Part D: Transport and Environment, 79 , pp. 102194, 2020. (Type: Journal Article | BibTeX | Tags: )@article{kamishetty2020towards,
title = {Towards a better management of urban traffic pollution using a Pareto max flow approach},
author = {Sreeja Kamishetty and Soumya Vadlamannati and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
journal = {Transportation Research Part D: Transport and Environment},
volume = {79},
pages = {102194},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
8. | Sai Krishna Munnangi; Praveen Paruchuri: Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach.. In: HICSS, pp. 1–10, 2020. (Type: Conference | BibTeX | Tags: )@inproceedings{munnangi2020improving,
title = {Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach.},
author = {Sai Krishna Munnangi and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {HICSS},
pages = {1--10},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
9. | Anoop Karnik Dasika; Praveen Paruchuri: An Ensemble Learning Approach to Improve Tracking Accuracy of Multi Sensor Fusion.. In: ICONIP, 2020. (Type: Conference | BibTeX | Tags: )@inproceedings{dasika2020an,
title = {An Ensemble Learning Approach to Improve Tracking Accuracy of Multi Sensor Fusion.},
author = {Anoop Karnik Dasika and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {ICONIP},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
10. | Anindya Pradhan; Easwar Subramanian; Sanjay Bhat P; Praveen Paruchuri; Sujit Gujar: Rise of Algorithmic Trading in Today's Changing Electricity Market.. In: India Smart Utility Week, 2020. (Type: Conference | BibTeX | Tags: )@inproceedings{pradhan2020rise,
title = {Rise of Algorithmic Trading in Today's Changing Electricity Market.},
author = {Anindya Pradhan and Easwar Subramanian and Sanjay P Bhat and Praveen Paruchuri and Sujit Gujar},
year = {2020},
date = {2020-01-01},
booktitle = {India Smart Utility Week},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
11. | Sai Naveen Pucha; Praveen Paruchuri: Inferring Personality Types for Better Automated Negotiation. In: International Conference on Group Decision and Negotiation, pp. 149–162, Springer 2020. (Type: Conference | BibTeX | Tags: )@inproceedings{pucha2020inferring,
title = {Inferring Personality Types for Better Automated Negotiation},
author = {Sai Naveen Pucha and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {International Conference on Group Decision and Negotiation},
pages = {149--162},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
12. | Aditya Srinivas Gear; Kritika Prakash; Nonidh Singh; Praveen Paruchuri: PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value. In: International Conference on Group Decision and Negotiation, pp. 135–148, Springer 2020. (Type: Conference | BibTeX | Tags: )@inproceedings{gear2020predictrv,
title = {PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value},
author = {Aditya Srinivas Gear and Kritika Prakash and Nonidh Singh and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {International Conference on Group Decision and Negotiation},
pages = {135--148},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
13. | Aditya Srinivas Gear; Kritika Prakash; Nonidh Singh; Praveen Paruchuri: PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value. In: Morais, Danielle Costa; Fang, Liping; Horita, Masahide (Ed.): Group Decision and Negotiation: A Multidisciplinary Perspective, pp. 135–148, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-48641-9. (Type: Conference | Abstract | BibTeX | Tags: )@inproceedings{10.1007/978-3-030-48641-9_10,
title = {PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value},
author = {Aditya Srinivas Gear and Kritika Prakash and Nonidh Singh and Praveen Paruchuri},
editor = {Danielle Costa Morais and Liping Fang and Masahide Horita},
isbn = {978-3-030-48641-9},
year = {2020},
date = {2020-01-01},
booktitle = {Group Decision and Negotiation: A Multidisciplinary Perspective},
pages = {135--148},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and Time-Dependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and Time-Dependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings. |
14. | Sreeja Kamishetty; Praveen Paruchuri: Towards a Better Management of Emergency Evacuation using Pareto Min Cost Max Flow Approach. 6th International Conference on Vehicle Technology and Intelligent Transportation Systems, 2020. (Type: Conference | BibTeX | Tags: )@conference{sreeja2020towards,
title = {Towards a Better Management of Emergency Evacuation using Pareto Min Cost Max Flow Approach},
author = {Sreeja Kamishetty and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {6th International Conference on Vehicle Technology and Intelligent Transportation Systems},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
15. | Vaibhav Gupta; Daksh Anand; Praveen Paruchuri; Balaraman Ravindran: Advice Replay Approach for Richer Knowledge Transfer in Teacher Student
Framework. In: AAMAS, pp. 1997–1999, International Foundation for Autonomous Agents and Multiagent Systems, 2019. (Type: Conference | BibTeX | Tags: )@inproceedings{DBLP:conf/atal/GuptaAPR19,
title = {Advice Replay Approach for Richer Knowledge Transfer in Teacher Student
Framework},
author = {Vaibhav Gupta and Daksh Anand and Praveen Paruchuri and Balaraman Ravindran},
year = {2019},
date = {2019-01-01},
booktitle = {AAMAS},
pages = {1997--1999},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
16. | Susobhan Ghosh; Easwar Subramanian; Sanjay Bhat P; Sujit Gujar; Praveen Paruchuri: VidyutVanika: A Reinforcement Learning Based Broker Agent for a
Power Trading Competition. In: AAAI, pp. 914–921, AAAI Press, 2019. (Type: Conference | BibTeX | Tags: )@inproceedings{DBLP:conf/aaai/GhoshSBGP19,
title = {VidyutVanika: A Reinforcement Learning Based Broker Agent for a
Power Trading Competition},
author = {Susobhan Ghosh and Easwar Subramanian and Sanjay P Bhat and Sujit Gujar and Praveen Paruchuri},
year = {2019},
date = {2019-01-01},
booktitle = {AAAI},
pages = {914--921},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
17. | Tarun Gupta; Akshat Kumar; Praveen Paruchuri: Successor Features Based Multi-Agent RL for Event-Based Decentralized
MDPs. In: AAAI, pp. 6054–6061, AAAI Press, 2019. (Type: Conference | BibTeX | Tags: )@inproceedings{DBLP:conf/aaai/0002KP19,
title = {Successor Features Based Multi-Agent RL for Event-Based Decentralized
MDPs},
author = {Tarun Gupta and Akshat Kumar and Praveen Paruchuri},
year = {2019},
date = {2019-01-01},
booktitle = {AAAI},
pages = {6054--6061},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
18. | Susobhan Ghosh; Kritika Prakash; Sanjay Chandlekar; Easwar Subramanian; Sanjay Bhat P; Sujit Gujar; Praveen Paruchuri: Vidyutvanika: An autonomous broker agent for smart grid environment. In: PASS, Policy, Awareness, Sustainability and Systems (PASS) Workshop, 2019. (Type: Conference | BibTeX | Tags: Reinforcement Learning, smart grids)@inproceedings{ghosh2019vidyutvanika,
title = {Vidyutvanika: An autonomous broker agent for smart grid environment},
author = {Susobhan Ghosh and Kritika Prakash and Sanjay Chandlekar and Easwar Subramanian and Sanjay P Bhat and Sujit Gujar and Praveen Paruchuri},
year = {2019},
date = {2019-01-01},
booktitle = {PASS},
volume = {7},
publisher = {Policy, Awareness, Sustainability and Systems (PASS) Workshop},
keywords = {Reinforcement Learning, smart grids},
pubstate = {published},
tppubtype = {inproceedings}
}
|
19. | Tarun Gupta; Akshat Kumar; Praveen Paruchuri: Planning and Learning for Decentralized MDPs with Event Driven Rewards. In: AAAI Workshops, pp. 665, AAAI Press, 2018. (Type: Conference | BibTeX | Tags: )@inproceedings{DBLP:conf/aaai/GuptaKP18a,
title = {Planning and Learning for Decentralized MDPs with Event Driven Rewards},
author = {Tarun Gupta and Akshat Kumar and Praveen Paruchuri},
year = {2018},
date = {2018-01-01},
booktitle = {AAAI Workshops},
volume = {WS-18},
pages = {665},
publisher = {AAAI Press},
series = {AAAI Workshops},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
20. | Tarun Gupta; Akshat Kumar; Praveen Paruchuri: Planning and Learning for Decentralized MDPs With Event Driven Rewards. In: AAAI, pp. 6186–6194, AAAI Press, 2018. (Type: Conference | BibTeX | Tags: )@inproceedings{DBLP:conf/aaai/GuptaKP18b,
title = {Planning and Learning for Decentralized MDPs With Event Driven Rewards},
author = {Tarun Gupta and Akshat Kumar and Praveen Paruchuri},
year = {2018},
date = {2018-01-01},
booktitle = {AAAI},
pages = {6186--6194},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|