Search Results for author: Sriram Gopalakrishnan

Found 15 papers, 3 papers with code

TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners

no code implementations14 Jun 2024 Tomas De la Rosa, Sriram Gopalakrishnan, Alberto Pozanco, Zhen Zeng, Daniel Borrajo

Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing some user satisfaction criteria.

Language Modelling Large Language Model +1

SafeAR: Safe Algorithmic Recourse by Risk-Aware Policies

no code implementations23 Aug 2023 Haochen Wu, Shubham Sharma, Sunandita Patra, Sriram Gopalakrishnan

However, the uncertainties of feature changes and the risk of higher than average costs in recourse have not been considered.

Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments

no code implementations28 Feb 2023 Tung Thai, Ming Shen, Mayank Garg, Ayush Kalani, Nakul Vaidya, Utkarsh Soni, Mudit Verma, Sriram Gopalakrishnan, Neeraj Varshney, Chitta Baral, Subbarao Kambhampati, Jivko Sinapov, Matthias Scheutz

Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task performance.

Novelty Detection

pyRDDLGym: From RDDL to Gym Environments

2 code implementations11 Nov 2022 Ayal Taitler, Michael Gimelfarb, Jihwan Jeong, Sriram Gopalakrishnan, Martin Mladenov, Xiaotian Liu, Scott Sanner

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description.

OpenAI Gym

Computing Policies That Account For The Effects Of Human Agent Uncertainty During Execution In Markov Decision Processes

1 code implementation15 Sep 2021 Sriram Gopalakrishnan, Mudit Verma, Subbarao Kambhampati

We present a framework to model the human agent's behavior with respect to state uncertainty, and can be used to compute MDP policies that accounts for these problems.

Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver

no code implementations9 Jul 2021 Sriram Gopalakrishnan, Utkarsh Soni, Tung Thai, Panagiotis Lymperopoulos, Matthias Scheutz, Subbarao Kambhampati

The game of monopoly is an adversarial multi-agent domain where there is no fixed goal other than to be the last player solvent, There are useful subgoals like monopolizing sets of properties, and developing them.

Long-range connectivity in a superconducting quantum processor using a ring resonator

no code implementations17 Dec 2020 Sumeru Hazra, Anirban Bhattacharjee, Madhavi Chand, Kishor V. Salunkhe, Sriram Gopalakrishnan, Meghan P. Patankar, R. Vijay

Qubit coherence and gate fidelity are typically considered the two most important metrics for characterizing a quantum processor.

Quantum Physics

Goal recognition via model-based and model-free techniques

no code implementations3 Nov 2020 Daniel Borrajo, Sriram Gopalakrishnan, Vamsi K. Potluru

In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains.

Minimizing Robot Navigation-Graph For Position-Based Predictability By Humans

no code implementations28 Oct 2020 Sriram Gopalakrishnan, Subbarao Kambhampati

In situations where humans and robots are moving in the same space whilst performing their own tasks, predictable paths taken by mobile robots can not only make the environment feel safer, but humans can also help with the navigation in the space by avoiding path conflicts or not blocking the way.

Blocking Position +1

Embedding Directed Graphs in Potential Fields Using FastMap-D

1 code implementation4 Jun 2020 Sriram Gopalakrishnan, Liron Cohen, Sven Koenig, T. K. Satish Kumar

FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs.

TGE-viz : Transition Graph Embedding for Visualization of Plan Traces and Domains

no code implementations24 Nov 2018 Sriram Gopalakrishnan, Subbarao Kambhampati

TGE-viz allows users to visualize and criticize plans more intuitively for mixed-initiative planning.

Graph Embedding

Recognizing Plans by Learning Embeddings from Observed Action Distributions

no code implementations5 Dec 2017 Yantian Zha, Yikang Li, Sriram Gopalakrishnan, Baoxin Li, Subbarao Kambhampati

The first involves resampling the distribution sequences to single action sequences, from which we could learn an action affinity model based on learned action (word) embeddings for plan recognition.

Activity Recognition Word Embeddings

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