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pro vyhledávání: '"Muppasani, Bharath"'
Autor:
Pallagani, Vishal, Roy, Kaushik, Muppasani, Bharath, Fabiano, Francesco, Loreggia, Andrea, Murugesan, Keerthiram, Srivastava, Biplav, Rossi, Francesca, Horesh, Lior, Sheth, Amit
Publikováno v:
Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 432-444 (2024)
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique appl
Externí odkaz:
http://arxiv.org/abs/2401.02500
Rubik's Cube (RC) is a well-known and computationally challenging puzzle that has motivated AI researchers to explore efficient alternative representations and problem-solving methods. The ideal situation for planning here is that a problem be solved
Externí odkaz:
http://arxiv.org/abs/2307.13552
Autor:
Muppasani, Bharath, Pallagani, Vishal, Srivastava, Biplav, Mutharaju, Raghava, Huhns, Michael N., Narayanan, Vignesh
Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we consider the problem of automated planning, where the objective is to find a seq
Externí odkaz:
http://arxiv.org/abs/2307.13549
Autor:
Lakkaraju, Kausik, Vuruma, Sai Krishna Revanth, Pallagani, Vishal, Muppasani, Bharath, Srivastava, Biplav
Increasingly powerful Large Language Model (LLM) based chatbots, like ChatGPT and Bard, are becoming available to users that have the potential to revolutionize the quality of decision-making achieved by the public. In this context, we set out to inv
Externí odkaz:
http://arxiv.org/abs/2307.07422
Autor:
Pallagani, Vishal, Muppasani, Bharath, Murugesan, Keerthiram, Rossi, Francesca, Srivastava, Biplav, Horesh, Lior, Fabiano, Francesco, Loreggia, Andrea
Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Language Models (LLMs) can answer questions, write high-quality programmi
Externí odkaz:
http://arxiv.org/abs/2305.16151
Autor:
Muppasani, Bharath, Pallagani, Vishal, Lakkaraju, Kausik, Lei, Shuge, Srivastava, Biplav, Robertson, Brett, Hickerson, Andrea, Narayanan, Vignesh
Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-informatio
Externí odkaz:
http://arxiv.org/abs/2212.11219
Autor:
Pallagani, Vishal, Muppasani, Bharath, Murugesan, Keerthiram, Rossi, Francesca, Horesh, Lior, Srivastava, Biplav, Fabiano, Francesco, Loreggia, Andrea
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP). From BERT to BLOOM, LLMs have surpassed state-of-the-art results in various natural language tasks such as
Externí odkaz:
http://arxiv.org/abs/2212.08681
Autor:
Muppasani, Bharath, Anand, Cheyyur Jaya, Appajigowda, Chinmayi, Srivastava, Biplav, Johri, Lokesh
Publikováno v:
Proc. Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-23), Washington DC, USA, 2023
The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medica
Externí odkaz:
http://arxiv.org/abs/2209.00987
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