21 citations · 34 across the 7 of their papers we have counts for
7 papers
CASSANDRA: Programmatic and Probabilistic Learning and Inference for Stochastic World Modeling
Panagiotis Lymperopoulos, Abhiramon Rajasekharan, Ian Berlot-Attwell +2
Building world models is essential for planning in real-world domains such as businesses. Since such domains have rich semantics, we can leverage world knowledge to effectively mod…
Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions
Stéphane Aroca-Ouellette, Ian Berlot-Attwell, Panagiotis Lymperopoulos +5
Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains such as bus…
Modeling Deontic Modal Logic in ASP
Gopal Gupta, Abhiramon Rajasekharan, Alexis R. Tudor +2
We consider the problem of implementing deontic modal logic. We show how (deontic) modal operators can be elegantly and directly expressed using default negation (negation-as-failu…
A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP
Yankai Zeng, Abhiramon Rajashekharan, Kinjal Basu +3
The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their abil…
Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
Yankai Zeng, Abhiramon Rajasekharan, Parth Padalkar +3
Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. These…
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming
Abhiramon Rajasekharan, Yankai Zeng, Parth Padalkar +1
Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions.…
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
Nathan Beck, Abhiramon Rajasekharan, Hieu Tran
Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been tr…