10 citations · 23 across the 14 of their papers we have counts for
15 papers
AIRA_2: Overcoming Bottlenecks in AI Research Agents
Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo +22
Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the ben…
COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game
Alkis Sygkounas, Rishi Hazra, Andreas Persson +2
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and gener…
LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language
Periklis Mantenoglou, Rishi Hazra, Pedro Zuidberg Dos Martires +1
Owing to their reasoning capabilities, large language models (LLMs) have been evaluated on planning tasks described in natural language. However, LLMs have largely been tested on p…
Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition
Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires +1
Large Language Models (LLMs) have been touted as AI models possessing advanced reasoning abilities. In theory, autoregressive LLMs with Chain-of-Thought (CoT) can perform more seri…
Evaluating Efficiency and Engagement in Scripted and LLM-Enhanced Human-Robot Interactions
Tim Schreiter, Jens V. Rüppel, Rishi Hazra +3
To achieve natural and intuitive interaction with people, HRI frameworks combine a wide array of methods for human perception, intention communication, human-aware navigation and c…
Bidirectional Intent Communication: A Role for Large Foundation Models
Tim Schreiter, Rishi Hazra, Jens Rüppel +1
Integrating multimodal foundation models has significantly enhanced autonomous agents' language comprehension, perception, and planning capabilities. However, while existing works…
Can Large Language Models Reason? A Characterization via 3-SAT
Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires +1
Large Language Models (LLMs) have been touted as AI models possessing advanced reasoning abilities. However, recent works have shown that LLMs often bypass true reasoning using sho…
REvolve: Reward Evolution with Large Language Models using Human Feedback
Rishi Hazra, Alkis Sygkounas, Andreas Persson +2
Designing effective reward functions is crucial to training reinforcement learning (RL) algorithms. However, this design is non-trivial, even for domain experts, due to the subject…
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt
Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge". Yet, obtaining plans that are both feasible (grounded in affordanc…
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach
Rishi Hazra, Luc De Raedt
Despite numerous successes in Deep Reinforcement Learning (DRL), the learned policies are not interpretable. Moreover, since DRL does not exploit symbolic relational representation…
gComm: An environment for investigating generalization in Grounded Language Acquisition
Rishi Hazra, Sonu Dixit
gComm is a step towards developing a robust platform to foster research in grounded language acquisition in a more challenging and realistic setting. It comprises a 2-d grid enviro…
Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols
Rishi Hazra, Sonu Dixit, Sayambhu Sen
Human language has been described as a system that makes \textit{use of finite means to express an unlimited array of thoughts}. Of particular interest is the aspect of composition…
Active Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra, Parag Dutta, Shubham Gupta +2
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. Ho…
Intrinsically Motivated Compositional Language Emergence
Rishi Hazra, Sonu Dixit, Sayambhu Sen
Recently, there has been a great deal of research in emergent communication on artificial agents interacting in simulated environments. Recent studies have revealed that, in genera…
Networked Multi-Agent Reinforcement Learning with Emergent Communication
Shubham Gupta, Rishi Hazra, Ambedkar Dukkipati
Multi-Agent Reinforcement Learning (MARL) methods find optimal policies for agents that operate in the presence of other learning agents. Central to achieving this is how the agent…