activity
20172021
most citedEnhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

17 citations · 42 across the 8 of their papers we have counts for

collaborators
Showing cs.AIShow all

10 papers · 1 filter

cs.AI2021

Learning to Guide a Saturation-Based Theorem Prover

Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni +8

Traditional automated theorem provers have relied on manually tuned heuristics to guide how they perform proof search. Recently, however, there has been a surge of interest in the…

cs.AI20217 cited

Logic Embeddings for Complex Query Answering

Francois Luus, Prithviraj Sen, Pavan Kapanipathi +4

Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order…

cs.AI2020

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines

Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi +6

Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential deci…

cs.AI202017 cited

Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla +3

In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments. This…

cs.AI20194 cited

Path-Based Contextualization of Knowledge Graphs for Textual Entailment

Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi +3

In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs fro…

cs.AI2019

A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving

Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni +7

Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to o…