1.6k citations · 8k across the 93 of their papers we have counts for
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Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Dora Jambor, Komal Teru, Joelle Pineau +1
Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods. These met…
No Press Diplomacy: Modeling Multi-Agent Gameplay
Philip Paquette, Yuchen Lu, Steven Bocco +6
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Reliance on trust and coordination makes Di…
The Second Conversational Intelligence Challenge (ConvAI2)
Emily Dinan, Varvara Logacheva, Valentin Malykh +14
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition ar…
MACA: A Modular Architecture for Conversational Agents
Hoai Phuoc Truong, Prasanna Parthasarathi, Joelle Pineau
We propose a software architecture designed to ease the implementation of dialogue systems. The Modular Architecture for Conversational Agents (MACA) uses a plug-n-play style that…
Non-Deterministic Policies in Markovian Decision Processes
Mahdi Milani Fard, Joelle Pineau
Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problem…
Online Planning Algorithms for POMDPs
Stéphane Ross, Joelle Pineau, Sébastien Paquet +1
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is…
Model-Based Bayesian Reinforcement Learning in Large Structured Domains
Stephane Ross, Joelle Pineau
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation tradeo…
Anytime Point-Based Approximations for Large POMDPs
J. Pineau, G. Gordon, S. Thrun
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact so…