52,164 research outputs found
Answer Set Programming for Non-Stationary Markov Decision Processes
Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains
Learning by Asking Questions
We introduce an interactive learning framework for the development and
testing of intelligent visual systems, called learning-by-asking (LBA). We
explore LBA in context of the Visual Question Answering (VQA) task. LBA differs
from standard VQA training in that most questions are not observed during
training time, and the learner must ask questions it wants answers to. Thus,
LBA more closely mimics natural learning and has the potential to be more
data-efficient than the traditional VQA setting. We present a model that
performs LBA on the CLEVR dataset, and show that it automatically discovers an
easy-to-hard curriculum when learning interactively from an oracle. Our LBA
generated data consistently matches or outperforms the CLEVR train data and is
more sample efficient. We also show that our model asks questions that
generalize to state-of-the-art VQA models and to novel test time distributions
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