Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...
Real time delivery of products is the context of stochastic demands and multiple vehicles is a difficult problem as it requires the joint investigation of the problems in inventory control and vehicle routing. We model this problem in the framework of Average reward Reinforcement Learning (ARL) and present experimental results...
Reinforcement Learning (RL) is the study of agents that learn optimal
behavior by interacting with and receiving rewards and punishments from an unknown
environment. RL agents typically do this by learning value functions that
assign a value to each state (situation) or to each state-action pair. Recently,
there has been...
This document analyzes the application of Monte Carlo Counterfactual Regret Minimization (MCCFR) in the game of Hasboro’s Clue. As a partially observable stochastic multiplayer game, Clue is well-suited for MCCFR methods. MCCFR has previously been shown to be effective in beating top human players around the world in No-Limit Texas...
Artificial Intelligence (AI) planning techniques have been central to automating a gamut of tasks from the mundane route planning and beer production to the ethereal image processing of space-ship images. Of all the planning techniques, hierarchical- decomposition planning has been the technique most employed in industrial-strength planners. Hierarchical-decomposition planning is...
Supervised learning programs, such as decision tree learners and neural networks, often must learn Boolean functions. The concept being learned may not easily be expressed in terms of the atomic features given. Constructive induction automatically produces higher level features (combinations of the atomic features), which can improve learning performance. The...
Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning algorithms for this problem employ some variation of a greedy separate-and-conquer algorithm. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a...
One current research goal of Artificial Intelligence and Machine Learning is to build learning systems that robustly improve their planning performance with experience [Tade91]. This work concentrates on learning decomposition rules, i.e., learning rules that guide the planning process by determining the order in which operators are to be applied...
Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving scalar rewards, which the agent aims to maximize. But in many real world situations, tradeoffs must be made among multiple objectives. This necessitates the use...
Intuitively, it seems as though natural language processing tasks might benefit from explicit representations of the syntactic and semantic properties of text. Ontonotes is a dataset which attempts to annotate texts, to represent as much as possible of the meaning of the text explicitly within the annotation. Many tools exist...