• Publications
  • Influence
Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces
A key element in the solution of reinforcement learning problems is the value function. The purpose of this function is to measure the long-term utility or value of any given state. The function isExpand
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Continuous Case-Based Reasoning
Abstract Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations.Expand
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Conversational AI: The Science Behind the Alexa Prize
Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics. To advanceExpand
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Case-Based Planning and Execution for Real-Time Strategy Games
Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificialExpand
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A Theory of Questions and Question Asking
  • A. Ram
  • Computer Science, Psychology
  • 1 July 1991
This article focuses on knowledge goals, that is, the goals of a reasoner to acquire or reorganize knowledge. Knowledge goals, often expressed as questions, arise when the reasoner's model of theExpand
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Some domains, such as real‐time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this article, we present a novel on‐line case‐based planningExpand
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Exploring question subjectivity prediction in community QA
In this paper we begin to investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questionsExpand
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Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases
  • A. Ram
  • Computer Science
  • Machine Learning
  • 1 March 1993
This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-basedExpand
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Efficient Feature Selection in Conceptual Clustering
Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate theExpand
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