Author: Karen Myers
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Learning by Demonstration Technology for Military Planning and Decision Making: A Deployment Story
We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army.
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Learning to Ask the Right Questions
Asking questions can clarify concepts, test hypotheses, add missing information, or provide additional knowledge to facilitate learning. The last item motivates the work described in this paper.
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Goal-directed Metacontrol for Integrated Procedure Learning
We describe a metalevel framework for coordinating the activities of a community of learners to create an integrated learning system. The metalevel framework is organized around learning goals, which are formulated through introspective reasoning to identify problems and requirements for the ongoing learning process.
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Learning By Demonstration to Support Military Planning and Decision Making
We describe the development and application of learning by demonstration technology to support user creation of automated procedures for a rich collaborative planning environment that is in widespread use by the U.S. Army.
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Question Asking to Inform Preference Learning: a Case Study
This paper presents a case study that explores how to instantiate a question asking framework to select questions for a particular type of learner used within learning by demonstration systems, namely Alexo graphic preference learner.
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MUESLI: Multiple utterance error correction for a spoken language interface
We propose a method for using all available information to help correct recognition errors in tasks that use constrained grammars of the kind used in the domain of Command and Control (CC) systems.
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CALO Workflow Recognition and Proactive Assistance
This short video offers glimpses of how SRI’s CALO agent helps users manage their tasks and time using advanced activity recognition algorithms based on logical probabilistic models.
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An Intelligent Personal Assistant for Task and Time Management
We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention agent system.
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Proactive Behavior of a Personal Assistive Agent
Our model for proactive assistance employs a meta-level layer to identify potentially helpful actions and determine when it is appropriate to perform them. We conclude by identifying technical challenges in developing systems that embody proactive behaviors.
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Proactivity in an Intentionally Helpful Personal Assistive Agent
This position paper outlines some initial thoughts on desired forms of proactive behavior, and identifies technical challenges in developing systems that embody such behaviors.
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Temporal Summarization of Plans
This paper presents an approach to summarizing temporal plans that focuses on identifying noteworthy temporal features. These techniques look for regularities or exceptional temporal elements, drawing on a modest domain theory to drive the search process.
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A Case Study in Engineering a Knowledge Base for an Intelligent Personal Assistant
We present a case study in engineering a large knowledge base (KB) to meet the requirements of a personal assistant. We discuss our KB development methodology and the engineering challenges we faced in the process.