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Papers by Gheorghe Tecuci

Research paper thumbnail of Artificial intelligence

Research paper thumbnail of A Methodology for Modeling and Representing Expert Knowledge that Supports Teaching-Based Intelligent Agent Development

Research paper thumbnail of Mixed-Initiative Exception-Based Learning for Knowledge Base Refinement

The Yard Allocation Problem (YAP) is a real-life resource allocation problem faced by the Port of... more The Yard Allocation Problem (YAP) is a real-life resource allocation problem faced by the Port of Singapore Authority (PSA). We first show that YAP is NP-Hard. As the problem is NP-Hard, we propose several heuristics, including Tabu Search methods with ...

Research paper thumbnail of Training an Agent Through Demonstration: A Plausible Version Space Approach

This paper presents an efficient approach to training an agent to perform a complex task through ... more This paper presents an efficient approach to training an agent to perform a complex task through demonstration, explanation and supervision. This approach is based on an integration of techniques of multistrategy and apprenticeship learning, knowledge elicitation and programming by demonstration, in a plausible version space framework, and is implemented in Agent-Disciple. Agent-Disciple addresses the complexity of the task training problem

Research paper thumbnail of Building Adaptive Autonomous Agents for Adversarial Domains

This paper presents a methodology, called CAPTAIN, to build adaptive agents in an integrated fram... more This paper presents a methodology, called CAPTAIN, to build adaptive agents in an integrated framework that facilitates both building agents through knowledge elicitation and interactive apprenticeship learning from subject matter experts, and making these agents adapt and improve during their normal use through autonomous learning. Such an automated adaptive agent consists of an adversarial planner and a muitistrategy learner. CAPTAIN

Research paper thumbnail of A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications

International Conference on Machine Learning, 1991

Research paper thumbnail of Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory

This book is printed on acid-free paper. Copyright © 1998 by ACADEMIC PRESS All Rights Reserved. ... more This book is printed on acid-free paper. Copyright © 1998 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means electronic or mechanical, including photocopy, recording, or any information ...

Research paper thumbnail of Input" Understanding" as a Basis for Multisteategy Task-Adaptive Learning

Research paper thumbnail of A Machine Learning Based Methodology for Building Intelligent Agents

Research paper thumbnail of DISCIPLE: une théorie, une méthodologie et un système pour apprendre des connaissances expertes

Research paper thumbnail of Multi Strategy Approaches to Learning: Why And How

Research paper thumbnail of Steps towards second generation expert systems

Research paper thumbnail of Special Issue: Multistrategy Learning

Research paper thumbnail of Collaborative Assistant for Rapid Knowledge Formation and Reasoning

Public reporting burden for this collection of information is estimated to average 1 hour per res... more Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to

Research paper thumbnail of Machine learning: a multistrategy approach

... Patterns: Making a Transition from Data-Driven to Theory-Driven Learning 267 ... CONTENTS vli... more ... Patterns: Making a Transition from Data-Driven to Theory-Driven Learning 267 ... CONTENTS vli CHAPTER 18 Multistrategy Learning from Engineering Data by Integrating Inductive ... Robert Levinson CHAPTER 23 Classifying for Prediction: A Multistrategy Approach to Predicting ...

Research paper thumbnail of Designing A Computer Opponent for Wargames: Integrating Planning, Knowledge Acquisition and Learning in WARGLES

Research paper thumbnail of Developing intelligent educational agents with the disciple learning agent shell

Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents ... more Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent how to perform domain-specific tasks in a way that resembles how the expert would teach an apprentice. We claim that Disciple can naturally be used to build certain types of educational agents. Indeed, an educator can teach a Disciple agent which in turn can tutor students in the same way it was taught by the educator. This paper presents the Disciple approach and its application to developing an educational agent that generates history test questions. The agent provides intelligent feedback to the student in the form of hints, answer and explanations, and assists in the assessment of student's understanding and use of higher-order thinking skills.

Research paper thumbnail of An inference-based framework for multistrategy learning

This chapter describes a general framework for multistrategy learning. One idea of this framework... more This chapter describes a general framework for multistrategy learning. One idea of this framework is to view learning as an inference process and to integrate the elementary inferences that are employed by the single-strategy learning methods. Another idea is to base learning on building and generalizing a special type of explanation structure called plausible justification tree which is composed of different types of inference and relates the learner's knowledge to the input. In this framework, learning consists of extending and/or improving the knowledge base of the system so that to explain the input received from an external source of information. The framework is illustrated with a specific method that integrates deeply and dynamically explanation-based learning, determination-based analogy, empirical induction, constructive induction, and abduction.

Research paper thumbnail of Learning as understanding the external world

Research paper thumbnail of Developing an intelligent educational agent with Disciple

Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents ... more Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent to perform domain-specific tasks in a way that resembles how the expert would teach an apprentice, by giving the agent examples and explanations, and by supervising and correcting its behavior. The Disciple approach is currently implemented in the Disciple Learning Agent Shell. We make the claim that Disciple can naturally be used by an educator to build certain types of educational agents. The educator will directly teach the Disciple agent how to perform certain educational tasks and then the agent can interact with the students to perform such tasks. This paper presents the Disciple approach and its application to building an educational agent that generates history tests for students. These tests provide intelligent feedback to the student in the form of hints, answer and explanations, and assist in the assessment of students' understanding and use of higher-order thinking skills.

Research paper thumbnail of Artificial intelligence

Research paper thumbnail of A Methodology for Modeling and Representing Expert Knowledge that Supports Teaching-Based Intelligent Agent Development

Research paper thumbnail of Mixed-Initiative Exception-Based Learning for Knowledge Base Refinement

The Yard Allocation Problem (YAP) is a real-life resource allocation problem faced by the Port of... more The Yard Allocation Problem (YAP) is a real-life resource allocation problem faced by the Port of Singapore Authority (PSA). We first show that YAP is NP-Hard. As the problem is NP-Hard, we propose several heuristics, including Tabu Search methods with ...

Research paper thumbnail of Training an Agent Through Demonstration: A Plausible Version Space Approach

This paper presents an efficient approach to training an agent to perform a complex task through ... more This paper presents an efficient approach to training an agent to perform a complex task through demonstration, explanation and supervision. This approach is based on an integration of techniques of multistrategy and apprenticeship learning, knowledge elicitation and programming by demonstration, in a plausible version space framework, and is implemented in Agent-Disciple. Agent-Disciple addresses the complexity of the task training problem

Research paper thumbnail of Building Adaptive Autonomous Agents for Adversarial Domains

This paper presents a methodology, called CAPTAIN, to build adaptive agents in an integrated fram... more This paper presents a methodology, called CAPTAIN, to build adaptive agents in an integrated framework that facilitates both building agents through knowledge elicitation and interactive apprenticeship learning from subject matter experts, and making these agents adapt and improve during their normal use through autonomous learning. Such an automated adaptive agent consists of an adversarial planner and a muitistrategy learner. CAPTAIN

Research paper thumbnail of A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications

International Conference on Machine Learning, 1991

Research paper thumbnail of Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory

This book is printed on acid-free paper. Copyright © 1998 by ACADEMIC PRESS All Rights Reserved. ... more This book is printed on acid-free paper. Copyright © 1998 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means electronic or mechanical, including photocopy, recording, or any information ...

Research paper thumbnail of Input" Understanding" as a Basis for Multisteategy Task-Adaptive Learning

Research paper thumbnail of A Machine Learning Based Methodology for Building Intelligent Agents

Research paper thumbnail of DISCIPLE: une théorie, une méthodologie et un système pour apprendre des connaissances expertes

Research paper thumbnail of Multi Strategy Approaches to Learning: Why And How

Research paper thumbnail of Steps towards second generation expert systems

Research paper thumbnail of Special Issue: Multistrategy Learning

Research paper thumbnail of Collaborative Assistant for Rapid Knowledge Formation and Reasoning

Public reporting burden for this collection of information is estimated to average 1 hour per res... more Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to

Research paper thumbnail of Machine learning: a multistrategy approach

... Patterns: Making a Transition from Data-Driven to Theory-Driven Learning 267 ... CONTENTS vli... more ... Patterns: Making a Transition from Data-Driven to Theory-Driven Learning 267 ... CONTENTS vli CHAPTER 18 Multistrategy Learning from Engineering Data by Integrating Inductive ... Robert Levinson CHAPTER 23 Classifying for Prediction: A Multistrategy Approach to Predicting ...

Research paper thumbnail of Designing A Computer Opponent for Wargames: Integrating Planning, Knowledge Acquisition and Learning in WARGLES

Research paper thumbnail of Developing intelligent educational agents with the disciple learning agent shell

Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents ... more Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent how to perform domain-specific tasks in a way that resembles how the expert would teach an apprentice. We claim that Disciple can naturally be used to build certain types of educational agents. Indeed, an educator can teach a Disciple agent which in turn can tutor students in the same way it was taught by the educator. This paper presents the Disciple approach and its application to developing an educational agent that generates history test questions. The agent provides intelligent feedback to the student in the form of hints, answer and explanations, and assists in the assessment of student's understanding and use of higher-order thinking skills.

Research paper thumbnail of An inference-based framework for multistrategy learning

This chapter describes a general framework for multistrategy learning. One idea of this framework... more This chapter describes a general framework for multistrategy learning. One idea of this framework is to view learning as an inference process and to integrate the elementary inferences that are employed by the single-strategy learning methods. Another idea is to base learning on building and generalizing a special type of explanation structure called plausible justification tree which is composed of different types of inference and relates the learner's knowledge to the input. In this framework, learning consists of extending and/or improving the knowledge base of the system so that to explain the input received from an external source of information. The framework is illustrated with a specific method that integrates deeply and dynamically explanation-based learning, determination-based analogy, empirical induction, constructive induction, and abduction.

Research paper thumbnail of Learning as understanding the external world

Research paper thumbnail of Developing an intelligent educational agent with Disciple

Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents ... more Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent to perform domain-specific tasks in a way that resembles how the expert would teach an apprentice, by giving the agent examples and explanations, and by supervising and correcting its behavior. The Disciple approach is currently implemented in the Disciple Learning Agent Shell. We make the claim that Disciple can naturally be used by an educator to build certain types of educational agents. The educator will directly teach the Disciple agent how to perform certain educational tasks and then the agent can interact with the students to perform such tasks. This paper presents the Disciple approach and its application to building an educational agent that generates history tests for students. These tests provide intelligent feedback to the student in the form of hints, answer and explanations, and assist in the assessment of students' understanding and use of higher-order thinking skills.