Archive for August, 1995

1 Aug

Learning as Goal-Driven Inference

Posted by cognitivecomputing in Learning. Tagged: , .

Developing an adequate and general computational model of adaptive, multistrategy, and goal-oriented learning is a fundamental long-term objective for machine learning research for both theoretical and pragmatic reasons. We outline a proposal for developing such a model based on two key ideas. First, we view learning as an active process involving the formulation of learning goals during the performance of a reasoning task, the prioritization of learning goals, and the pursuit of learning goals using multiple learning strategies. The second key idea is to model learning as a kind of inference in which the system augments and reformulates its knowledge using various types of primitive inferential actions, known as knowledge transmutations.

Read the paper:

Learning as Goal-Driven Inference

by Ryszard Michalski, Ashwin Ram

In A. Ram & D. Leake (eds.), Goal-Driven Learning, chapter 21, MIT Press/Bradford Books, 1995
www.cc.gatech.edu/faculty/ashwin/papers/er-95-05.pdf

1 Aug

Goal-Driven Learning in Multistrategy Reasoning and Learning Systems

Posted by cognitivecomputing in Learning. Tagged: , , , .

This chapter presents a computational model of introspective multistrategy learning, which is a deliberative or strategic learning process in which a reasoner introspects about its own performance to decide what to learn and how to learn it. The reasoner introspects about its own performance on a reasoning task, assigns credit or blame for its performance, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. Our theory models a process of learning that is active, experiential, opportunistic, diverse, and introspective. This chapter also describes two computer systems that implement our theory, one that learns diagnostic knowledge during a troubleshooting task and one that learns multiple kinds of causal and explanatory knowledge during a story understanding task.

Read the paper:

Goal-Driven Learning in Multistrategy Reasoning and Learning Systems

by Ashwin Ram, Mike Cox, S Narayanan

In A. Ram & D. Leake (eds.), Goal-Driven Learning, chapter 18, MIT Press/Bradford Books, 1995
www.cc.gatech.edu/faculty/ashwin/papers/er-95-04.pdf

1 Aug

Learning, Goals, and Learning Goals

Posted by cognitivecomputing in Learning. Tagged: , , .

In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner’s goals. Investigators in each of these areas have independently pursued the common issues of how learning goals arise, how they affect learner decisions of when and what to learn, and how they guide the learning process. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning.

This chapter discusses fundamental questions for goal-driven learning: the motivations for adopting a goal-driven model of learning, the basic goal-driven learning framework, the specific issues raised by the framework that a theory of goal-driven learning must address, the types of goals that can influence learning, the types of influences those goals can have on learning, and the pragmatic implications of the goal-driven learning model.

Read the paper:

Learning, Goals, and Learning Goals

by Ashwin Ram, David Leake

In A. Ram & D. Leake (eds.), Goal-Driven Learning, chapter 1, MIT Press/Bradford Books, 1995

www.cc.gatech.edu/faculty/ashwin/papers/er-95-03.pdf

1 Aug

Goal-Driven Learning

Posted by cognitivecomputing in Books, Learning. Tagged: , .

In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner’s goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations.

The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts.

The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning.

Find the book:

Goal-Driven Learning

edited by Ashwin Ram, David Leake

MIT Press/Bradford Books, Cambridge, MA, 1995, ISBN 978-0-262-18165-5
mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8349

Preview the book: books.google.com/books?id=5vo9zMJRnMwC

Table of Contents

Preface by Professor Tom Mitchell
Editors’ Preface
Chapter 1: Learning, Goals, and Learning Goals, Ram, Leake

Part I: Current state of the field

Chapter 2: Planning to Learn, Hunter
Chapter 3: Quantitative Results Concerning the Utility of Explanation-Based Learning, Minton
Chapter 4: The Use of Explicit Goals for Knowledge to Guide Inference and Learning, Ram, Hunter
Chapter 5: Deriving Categories to Achieve Goals, Barsalou
Chapter 6: Harpoons and Long Sticks: The Interaction of Theory and Similarity in Rule Induction, Wisniewski, Medin
Chapter 7: Introspective Reasoning using Meta-Explanations for Multistrategy Learning, Ram, Cox
Chapter 8: Goal-Directed Learning: A Decision-Theoretic Model for Deciding What to Learn Next, desJardins
Chapter 9: Goal-Based Explanation Evaluation, Leake
Chapter 10: Planning to Perceive, Pryor, Collins
Chapter 11: Learning and Planning in PRODIGY: Overview of an Integrated Architecture, Carbonell, Etzioni, Gil, Joseph, Knoblock, Minton, Veloso
Chapter 12: A Learning Model for the Selection of Problem Solving Strategies in Continuous Physical Systems, Xia, Yeung
Chapter 13: Explicitly Biased Generalization, Gordon, Perlis
Chapter 14: Three Levels of Goal Orientation in Learning, Ng, Bereiter
Chapter 15: Characterising the Application of Computer Simulations in Education: Instructional Criteria, van Berkum, Hijne, de Jong, van Joolingen, Njoo

Part II: Current research and recent directions

Chapter 16: Goal-Driven Learning: Fundamental Issues and Symposium Report, Leake, Ram
Chapter 17: Storage Side Effects: Studying Processing to Understand Learning, Barsalou
Chapter 18: Goal-Driven Learning in Multistrategy Reasoning and Learning Systems, Ram, Cox, Narayanan
Chapter 19: Inference to the Best Plan: A Coherence Theory of Decision, Thagard, Millgram
Chapter 20: Towards Goal-Driven Integration of Explanation and Action, Leake
Chapter 21: Learning as Goal-Driven Inference, Michalski, Ram