Ashwin Ram, David B. Leake
MIT Press, 1995 - Computers - 507 pages
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.
A Bradford Book
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Learning Goals and Learning Goals
Planning to Learn
Quantitative Results Concerning the Utility
The Use of Explicit Goals for Knowledge to Guide Inference
Deriving Categories to Achieve Goals
The Interaction of Theory
Introspective Reasoning Using MetaExplanations
A DecisionTheoretic Model
Explicitly Biased Generalization
Three Levels of Goal Orientation in Learning
Characterizing the Application of Computer Simulations
Fundamental Issues and Symposium Report
Studying Processing to Understand Learning
GoalDriven Learning in Multistrategy Reasoning
A Coherence Theory of Decision
Toward GoalDriven Integration of Explanation and Action
achieve actions active approach AQUA Artificial Intelligence attribute Barsalou Carbonell case-based Case-Based Reasoning central tendency chap Cognitive Science Cognitive Science Society common taxonomic categories Computer Science constraints context control rules decision derived categories determine domain theory effect evaluation example exemplars expected utility explanation-based learning explicit frame goal orientation goal-based goal-derived categories goal-driven learning heuristics hypotheses induction inference input instance instantiations interaction Introspective knowledge goals Leake learner learning goals learning process learning strategies learning systems lexemes Machine Learning macro-operator Medin memory Meta-XP methods Michalski Morgan Kaufmann multistrategy learning needs node operator performance planning prediction problem problem-solving Proceedings PRODIGY prototype structure Psychology questions reasoning failure reasoning process relevant representation represented retrieval role rule induction Schank selection simulation situation solving specific storage subgoals target concept task goals tion types typicality understanding vacation locations volume world model