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基本説明
The book opens with an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process.
Full Description
Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process.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, planful 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
Contents
Learning, goals, and learning goals, Ashwin Ram and David B. Leake. Part 1 Current state of the field: planning to learn, Lawrence Hunter; quantitative results concerning the utility of explanation-based learning, Steven Minton; the use of explicit goals for knowledge to guide inference and learning, Ashwin Ram and Lawrence Hunter; deriving categories to achieve goals, Lawrence W. Barsalou; harpoons and long sticks - the interaction of theory and similarity in rule induction, Edward J. Wisniewski and Douglas L. Medlin; introspective reasoning using meta-explanations for multistrategy learning, Ashwin Ram and Michael T. Cox; goal-directed learning - a decision-theoretic model for deciding what to learn next, Marie desJardins; goal-based explanation evaluation, David B. Leake; planning to perceive, Louise Pryor and Gregg Collins; planning and learning in PRODIGY - overview of an integrated architecture, Jaime Carbonell et al; a learning model for the selection of problem-solving strategies in continuous physical systems, Xiaodong Xia and Dit-Yan Yeung; explicitly biased generalization, Diana Gordon and Donald Perlis; three levels of goal orientation in learning, Evelyn Ng and Carl Bereiter; characterizing the application of computer simulations in education - instructional criteria, Jos J.A. van Berkum et al. Part 2 Current research and recent directions: goal-driven learning - fundamental issues and symposium report, David B. Leake and Ashwin Ram; storage side effects - studying processing to understand learning, Lawrence W. Barsalou; goal-driven learning in multistrategy reasoning and learning systems, Ashwin Ram et al; inference to the best plan - a coherence theory of decision, Paul Thagard and Elijah Millgram; toward goal-driven integration of explanation and action, David B. Leake; learning as goal-driven inference, Ryszard Michalski and Ashwin Ram.
