Posts Tagged ‘goal-driven learning’

7 Aug

Invention as an Opportunistic Enterprise

Posted by cognitivecomputing in Learning. Tagged: , , .

This paper identifies goal handling processes that begin to account for the kind of processes involved in invention. We identify new kinds of goals with special properties and mechanisms for processing such goals, as well as means of integrating opportunism, deliberation, and social interaction into goal/plan processes. We focus on invention goals, which address significant enterprises associated with an inventor. Invention goals represent “seed” goals of an expert, around which the whole knowledge of an expert gets reorganized and grows more or less opportunistically. Invention goals reflect the idiosyncrasy of thematic goals among experts. They constantly increase the sensitivity of individuals for particular events that might contribute to their satisfaction.

Our exploration is based on a well-documented example: the invention of the telephone by Alexander Graham Bell. We propose mechanisms to explain: (1) how Bell’s early thematic goals gave rise to the new goals to invent the multiple telegraph and the telephone, and (2) how the new goals interacted opportunistically. Finally, we describe our computational model, ALEC, that accounts for the role of goals in invention.

Invention as an Opportunistic Enterprise

by Marin Simina, Janet Kolodner, Ashwin Ram, Michael Gorman

19th Annual Conference of the Cognitive Science Society, Stanford, CA, August 1997
www.cc.gatech.edu/faculty/ashwin/papers/git-cs-97-04.pdf

25 Jul

Case-Based Planning to Learn

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

Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework.

Read the paper:

Case-Based Planning to Learn

by Bill Murdock, Gordon Shippey, Ashwin Ram

2nd International Conference on Case-Based Reasoning (ICCBR-97), Providence, RI, July 1997
www.cc.gatech.edu/faculty/ashwin/papers/er-97-04.pdf

13 Aug

Multi-Plan Retrieval and Adaptation in an Experience-Based Agent

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

The real world has many properties that present challenges for the design of intelligent agents: it is dynamic, unpredictable, and independent, poses poorly structured problems, and places bounds on the resources available to agents. Agents that opearate in real worlds need a wide range of capabilities to deal with them: memory, situation analysis, situativity, resource-bounded cognition, and opportunism.

We propose a theory of experience-based agency which specifies how an agent with the ability to richly represent and store its experiences could remember those experiences with a context-sensitive, asynchronous memory, incorporate those experiences into its reasoning on demand with integration mechanisms, and usefully direct memory and reasoning through the use of a utility-based metacontroller. We have implemented this theory in an architecture called NICOLE and have used it to address the problem of merging multiple plans during the course of case-based adaptation in least-committment planning.

Read the paper:

Multi-Plan Retrieval and Adaptation in an Experience-Based Agent

by Ashwin Ram, Anthony Francis

In Case-Based Reasoning: Experiences, Lessons, and Future Directions, D.B. Leake, editor, AAAI Press, 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-06.pdf

24 Jul

The Role of Student Tasks in Accessing Cognitive Media Types

Posted by cognitivecomputing in Education. Tagged: , , .

We believe that identifying media by their cognitive roles (e.g., definition, explanation, pseudo-code, visualization) can improve comprehension and usability in hypermedia systems designed for learning. We refer to media links organized around their cognitive role as cognitive media types (Recker, Ram, Shikano, Li, & Stasko, 1995). Our hypothesis is that the goals that students bring to the learning task will affect how they will use the hypermedia support system (Ram & Leake, 1995).

We explored student use of a hypermedia system based on cognitive media types where students performed different orienting tasks: undirected, browsing in order to answer specific questions, problem-solving, and problem-solving with prompted self-explanations. We found significant differences in use behavior between problem-solving and browsing students, though no learning differences.

Read the paper:

The Role of Student Tasks in Accessing Cognitive Media Types

by Mike Byrne, Mark Guzdial, Preetha Ram, Rich Catrambone, Ashwin Ram, John Stasko, Gordon Shippey, Florian Albrecht

Second International Conference on the Learning Sciences (ICLS-96), Evanson, IL, July 1996
www.cc.gatech.edu/faculty/ashwin/papers/er-96-03.pdf

15 May

Introspective Multistrategy Learning: Constructing a Learning Strategy under Reasoning Failure

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

The thesis put forth by this dissertation is that introspective analyses facilitate the construction of learning strategies. Furthermore, learning is much like nonlinear planning and problem solving. Like problem solving, it can be specified by a set of explicit learning goals (i.e., desired changes to the reasoner’s knowledge); these goals can be achieved by constructing a plan from a set of operators (the learning algorithms) that execute in a knowledge space. However, in order to specify learning goals and to avoid negative interactions between operators, a reasoner requires a model of its reasoning processes and knowledge.

With such a model, the reasoner can declaratively represent the events and causal relations of its mental world in the same manner that it represents events and relations in the physical world. This representation enables introspective self-examination, which contributes to learning by providing a basis for identifying what needs to be learned when reasoning fails. A multistrategy system possessing several learning algorithms can decide what to learn, and which algorithm(s) to apply, by analyzing the model of its reasoning. This introspective analysis therefore allows the learner to understand its reasoning failures, to determine the causes of the failures, to identify needed knowledge repairs to avoid such failures in the future, and to build a learning strategy (plan).

Thus, the research goal is to develop both a content theory and a process theory of introspective multistrategy learning and to establish the conditions under which such an approach is fruitful. Empirical experiments provide results that support the claims herein. The theory was implemented in a computational model called Meta-AQUA that attempts to understand simple stories. The system uses case-based reasoning to explain reasoning failures and to generate sets of learning goals, and it uses a standard non-linear planner to achieve these goals.

Evaluating Meta-AQUA with and without learning goals generated results indicating that computational introspection facilitates the learning process. In particular, the results lead to the conclusion that the stage that posts learning goals is a necessary stage if negative interactions between learning methods are to be avoided and if learning is to remain effective.

Read the thesis:

Introspective multistrategy learning: Constructing a learning strategy under reasoning failure

by Michael T. Cox

PhD Thesis, Technical Report GIT-CC-96/06, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1996
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-96-06.pdf

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

1 Mar

Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real-World Problem Solving Task

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

This article presents a computational model of the learning of diagnostic knowledge based on observations of human operators engaged in a real-world troubleshooting task. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem solving task, 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. The model is implemented in a computer system which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model is intended as a computational model of human learning; in addition, it is computationally justified as a uniform, extensible framework for multistrategy learning.

Read the paper:

Learning to Troubleshoot: Multistrategy Learning of Diagnostic Knowledge for a Real-World Problem Solving Task

by Ashwin Ram, S Narayanan, Mike Cox

Cognitive Science journal, 19(3):289-340, 1995
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-93-67.pdf
« Older Entries
Newer Entries »