Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning

Sep 01, 2012

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Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning

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Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning

ECAI 2008. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning. Martin Mozina, Matej Guid, Jana Krivec, Aleksander Sadikov and Ivan Bratko. Faculty of Computer and Information Science University of Ljubljana, Slovenia.

540 views • 26 slides

Knowledge-Based Discovery: Using Semantics in Machine Learning

Knowledge-Based Discovery: Using Semantics in Machine Learning. Bruce Buchanan Joe Phillips University of Pittsburgh. buchanan @ cs.pitt.edu josephp @ cs.pitt.edu. Intelligent Systems Laboratory. Faculty: Bruce Buchanan, P.I., John Aronis

402 views • 31 slides

Knowledge Acquisition

Knowledge Acquisition. PHE 498. Epistemology. A branch of philosophy which deals with the basic question: “How do we come to know”. Knowledge acquisition applies some form of SYSTEM. That is, there is some form of systematic application to impart knowledge. Sources of Knowledge. Experience

272 views • 8 slides

Machine Learning: Symbol-based

10b . Machine Learning: Symbol-based. 10.0 Introduction 10.1 A Framework for Symbol-based Learning 10.2 Version Space Search 10.3 The ID3 Decision Tree Induction Algorithm 10.4 Inductive Bias and Learnability. 10.5 Knowledge and Learning 10.6 Unsupervised Learning

628 views • 50 slides

Knowledge Acquisition

Knowledge Acquisition. Professor Nigel Shadbolt. Learning Objectives. Appreciate the maj0r features of expertise Be able to describe at least four families of KA technique Appreciate the pros and cons of various techniques Familiarity with material in “Eliciting Expertise”

606 views • 39 slides

Knowledge Acquisition

Knowledge Acquisition. Graphic Era Hill University, Dehradun. Outline. Introduction Scopes of knowledge Knowledge Acquisition Problems in Knowledge Acquisition Knowledge Acquisition Techniques Structuring Knowledge Graphically. Introduction.

1.02k views • 47 slides

Knowledge Representation and Machine Learning

Knowledge Representation and Machine Learning. Stephen J. Guy. Overview. Recap some Knowledge Rep. History First order logic Machine Learning ANN Bayesian Networks Reinforcement Learning Summary. Knowledge Representation?. Ambiguous term

505 views • 37 slides

Machine Learning: Symbol-based

9b. Machine Learning: Symbol-based. 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version Space Search 9.3 The ID3 Decision Tree Induction Algorithm 9.4 Inductive Bias and Learnability. 9.5 Knowledge and Learning 9.6 Unsupervised Learning 9.7 Reinforcement Learning

715 views • 48 slides

Machine Learning: Symbol-based

10d. Machine Learning: Symbol-based. More clustering examples. 10.5 Knowledge and Learning 10.6 Unsupervised Learning 10.7 Reinforcement Learning 10.8 Epilogue and References 10.9 Exercises. Additional references for the slides:

547 views • 29 slides

The Knowledge Acquisition Bottleneck Revisited: How can we build large KBs?

The Knowledge Acquisition Bottleneck Revisited: How can we build large KBs?. Illustrations of different approaches Peter Clark and John Thompson Boeing Research 2004. Premise. Intelligent machines needs lots of knowledge , for question-answering intelligent search information integration

499 views • 12 slides

Knowledge – based learning

Knowledge – based learning. GROUP 1 – Feedback to the Rwanda team. Objective 1. Establish knowledge based project Generate necessary research to inform project analysis and decision making. Add. Production Assess income and profitability at farm level

208 views • 6 slides

Knowledge Acquisition

Knowledge Acquisition. CIS 479/579 Bruce R. Maxim UM-Dearborn. Architectural Principles. Knowledge is power Knowledge is often inexact & incomplete Knowledge is often poorly specified Amateurs become experts slowly Expert systems must be flexible Expert systems must be transparent

407 views • 22 slides

Machine Learning: Symbol-based

10a. Machine Learning: Symbol-based. 10.0 Introduction 10.1 A Framework for Symbol-based Learning 10.2 Version Space Search 10.3 The ID3 Decision Tree Induction Algorithm 10.4 Inductive Bias and Learnability. 10.5 Knowledge and Learning 10.6 Unsupervised Learning

877 views • 54 slides

Knowledge Acquisition

Knowledge Acquisition. Knowledge Aquisition. Definition – The process of acquiring, organising, & studying knowledge. Identified by many researchers and practitioners (in particular Feigenbaum) as the bottleneck in ES development. Knowledge Aquisition.

285 views • 13 slides

Knowledge Acquisition

Knowledge Acquisition. Concepts of Knowledge Engineering. Knowledge engineering The engineering discipline in which knowledge is integrated into computer systems to solve complex problems that normally require a high level of human expertise. Concepts of Knowledge Engineering.

2.26k views • 47 slides

Knowledge Acquisition

Knowledge Acquisition. How to Study Effectively. Rehearsal Strategies. Rehearsal strategies are used for memorizing when we are interested primarily in putting information into short-term memory (for example, the night before the exam). Rehearsal Strategies.

206 views • 17 slides

Knowledge Acquisition

115 views • 8 slides

Machine Learning: Symbol-based

10c. Machine Learning: Symbol-based. 10.0 Introduction 10.1 A Framework for Symbol-based Learning 10.2 Version Space Search 10.3 The ID3 Decision Tree Induction Algorithm 10.4 Inductive Bias and Learnability. 10.5 Knowledge and Learning 10.6 Unsupervised Learning

367 views • 35 slides

Machine Learning: Symbol-based

10d. Machine Learning: Symbol-based. 10.0 Introduction 10.1 A Framework for Symbol-based Learning 10.2 Version Space Search 10.3 The ID3 Decision Tree Induction Algorithm 10.4 Inductive Bias and Learnability. 10.5 Knowledge and Learning 10.6 Unsupervised Learning

312 views • 29 slides

Machine Learning: Symbol-based

323 views • 29 slides

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