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Format: 1 online resource (xxiv, 455 pages) : , digital, PDF file(s).

ISBN: 1-316-65558-X , 1-316-38846-8

Content: This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of systems that use expert knowledge and reasoning to solve complex problems. It covers the main stages in the development of a knowledge-based system: understanding the application domain, modeling problem solving in that domain, developing the ontology and the reasoning rules, and testing the system. The book focuses on a special class of systems - learning assistants for evidence-based reasoning that learn complex problem solving expertise directly from human experts, support experts and non-experts in problem solving and decision making, and teach their problem solving expertise to students. A powerful learning agent shell, Disciple-EBR, is included with the book, enabling students, practitioners, and researchers to rapidly develop learning assistants in a wide variety of domains that require evidence-based reasoning, including intelligence analysis, cyber security, law, forensics, medicine, and education.

Note: Title from publisher's bibliographic system (viewed on 06 Sep 2016). , Cover -- Half-title -- Title page -- Copyright information -- Table of contents -- Preface -- Book Purpose -- Book Contents -- Background -- Acknowledgments -- About the Authors -- 1 Introduction -- 1.1 Understanding the World through Evidence-based Reasoning -- 1.1.1 What Is Evidence? -- 1.1.2 Evidence, Data, and Information -- 1.1.3 Evidence and Fact -- 1.1.4 Evidence and Knowledge -- 1.1.5 Ubiquity of Evidence -- 1.2 Abductive Reasoning -- 1.2.1 From Aristotle to Peirce -- 1.2.2 Peirce and Sherlock Holmes on Abductive Reasoning -- 1.3 Probabilistic Reasoning -- 1.3.1 Enumerative Probabilities: Obtained by Counting -- 1.3.1.1 Aleatory Probability -- 1.3.1.2 Relative Frequency and Statistics -- 1.3.2 Subjective Bayesian View of Probability -- 1.3.3 Belief Functions -- 1.3.4 Baconian Probability -- 1.3.4.1 Variative and Eliminative Inferences -- 1.3.4.2 Importance of Evidential Completeness -- 1.3.4.3 Baconian Probability of Boolean Expressions -- 1.3.5 Fuzzy Probability -- 1.3.5.1 Fuzzy Force of Evidence -- 1.3.5.2 Fuzzy Probability of Boolean Expressions -- 1.3.5.3 On Verbal Assessments of Probabilities -- 1.3.6 A Summary of Uncertainty Methods and What They Best Capture -- 1.4 Evidence-based Reasoning -- 1.4.1 Deduction, Induction, and Abduction -- 1.4.2 The Search for Knowledge -- 1.4.3 Evidence-based Reasoning Everywhere -- 1.5 Artificial Intelligence -- 1.5.1 Intelligent Agents -- 1.5.2 Mixed-Initiative Reasoning -- 1.6 Knowledge Engineering -- 1.6.1 From Expert Systems to Knowledge-based Agents and Cognitive Assistants -- 1.6.2 An Ontology of Problem-Solving Tasks -- 1.6.2.1 Analytic Tasks -- 1.6.2.2 Synthetic Tasks -- 1.6.3 Building Knowledge-based Agents -- 1.6.3.1 How Knowledge-based Agents Are Built and Why It Is Hard -- 1.6.3.2 Teaching as an Alternative to Programming: Disciple Agents. , 1.6.3.3 Disciple-EBR, Disciple-CD, and TIACRITIS -- 1.7 Obtaining Disciple-EBR -- 1.8 Review Questions -- 2 Evidence-based Reasoning: Connecting the Dots -- 2.1 How Easy Is It to Connect the Dots? -- 2.1.1 How Many Kinds of Dots Are There? -- 2.1.2 Which Evidential Dots Can Be Believed? -- 2.1.3 Which Evidential Dots Should Be Considered? -- 2.1.4 Which Evidential Dots Should We Try to Connect? -- 2.1.5 How to Connect Evidential Dots to Hypotheses? -- 2.1.6 What Do Our Dot Connections Mean? -- 2.2 Sample Evidence-based Reasoning Task: Intelligence Analysis -- 2.2.1 Evidence in Search of Hypotheses -- 2.2.2 Hypotheses in Search of Evidence -- 2.2.3 Evidentiary Testing of Hypotheses -- 2.2.4 Completing the Analysis -- 2.3 Other Evidence-based Reasoning Tasks -- 2.3.1 Cyber Insider Threat Discovery and Analysis -- 2.3.2 Analysis of Wide-Area Motion Imagery -- 2.3.3 Inquiry-based Teaching and Learning in a Science Classroom -- 2.3.3.1 Need for Inquiry-based Teaching and Learning -- 2.3.3.2 Illustration of Inquiry-based Teaching and Learning -- 2.3.3.3 Other Examples of Inquiry-based Teaching and Learning -- 2.4 Hands On: Browsing an Argumentation -- 2.5 Project Assignment 1 -- 2.6 Review Questions -- 3 Methodologies and Tools for Agent Design and Development -- 3.1 A Conventional Design and Development Scenario -- 3.1.1 Conventional Design and Development Phases -- 3.1.2 Requirements Specification and Domain Understanding -- 3.1.3 Ontology Design and Development -- 3.1.4 Development of the Problem-Solving Rules or Methods -- 3.1.5 Verification, Validation, and Certification -- 3.2 Development Tools and Reusable Ontologies -- 3.2.1 Expert System Shells -- 3.2.2 Foundational and Utility Ontologies and Their Reuse -- 3.2.3 Learning Agent Shells -- 3.2.4 Learning Agent Shell for Evidence-based Reasoning. , 3.3 Agent Design and Development Using Learning Technology -- 3.3.1 Requirements Specification and Domain Understanding -- 3.3.2 Rapid Prototyping -- 3.3.3 Ontology Design and Development -- 3.3.4 Rule Learning and Ontology Refinement -- 3.3.5 Hierarchical Organization of the Knowledge Repository -- 3.3.6 Learning-based Design and Development Phases -- 3.4 Hands On: Loading, Saving, and Closing Knowledge Bases -- 3.5 Knowledge Base Guidelines -- Guideline 3.1. Work with only one knowledge base loaded in memory -- Guideline 3.2. Create a knowledge base and save successive versions -- 3.6 Project Assignment 2 -- 3.7 Review Questions -- 4 Modeling the Problem-Solving Process -- 4.1 Problem Solving through Analysis and Synthesis -- 4.2 Inquiry-driven Analysis and Synthesis -- 4.3 Inquiry-driven Analysis and Synthesis for Evidence-based Reasoning -- 4.3.1 Hypothesis Reduction and Assessment Synthesis -- 4.3.2 Necessary and Sufficient Conditions -- 4.3.3 Sufficient Conditions and Scenarios -- 4.3.4 Indicators -- 4.4 Evidence-based Assessment -- 4.5 Hands On: Was the Cesium Stolen? -- 4.6 Hands On: Hypothesis Analysis and Evidence Search and Representation -- 4.7 Believability Assessment -- 4.7.1 Tangible Evidence -- 4.7.2 Testimonial Evidence -- 4.7.3 Missing Evidence -- 4.7.4 Authoritative Record -- 4.7.5 Mixed Evidence and Chains of Custody -- 4.8 Hands On: Believability Analysis -- 4.9 Drill-Down Analysis, Assumption-based Reasoning, and What-If Scenarios -- 4.10 Hands On: Modeling, Formalization, and Pattern Learning -- 4.11 Hands On: Analysis Based on Learned Patterns -- 4.12 Modeling Guidelines -- Guideline 4.1. Structure the modeling process based on the agent's specification -- Guideline 4.2. Define reduction trees in natural language using simple questions -- Guideline 4.3. Identify the specific instances, the generic instances, and the constants. , Guideline 4.4. Guide the reduction by the possible need of future changes -- Guideline 4.5. Learn and reuse reduction patterns -- 4.13 Project Assignment 3 -- 4.14 Review Questions -- 5 Ontologies -- 5.1 What Is an Ontology? -- 5.2 Concepts and Instances -- 5.3 Generalization Hierarchies -- 5.4 Object Features -- 5.5 Defining Features -- 5.6 Representation of N-ary Features -- 5.7 Transitivity -- 5.8 Inheritance -- 5.8.1 Default Inheritance -- 5.8.2 Multiple Inheritance -- 5.9 Concepts as Feature Values -- 5.10 Ontology Matching -- 5.11 Hands On: Browsing an Ontology -- 5.12 Project Assignment 4 -- 5.13 Review Questions -- 6 Ontology Design and Development -- 6.1 Design and Development Methodology -- 6.2 Steps in Ontology Development -- 6.3 Domain Understanding and Concept Elicitation -- 6.3.1 Tutorial Session Delivered by the Expert -- 6.3.2 Ad-hoc List Created by the Expert -- 6.3.3 Book Index -- 6.3.4 Unstructured Interviews with the Expert -- 6.3.5 Structured Interviews with the Expert -- 6.3.6 Protocol Analysis (Think-Aloud Technique) -- 6.3.7 The Card-Sort Method -- 6.4 Modeling-based Ontology Specification -- 6.5 Hands On: Developing a Hierarchy of Concepts and Instances -- 6.6 Guidelines for Developing Generalization Hierarchies -- 6.6.1 Well-structured Hierarchies -- Guideline 6.1. Define similar siblings -- Guideline 6.2. Group similar siblings under natural concepts -- Guideline 6.3. Recognize that a single subconcept may indicate ontology incompleteness or error -- 6.6.2 Instance or Concept? -- 6.6.3 Specific Instance or Generic Instance? -- 6.6.4 Naming Conventions -- Guideline 6.4. Adopt and follow a naming convention -- Guideline 6.5. Name subconcepts based on superconcepts -- 6.6.5 Automatic Support -- 6.7 Hands On: Developing a Hierarchy of Features -- 6.8 Hands On: Defining Instances and Their Features. , 6.9 Guidelines for Defining Features and Values -- 6.9.1 Concept or Feature?Guideline 6.6. Represent well-established categories from the real world as concepts -- Guideline 6.7. Define concepts and instances to represent knowledge corresponding to n-ary relations -- 6.9.2 Concept, Instance, or Constant? -- 6.9.3 Naming of FeaturesGuideline 6.8. Define feature names that distinguish them from concept names -- 6.9.4 Automatic Support -- 6.10 Ontology Maintenance -- 6.11 Project Assignment 5 -- 6.12 Review Questions -- 7 Reasoning with Ontologies and Rules -- 7.1 Production System Architecture -- 7.2 Complex Ontology-based Concepts -- 7.3 Reduction and Synthesis Rules and the Inference Engine -- 7.4 Reduction and Synthesis Rules for Evidence-based Hypotheses Analysis -- 7.5 Rule and Ontology Matching -- 7.6 Partially Learned Knowledge -- 7.6.1 Partially Learned Concepts -- 7.6.2 Partially Learned Features -- 7.6.3 Partially Learned Hypotheses -- 7.6.4 Partially Learned Rules -- 7.7 Reasoning with Partially Learned Knowledge -- 7.8 Review Questions -- 8 Learning for Knowledge-based Agents -- 8.1 Introduction to Machine Learning -- 8.1.1 What Is Learning? -- 8.1.2 Inductive Learning from Examples -- 8.1.3 Explanation-based Learning -- 8.1.4 Learning by Analogy -- 8.1.5 Multistrategy Learning -- 8.2 Concepts -- 8.2.1 Concepts, Examples, and Exceptions -- 8.2.2 Examples and Exceptions of a Partially Learned Concept -- 8.3 Generalization and Specialization Rules -- 8.3.1 Turning Constants into Variables -- 8.3.2 Turning Occurrences of a Variable into Different Variables -- 8.3.3 Climbing the Generalization Hierarchies -- 8.3.4 Dropping Conditions -- 8.3.5 Extending Intervals -- 8.3.6 Extending Ordered Sets of Intervals -- 8.3.7 Extending Symbolic Probabilities -- 8.3.8 Extending Discrete Sets -- 8.3.9 Using Feature Definitions. , 8.3.10 Using Inference Rules.

Additional Edition: ISBN 1-107-12256-2

Language: English

Subjects: Computer Science , Economics

URL: Volltext  (lizenzpflichtig)

URL: https://doi.org/10.1017/CBO9781316388464

URL: https://doi.org/10.1017/CBO9781316388464