Qualitative representations : how people reason and learn about the continuous world / Kenneth D. Forbus.

Format:

Book

Author/Creator:

Forbus, Kenneth D., author.

Series:

MIT Press.

MIT Press

Language:

English

Subjects (All):

Cognitive science.

Qualitative reasoning.

Physical Description:

1 online resource (432 pages).

Other Title:

MIT Press CogNet.

Place of Publication:

Cambridge : The MIT Press, 2018.

System Details:

text file

Summary:

An argument that qualitative representations -- symbolic representations that carve continuous phenomena into meaningful units -- are central to human cognition. In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations -- symbolic representations that carve continuous phenomena into meaningful units -- are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representations a useful component of natural language semantics. Qualitative representations also provide a foundation for expert reasoning in science and engineering by making explicit the broad categories of things that might happen and enabling causal models that help guide the application of more quantitative knowledge as needed. Qualitative representations are important for creating more human-like artificial intelligence systems with capabilities for spatial reasoning, vision, question answering, and understanding natural language. Forbus discusses, among other topics, basic ideas of knowledge representation and reasoning; qualitative process theory; qualitative simulation and reasoning about change; compositional modeling; qualitative spatial reasoning; and learning and conceptual change. His argument is notable both for presenting an approach to qualitative reasoning in which analogical reasoning and learning play crucial roles and for marshaling a wide variety of evidence, including the performance of AI systems. Cognitive scientists will find Forbus's account of qualitative representations illuminating; AI scientists will value Forbus's new approach to qualitative representations and the overview he offers.

Contents:

I Introduction and Preliminaries p. 1

1.1 Some Examples of Everyday Qualitative Reasoning p. 4

1.1.1 Heating Water p. 4

1.1.2 Does Cold Water Freeze Faster Than Warm Water? p. 5

1.1.3 The Seasons p. 5

1.1.4 Will These Collide? p. 6

1.1.5 Raven's Progressive Matrices p. 7

1.1.6 Moral Decision Making p. 8

1.2 The Importance of Qualitative Reasoning in Human Cognition p. 8

2 Representation: An Overview p. 13

2.1 The Importance of Structured, Relational Representations p. 13

2.2 Logic, Formalism, and Precision p. 14

2.2.1 Syntax p. 14

2.3 Schemas, Frames, and Cases p. 19

2.4 Ontologies and Knowledge Bases p. 20

2.5 Richness and Structure of Predicate Vocabularies p. 22

2.6 Summary: Evaluating Representations p. 23

3 Reasoning: An Overview p. 25

3.1 Computational Complexity and Tractability p. 25

3.2 Deduction, Abduction, and Induction p. 27

3.3 Pattern Matching and Unification p. 32

3.3.1 Storing and Retrieving Knowledge p. 33

3.4 Closed-World Assumptions p. 33

3.5 Probability p. 34

4 Analogy p. 35

4.1 Some Psychologically Motivated Representation Conventions p. 35

4.2 Structure-Mapping Theory p. 36

4.3 Psychological Support for Structure-Mapping Theory p. 42

4.4 Computational Models of Analogical Processing p. 43

4.4.2 Retrieval p. 46

4.4.3 Generalization p. 48

4.5 The Centrality of Analogy in Human Cognition p. 50

II Dynamics p. 53

5 Quantity p. 55

5.1 The Reals p. 56

5.2 Finite Approximations to the Reals p. 58

5.3 Finite Algebras and Fuzzy Logic p. 59

5.4 Signs p. 60

5.5 Ordinal Relations p. 61

5.6 Numerical Intervals p. 62

5.7 Order of Magnitude p. 63

5.8 Infinitesimals p. 64

5.9 Status Values p. 65

6 Relationships between Quantities p. 69

6.1 Why Qualitative Mathematics? p. 69

6.1.1 Soundness p. 70

6.1.2 Minimal Knowledge p. 71

6.1.3 Causality p. 72

6.2 Qualitative Mathematics in QP Theory p. 73

6.2.1 Direct Influences p. 73

6.2.2 Indirect Influences p. 75

6.2.3 Compositionality and Graceful Extension of Knowledge p. 77

6.2.4 Specifying Additional Information about Relationships p. 79

6.3 Naturalness p. 81

6.4 Expressiveness p. 82

6.5 Confluences and Causal Ordering p. 85

7 Qualitative Process Theory p. 89

7.1 Modeling the Modeling Process p. 90

7.2 Model Fragments p. 92

7.3 The Ontology of QP Theory p. 95

7.4 Basic Inferences of QP Theory p. 98

7.4.1 Model Formulation p. 98

7.4.2 Determining Activity p. 99

7.4.3 Resolving influences p. 100

7.4.4 Limit Analysis p. 103

7.5 Encapsulated Histories p. 110

8 Examples Using QP Theory p. 113

8.1 Modeling Fluids p. 113

8.2 Existence and Why It Matters p. 114

8.3 Representing Contained Liquids p. 118

8.4 Representing Gases p. 120

8.5 Phase Changes p. 123

8.6 Boiling Water and Its Consequences p. 128

8.7 Ice Cubes in Freezers, Revisited p. 131

8.8 Modeling Motion p. 133

8.9 Modeling Materials p. 136

8.10 Modeling an Oscillator p. 144

8.11 Analyzing Stability p. 148

9 Causality p. 153

9.1 What Is Causality? p. 153

9.2 Causality in QP Theory p. 156

9.3 Causality via Propagation p. 160

9.3.1 Causality in Confluence Models p. 160

9.3.2 Causal Ordering p. 161

9.4 Other Notions of Causality in Cognitive Science p. 163

10 Qualitative Simulation and Reasoning about Change p. 165

10.1 Qualitative Simulation p. 165

10.2 Existence and Continuity p. 168

10.3 Correctness of Qualitative Reasoning p. 171

10.3.1 Phase Space p. 172

11 Modeling p. 177

11.1 Example: A Steam Propulsion Plant p. 178

11.2 Compositional Modeling p. 183

11.2.1 Modeling Criteria p. 184

11.2.2 Representing Modeling Assumptions and Constraints p. 185

11.2.3 Structural Abstractions p. 191

11.3 Model Formulation Algorithms p. 192

11.4 How Might People Do Model Formulation? p. 193

12 Analogy in Dynamics p. 197

12.1 Mental Models and Runnability p. 197

12.2 Human Qualitative Reasoning: First Principles or Analogical? p. 200

12.2.1 Remembered Experience Model p. 203

12.2.2 Partial Generalization Model p. 204

12.2.3 Causally Annotated Experience Model p. 204

12.2.4 Generic Domain Theory p. 205

12.3 Similarity-Based Qualitative Simulation p. 206

12.3.1 A Prototype Similarity-Based Qualitative Simulator p. 206

12.4 Psychological Implications p. 214

12.4.1 Distribution of Reliance on Memory with Expertise p. 214

12.4.2 Differences in Novice/Expert Retrieval Patterns p. 215

12.4.3 Factors That Should Promote Expertise p. 215

13 Dynamics in Language p. 219

13.2 Recasting Qualitative Representations as Linguistic Frames p. 220

13.3 How QP Theory Manifests in English p. 221

13.3.1 Quantities p. 221

13.3.2 Ordinal Relationships p. 225

13.3.3 Influences p. 226

13.3.4 Model Fragments and Processes p. 228

13.4 Evidence p. 229

13.4.1 Corpus Analysis p. 230

13.4.2 Compatibility with Other Aspects of Semantics p. 231

13.4.3 Natural-Language Understanding Examples p. 232

13.5 Other Accounts p. 234

III Space p. 235

14 Qualitative Spatial Reasoning: A Theoretical Framework p. 237

14.1 Reasoning about Motion through Space p. 237

14.2 The Metric Diagram/Place Vocabulary Model p. 242

14.2.1 The Poverty Conjecture p. 243

14.3 Other Examples of the MD/PV Model p. 245

14.4 Categorical/Coordinate Models in Psychology p. 247

14.5 A Unified Account p. 249

15 Qualitative Spatial Calculi p. 251

15.1 Example: Region Connection Calculus p. 251

15.2 A Collection of Calculi p. 255

15.2.1 Intersection Models of Topology p. 255

15.2.2 Distance Calculi p. 258

15.2.3 Orientation Calculi p. 258

15.3 Reasoning Issues p. 261

16 Understanding Sketches and Diagrams p. 265

16.1 Investigations of Sketching and Diagrams p. 266

16.2 The nuSketch Model of Sketch Understanding p. 267

16.3 CogSketch: Representations and Processing p. 270

16.4 Learning Spatial Prepositions p. 275

16.5 Reasoning about Depiction p. 278

16.6 Modeling Visual Problem Solving p. 285

16.6.1 Geometric Analogies p. 289

16.6.2 Raven's Matrices p. 290

16.6.3 Oddity Task p. 293

16.6.4 What Makes an Effective Visual Problem Solver? p. 294

IV Learning and Reasoning p. 297

17 Learning and Conceptual Change p. 299

17.1 A Framework for Mental Models in Physical Domains p. 299

17.2 Learning Protohistories p. 301

17.3 Constructing First-Principles Knowledge via Protohistory Statistics p. 307

17.4 Distributed Knowledge, Explanation Structure, and Conceptual Change p. 311

17.5 Learning via Cross-Domain Analogies p. 317

18 Commonsense Reasoning p. 321

18.1 How Common Sense Doesn't Work p. 322

18.2 Some Psychological Considerations Concerning Common Sense p. 325

18.3 Quantitative Aspects of Common Sense p. 329

18.3.1 Analogical Estimation of Numerical Values p. 330

18.3.2 Qualitative Representations Can Enhance Similarity p. 331

18.3.3 Strategies for Bock-of-the-Envelope Reasoning p. 333

18.3.4 How Well Does This Model Do? p. 335

18.4 Qualitative Representations in Conceptual Metaphors p. 335

38.4 Social Reasoning p. 337

18.5.1 Modeling Aspects of Emotions p. 337

18.5.2 Blame Assignment p. 339

18.5.3 Moral Decision Making p. 341

19 Expert Reasoning p. 349

19.1 Engineering Reasoning p. 351

19.1.2 Monitoring, Control, and Diagnosis p. 356

19.1.3 Design p. 360

19.1.4 System Identification p. 361

19.2 Scientific Modeling p. 362

V Summary and New Directions p. 367

20.1 Bridge between Perception and Cognition p. 369

20.2 Basis for Commonsense Reasoning p. 369

20.3 Foundation for Expert Reasoning p. 370

21 New Directions p. 371

21.1 Formalizing Discrete Processes and Their Interactions with Continuous Processes p. 371

21.2 Qualitative Vision p. 371

21.3 Qualitative Representations in Other Modalities p. 372

21.4 Qualitative Representations in Semantics p. 372

21.5 Qualitative Representations in Robotics p. 373

21.6 Cataloging the Range of Human Mental Models and Ontologies p. 373

21.7 Qualitative Representations for Social Science p. 374

21.8 Qualitative Representations in Cognitive Architecture p. 375

21.9 Multimodal Science Learning and Teaching p. 376.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9780262349802

0262349809

OCLC:

1035389804

Access Restriction:

Restricted for use by site license.

Online:

OCLC metadata license agreement Connect to full text