Robots, reasoning, and reification / by J.P. Gunderson, L.F. Gunderson.

Format:

Book

Author/Creator:

Gunderson, James Patrick.

Contributor:

Gunderson, L. F. (Louise F.)

Language:

English

Subjects (All):

Autonomous robots.

Reification.

Reasoning.

Physical Description:

xv, 201 pages : illustrations ; 24 cm

Place of Publication:

New York ; London : Springer, [2009]

Summary:

Robots, Reasoning, and Reification focuses on a critical obstacle that is preventing the development of intelligent, autonomous robots: the gap between the ability to reason about the world and the ability to sense the world and translate that sensory data into a symbolic model.

This abilit is what enables living systems to look at the world and perceive the things in it. In addition, intelligent living systems can extrapolate from their mental models and predict the effects of their actions in the real world. The authors call this bi-directional mapping of sensor data to symbols and symbolic manipulation onto real world effects reification. After exploring the gulf between bottom-up and top-down approaches to autonomous robotics, the book develops the concepts of reification from biologically based premises, and follows the development into the necessary components and structures that can be used to provide equivalent capabilities for intelligent robots. It continues by demonstrating how the reification engine supports both learning from experience and creating new behaviors and representations of the world.

Contents:

1.1 Bridging the Gap 2

1.1.1 Bidirectional Mapping 3

1.2 Reification and Preafference in Biological Entities 4

1.3 More Advanced Brains 5

1.4 What This Book Is and What It Is Not 6

1.6 A Note on Typefaces and Terminology 7

1.6.1 Anthropomorphization 8

2 Some background material on probability and biology 9

2.1 Layout 9

2.2 Probability in the Real World 10

2.3 Why a Biologically Principled Argument? 11

2.3.1 Biological Principles 13

2.4 What Is a Biologically Principled Argument? 14

2.4.1 Biology Is an Observational Science 14

2.4.2 Life Has Structure 16

2.4.3 The Theory of Evolution Explains the Observed Diversity of Life 17

2.5 So Why Is Our Model Biologically Principled? 18

2.5.1 Why Not Just Use Expected Value? 19

3 Using Cognition and Physiology to Build a Cognitive Model 21

3.1 Reification in Biological Entities 21

3.1.1 Recognition 22

3.1.2 Preafference 23

3.2 Biological Storage 26

3.2.1 Explicit Memory 27

3.3 Emotion 29

3.3.1 Emotion as mediator 29

4 Representation 31

4.1 Representing Features of the World 33

4.2 Representing Goals 34

4.3 Representing Actions in the World 35

4.3.1 Enabling Conditions 35

4.3.2 Outcomes 35

4.3.3 Representing Likelihoods 36

4.4 Exogenous Events 37

5 Perception/Action System 39

5.1 Robot as Perception/Action System 40

5.1.1 Robot as Body 41

5.1.2 Robot as Senor 42

5.1.3 Robot as Agent of Change 43

5.1.4 Low Level Control Loop - Procedural Memory 45

5.1.5 System Safety and Routine Actions 47

5.2 Examples of Perception/Action Systems 47

5.2.1 Fred - a simple test robot 47

5.2.2 Basil 51

5.3 Summary of Perception/Action Systems 54

6 Design of a Reification Engine 57

6.1 Model Selection Criteria 57

6.2 Judgment Analysis 59

6.3 Designing the Reification Engine 62

7 Bridging the Sensor to Symbol Gap 65

7.1 Supporting Bidirectional Mapping 66

7.1.1 A Third Approach 68

7.2 Reification Architecture 68

7.3 PerCepts and Reification 71

7.3.1 PerCept Data 73

7.3.2 PerCept Function 76

7.4 Mental Model 77

7.5 Current World State 79

7.6 Reification functionality 80

7.6.1 Initialization 80

7.6.2 Mapping the World onto its Model - Recognition 81

7.6.3 Projecting the Model onto the World - Preafference 82

7.6.4 Updating the Current World State 84

7.7 Wrapping Up Reification 84

8 Working Memory and the Construction of Personal Experiences 87

8.1 Transient Memory 88

8.1.1 Working Memory and the Current World State 89

8.1.2 Internal State 92

8.2 Episodic Memory 92

8.2.1 Emotive Tags 96

8.3 Memory Services 97

8.4 Providing Memory Services to the Reification Process 98

8.5 Memory, What Was That Again? 98

9 Semantic Memory and the Personal Rough Ontology 101

9.1 Semantic Memory 101

9.1.1 What is a Personal Rough Ontology? 102

9.2 Building Semantic Memory 104

9.2.1 Structure of the Ontology 106

9.2.2 The nodes in the multi-graph 107

9.2.3 Relationships, the Edges of the Graph 112

9.2.4 A Note on Representing Probabilities 114

9.3 Persistent Storage in the Personal Rough Ontology 115

9.4 Transient versus Persistent Knowledge 115

9.5 Extracting Problems for the Deliberative System 117

9.6 Focusing Attention by Finding Sub-Ontologies 117

9.6.1 Weighted Transitivity 118

10 Deliberative System 121

10.1 Deliberation 122

10.2 Reasoning About the Present 124

10.2.1 Sense-Symbols from the Reification Engine 125

10.2.2 Symbols from the Ontology 125

10.2.3 Internal State 126

10.2.4 Reasoning with WorldSets 126

10.3 Choosing the Future 128

10.3.1 Planning as Search 129

10.3.2 Adapting to Failure 134

10.4 Plan Evaluation and Selection 135

10.4.1 Acquiring Distributions 135

10.4.2 Simulator Fidelity 136

11 Putting it All Together 139

11.1 How it Fits Together 139

11.2 Goals and Environment 141

11.3 Knowledge Sources 143

11.3.1 Ontological Knowledge 144

11.3.2 Reification Knowledge 148

11.3.3 Perception/Action Knowledge 148

11.4 The process 149

11.4.1 Perception/Action 149

11.4.2 Reification 149

11.4.3 Execution 149

11.4.4 Deliberation 150

11.4.5 Execution, Reification and Action 151

11.4.6 Perception/Action - Reflex 152

11.4.7 Execution Failure 152

11.4.8 Back Up to Deliberation 152

11.4.9 Procedural Memory and Localization 153

11.5 A Few Notes About the General Flow 154

12 Testing 157

12.1 Testing the Robot, or How Does One Test an Embedded System? 157

12.2 eXtreme Programming 158

12.3 Methodology for Testing Embodied Systems 159

12.3.1 Benefits of Partitioning the Tests 161

12.4 General Testing Guidelines 162

12.4.1 General Partitioning Guidelines 162

12.5 Testing in the lab 163

12.5.1 Hardware 163

12.5.2 Static Tests 164

12.5.3 Dynamic tests 167

12.6 Formal System Tests - Testing In The Real World 169

12.6.1 Testing Recognition 169

12.6.2 Testing Preafference 174

12.6.3 Testing Self-Localization 179

13 Where do we go from here 183

13.1 A Stopping Point 183

13.2.1 Adding Learning to the Model 185

13.2.2 Adding Additional Data Sources 185

13.2.3 Porting the Brain into New Bodies 186.

ISBN:

9780387874876

0387874879

OCLC:

268931373