Sold Out
Book Categories |
Preface | ||
Pt. I | The Study of Intelligence - Foundations and Issues | 1 |
1 | The Study of Intelligence | 3 |
1.1 | Characterizing Intelligence | 6 |
1.2 | Studying Intelligence: The Synthetic Approach | 21 |
2 | Foundations of Classical Artificial Intelligence and Cognitive Science | 35 |
2.1 | Cognitive Science: Preliminaries | 35 |
2.2 | The Cognitivistic Paradigm | 39 |
2.3 | An Architecture for an Intelligent Agent | 47 |
3 | The Fundamental Problems of Classical AI and Cognitive Science | 59 |
3.1 | Real Worlds versus Virtual Worlds | 59 |
3.2 | Some Well-Known Problems with Classical Systems | 63 |
3.3 | The Fundamental Problems of Classical AI | 64 |
3.4 | Remedies and Alternatives | 74 |
Pt. II | A Framework for Embodied Cognitive Science | 79 |
4 | Embodied Cognitive Science: Basic Concepts | 81 |
4.1 | Complete Autonomous Agents | 82 |
4.2 | Biological and Artificial Agents | 99 |
4.3 | Designing for Emergence - Logic-Based and Embodied Systems | 111 |
4.4 | Explaining Behavior | 127 |
5 | Neural Networks for Adaptive Behavior | 139 |
5.1 | From Biological to Artificial Neural Networks | 140 |
5.2 | The Four or Five Basics | 143 |
5.3 | Distributed Adaptive Control | 152 |
5.4 | Types of Neural Networks | 167 |
5.5 | Beyond Information Processing: A Polemic Digression | 172 |
Pt. III | Approaches and Agent Examples | 179 |
6 | Braitenberg Vehicles | 181 |
6.1 | Motivation | 181 |
6.2 | The Fourteen Vehicles | 182 |
6.3 | Segmentation of Behavior and the Extended Braitenberg Architecture | 195 |
7 | The Subsumption Architecture | 199 |
7.1 | Behavior-Based Robotics | 201 |
7.2 | Designing a Subsumption-Based Robot | 202 |
7.3 | Examples of Subsumption-Based Architectures | 206 |
7.4 | Conclusions: The Subsumption Approach to Designing Intelligent Systems | 219 |
8 | Artificial Evolution and Artificial Life | 227 |
8.1 | Basic Principles | 230 |
8.2 | An Introduction to Genetic Algorithms: Evolving a Neural Controller for an Autonomous Agent | 234 |
8.3 | Examples of Artificially Evolved Agents | 240 |
8.4 | Toward Biological Plausibility: Cell Growth from Genome-Based Cell-to-Cell Communication | 250 |
8.5 | Real Robots, Evolution of Hardware, and Simulation | 255 |
8.6 | Artificial Life: Additional Examples | 260 |
8.7 | Methodological Issues and Conclusions | 270 |
9 | Other Approaches | 277 |
9.1 | The Dynamical Systems Approach | 277 |
9.2 | Behavioral Economics | 283 |
9.3 | Schema-Based Approaches | 292 |
Pt. IV | Principles of Intelligent Systems | 297 |
10 | Design Principles of Autonomous Agents | 299 |
10.1 | The Nature of the Design Principles | 299 |
10.2 | Design Principles for Autonomous Agents | 302 |
10.3 | Design Principles in Context | 318 |
11 | The Principle of Parallel, Loosely Coupled Processes | 327 |
11.1 | Control Architectures for Autonomous Agents | 330 |
11.2 | Traditional Views on Control Architectures | 337 |
11.3 | Parallel, Decentralized Approaches | 345 |
11.4 | Case Study: A Self-Sufficient Garbage Collector | 357 |
12 | The Principle of Sensory-Motor Coordination | 377 |
12.1 | Categorization: Traditional Approaches | 378 |
12.2 | The Sensory-Motor Coordination Approach | 392 |
12.3 | Case Study: The SMC Agents | 407 |
12.4 | Application: Active Vision | 431 |
13 | The Principles of Cheap Design, Redundancy, and Ecological Balance | 435 |
13.1 | The Principle of Cheap Design | 435 |
13.2 | The Redundancy Principle | 446 |
13.3 | The Principle of Ecological Balance | 455 |
14 | The Value Principle | 467 |
14.1 | Value Systems | 469 |
14.2 | Self-Organization | 475 |
14.3 | Learning in Autonomous Agents | 485 |
15 | Human Memory: A Case Study | 503 |
15.1 | Memory Defined | 503 |
15.2 | Problems of Classical Notions of Memory | 506 |
15.3 | The Frame-of-Reference Problem in Memory Research | 511 |
15.4 | The Alternatives | 516 |
15.5 | Implications for Memory Research | 530 |
Pt. V | Design and Evaluation | 535 |
16 | Agent Design Considerations | 537 |
16.1 | Preliminary Design Considerations | 539 |
16.2 | Agent Design | 542 |
16.3 | Putting It All Together: Control Architectures | 562 |
16.4 | Summary and a Fundamental Issue | 569 |
17 | Evaluation | 577 |
17.1 | General Introduction | 578 |
17.2 | Performing Agent Experiments | 588 |
17.3 | Measuring Behavior | 593 |
Pt. VI | Future Directions | 605 |
18 | Theory, Technology, and Applications | 607 |
18.1 | Hard Problems | 607 |
18.2 | Theory and Technology | 612 |
18.3 | Applications | 618 |
19 | Intelligence Revisited | 631 |
19.1 | Elements of a Theory of Intelligence | 631 |
19.2 | Implications for Society | 638 |
Glossary | 645 | |
References | 659 | |
Author Index | 677 | |
Subject Index | 681 |
Login|Complaints|Blog|Games|Digital Media|Souls|Obituary|Contact Us|FAQ
CAN'T FIND WHAT YOU'RE LOOKING FOR? CLICK HERE!!! X
You must be logged in to add to WishlistX
This item is in your Wish ListX
This item is in your CollectionUnderstanding Intelligence
X
This Item is in Your InventoryUnderstanding Intelligence
X
You must be logged in to review the productsX
X
X
Add Understanding Intelligence, By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run programs; it does something en, Understanding Intelligence to the inventory that you are selling on WonderClubX
X
Add Understanding Intelligence, By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run programs; it does something en, Understanding Intelligence to your collection on WonderClub |