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Robust Intelligent Systems

:	Alfons Schuster
:	Alfons Schuster
:	Robust Intelligent Systems
:	Springer-Verlag
:	9781848002616
:	1
:	CHF 139.10
:

:	Anwendungs-Software
:	English

:	299
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Our time recognizes robustness as an important, all-pervading feature in the world around us. Despite its omnipresence, robustness is not entirely understood, rather dif?cult to de?ne, and, despite its obvious value in many situations, rather dif?cult to achieve. One of the goals of this edited book is to report on the topic of robustness from a variety and diverse range of ?elds and perspectives. We are interested, for instance, in fundamental strategies nature applies to make systems robust-and arguably 'intelligent'-and how these strategies may hold as general design principles in modern technology. A particular focus is on computer-based systems and appli- tions. This in mind, the book has four main sections: Part I has a look at robustness in terms of underlying technologies and infrastr- tures upon which many computer-based 'intelligent' systems reside and inves- gates robustness on the hardware and software level, but also in larger environments such as the Internet and self-managing systems. The contributions in Part II target robustness in research areas that are inspired by biology, including brain-computer interfaces, biological networks, and biological immune systems, for example. Part III involves the exciting ?eld of arti?cial intelligence. The chapters here discuss the value of robustness as a general design principle for arti?cial intelligence, stressing its potential in areas such as humanoid robotics and image processing.

	Preface	5
	Contents	7
	Contributors	9
	Part I Robustness in Computer Hardware, Software, Networks, and Protocols	13
	Robustness in Digital Hardware	14
	1.1 Introduction	14
	1.2 Digital Hardware Technologies	16
	1.3 Issues with Testing and Verifying Digital Hardware	20
	1.4 Robustness Approaches	23
	1.5 Summary	30
	References	30
	Multiagent-Based Fault Tolerance Management for Robustness	34
	2.1 Introduction	34
	2.2 Software Dependability	37
	2.3 Robust Software	38
	2.4 Multiagent-Based Fault Tolerance Management	43
	2.5 Discussion	49
	References	49
	A Two-Level Robustness Model for Self- Managing Software Systems	54
	3.1 Introduction	54
	3.2 PERFECT Software	56
	3.3 Two-Level Robustness Model	60
	3.4 Examples	62
	3.5 Additional Thoughts on Self-Managing Systems and Software	68
	3.6 Conclusion	69
	References	69
	Robustness in Network Protocols and Distributed Applications of the Internet	72
	4.1 Introduction	72
	4.2 How the InternetWorks	73
	4.3 Measuring the Internet’s Robustness	80
	4.4 A Closer Look at the Internet Protocols	81
	4.5 Wireless Networks	84
	4.6 Distributed Applications	87
	4.7 Summary	92
	References	94
	Part II Robustness in Biology Inspired Systems	98
	Detecting Danger: The Dendritic Cell Algorithm	100
	5.1 Introduction	100
	5.2 Biological Inspiration	101
	5.3 Abstract Model	103
	5.4 The Dendritic Cell Algorithm	112
	5.5 Applications: Past and Present	120
	5.6 Conclusions	121
	References	122
	Non-invasive Brain-Computer Interfaces for Semi- autonomous Assistive Devices	124
	6.1 Introduction	124
	6.2 Modern Brain-Computer Interfaces	125
	6.3 A Robust BCI Based on SSVEP	133
	6.4 New Semi-autonomous Applications for BCIs	136
	6.5 Conclusion and Future Prospects	144
	References	146
	Robust Learning of High-dimensional Biological Networks with Bayesian Networks	150
	7.1 Overview	150
	7.2 Methods	152
	7.3 Results	161
	7.4 Conclusion	168
	References	170
	Part III Robustness in Artificial Intelligence Systems	174
	Robustness in Nature as a Design Principle for Artificial Intelligence	176
	8.1 Introduction	176
	8.2 Robustness	177
	8.3 Robustness In Nature	178
	8.4 Robustness in Artificial Intelligence	183
	8.5 Robustness Elsewhere	195
	8.6 Summary	197
	References	197
	Feedback Structures as a Key Requirement for Robustness: Case Studies in Image Processing	200
	9.1 Robustness Through Feedback	200
	9.2 Robust Image Processing	202
	9.3 Feedback Structures in Image Processing	204
	9.4 Closed-Loop Image Segmentation	205
	9.5 Closed-Loop Region-Based Image Segmentation for a Service Robotic Task	209
	9.6 Closed-Loop Boundary-Based Image Segmentation for an Industrial Application	214
	9.7 Conclusions	218
	References	218
	Exploiting Motor Modules in Modular Contexts in Humanoid Robotics	220
	10.1 Introduction	220
	10.2 Reaching with a Modular Control Structure	224
	10.3 Reaching in Different Contexts	226
	10.4 Experimental Results	232
	10.5 Open Issues and FutureWork	234
	10.6 Conclusions	236
	A Minimum Number of Motion Primitives	236
	References	239
	Part IV Robustness in Space Applications	242
	Robustness as Key to Success for Space Missions	244
	11.1 Introduction	244
	11.2 Challenge Space	245
	11.3 Robustness in the Hardware	247
	11.4 Robustness in the Software	250
	11.5 Testing Robustness	258
	11.6 Conclusion	259
	References	260
	Robust and Automated Space System Design	262
	12.1 Introduction	262
	12.2 Uncertainty Modeling	265
	12.3 Design Optimization	268
	12.4 Case Study	270
	12.5 Discussion	272
	12.6 Conclusions and FutureWork	274
	References	274
	Appendix A Model Variable Structure	276
	B Model Equations	278
	C MER Mission Sequence	280
	D Thruster Specification	280
	E Uncertainty Specification	281
	Robust Bio-regenerative Life Support Systems Control	284
	13.1 Introduction	284
	13.2 Life Support Systems	285
	13.3 Control Architecture for a BLSS	289
	13.4 The Reactive Level	292
	13.5 The Deliberative Level	297
	13.6 Toward a Fault-Tolerant and Reliable Life Support System	301
	13.7 Conclusions	303
	Appendix A: Abbreviations in This Chapter	304
	References	304
	Index	308