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Tables of Contents for Multiagent Robotic Systems
Chapter/Section Title
Page #
Page Count
I Motivation, Approaches, and Outstanding Issues
1
48
Why Multiple Robots?
3
8
Advantages
4
1
Major Themes
4
1
Agents and Multi-Agent Systems
5
1
Multi-Agent Robotics
6
5
Toward Cooperative Control
11
8
Cooperation-Related Research
12
1
Distributed Artificial Intelligence
12
1
Distributed Systems
13
1
Biology
13
1
Learning, Evolution, and Adaptation
13
2
Design of Multi-Robot Control
15
4
Approaches
19
8
Behavior-Based Robotics
20
1
Collective Robotics
21
1
Evolutionary Robotics
21
3
Inspiration from Biology and Sociology
24
1
Summary
25
2
Models and Techniques
27
12
Reinforcement Learning
27
5
Markov Decision Process
29
1
Reinforcement Learning Algorithms
29
1
Temporal Differencing Techniques
29
1
Q-Learning
30
1
Multi-Agent Reinforcement Learning
30
2
Genetic Algorithms
32
2
Artificial Life
34
1
Artificial Immune System
35
1
Probabilistic Modeling
36
2
Related Work on Multi-Robot Planning and Coordination
38
1
Outstanding Issues
39
10
Self-Organization
40
1
Local vs. Global Performance
40
1
Planning
41
1
Multi-Robot Learning
42
1
Coevolution
42
1
Emergent Behavior
43
1
Reactive vs. Symbolic Systems
43
1
Heterogeneous vs. Homogeneous Systems
44
1
Simulated vs. Physical Robots
45
1
Dynamics of Multi-Agent Robotic Systems
46
1
Summary
47
2
II Case Studies in Learning
49
78
Multi-Agent Reinforcement Learning: Technique
51
14
Autonomous Group Robots
52
8
Overview
52
1
Sensing Capability
53
1
Long-Range Sensors
53
1
Short-Range Sensors
54
1
Stimulus Extraction
55
1
Primitive Behaviors
56
3
Motion Mechanism
59
1
Multi-Agent Reinforcement Learning
60
4
Formulation of Reinforcement Learning
60
3
Behavior Selection Mechanism
63
1
Summary
64
1
Multi-Agent Reinforcement Learning: Results
65
28
Measurements
66
1
Stimulus Frequency
66
1
Behavior Selection Frequency
66
1
Group Behaviors
67
26
Collective Surrounding
68
2
Cooperation among RANGER Robots
70
1
Moving away from Spatially Cluttered Locations
70
1
Changing a Target
71
1
Cooperatively Pushing Scattered Objects
71
1
Collective Manipulation of Scattered Objects
71
1
Concurrent Learning in Different Groups of Robots
72
1
Concurrent Learning in Predator and Prey
72
13
Chasing
85
1
Escaping from a Surrounding Crowd
86
7
Multi-Agent Reinforcement Learning: What Matters?
93
20
Collective Sensing
94
2
Initial Spatial Distribution
96
1
Inverted Sigmoid Function
97
1
Behavior Selection Mechanism
97
1
Motion Mechanism
98
11
Emerging a Periodic Motion
109
1
Macro-Stable but Micro-Unstable Properties
110
1
Dominant Behavior
111
2
Evolutionary Multi-Agent Reinforcement Learning
113
14
Robot Group Example
114
2
Target Spatial Distributions
114
1
Target Motion Characteristics
114
1
Behavior Learning Mechanism
115
1
Evolving Group Motion Strategies
116
4
Chromosome Representation
116
1
Fitness Functions
117
2
The Algorithm
119
1
Parameters in the Genetic Algorithm
120
1
Examples
120
2
Summary
122
5
III Case Studies in Adaptation
127
56
Coordinated Maneuvers in a Dual-Agent System
129
14
Issues
130
1
Dual-Agent Learning
130
1
Specialized Roles in a Dual-Agent System
131
1
The Basic Capabilities of the Robot Agent
131
1
The Rationale of the Advice-Giving Agent
132
3
The Basic Actions: Learning Prerequisites
133
1
Genetic Programming of General Maneuveres
133
1
Genetic Programming of Specialized Strategic Maneuvers
134
1
Acquiring Complex Maneuvers
135
3
Experimental Design
135
1
The Complexity of Robot Environments
135
1
Experimental Results
136
1
Lightweight or Heavyweight Flat Posture
137
1
Lightweight Curved Posture
137
1
Lightweight Corner Posture
138
1
Lightweight Point Posture
138
1
Summary
138
5
Collective Behavior
143
40
Group Behavior
144
2
What is Group Behavior?
145
1
Group Behavior Learning Revisited
145
1
The Approach
146
3
The Basic Ideas
146
1
Group Robots
147
1
Performance Criterion for Collective Box-Pushing
147
1
Evolving a Collective Box-Pushing Behavior
148
1
The Remote Evolutionary Computation Agent
149
1
Collective Box-Pushing by Applying Repulsive Forces
149
6
A Model of Artificial Repulsive Forces
149
1
Pushing Force and the Resulting Motion of a Box
149
1
Chromosome Representation
150
1
Fitness Function
151
1
Examples
152
1
Task Environment
152
1
Simulation Results
153
1
Generation of Collective Pushing Behavior
153
1
Adaptation to New Goals
154
1
Discussions
154
1
Collective Box-Pushing by Exerting External Contact Forces and Torques
155
20
Interaction between Three Group Robots and a Box
155
1
Pushing a Cylindrical Box
156
1
Pushing Position and Direction
156
1
Pushing Force and Torque
156
1
Pushing a Cubic Box
157
1
The Coordinate System
157
1
Pushing Force and Torque
157
1
Chromosome Representation
157
1
Fitness Functions
157
1
Examples
158
1
Task Environment
158
1
Adaptation to New Goals
158
1
Simulation Results
158
14
Adaptation to Dynamically Changing Goals
172
2
Discussions
174
1
Convergence Analysis for the Fittest-Preserved Evolution
175
6
The Transition Matrix of a Markov Chain
175
3
Characterizing the Transition Matrix using Eigenvalues
178
3
Summary
181
2
IV Case Studies in Self-Organization
183
48
Multi-Agent Self-Organization
185
24
Artificial Potential Field (APF)
186
2
Motion Planning Based on Artificial Potential Field
186
1
Collective Potential Field Map Building
187
1
Overview of Self-Organization
188
1
Self-Organization of a Potential Field Map
189
5
Coordinate Systems for a Robot
189
1
Proximity Measurements
190
1
Distance Association in a Neighboring Region
190
2
Incremental Self-Organization of a Potential Field Map
192
1
Robot Motion Selection
193
1
Directional 1
193
1
Directional 2
194
1
Random
194
1
Experiment 1
194
1
Experimental Design
194
1
Experimental Results
195
1
Experiment 2
195
1
Experimental Design
195
1
Experimental Results
196
1
Discussions
196
13
Evolutionary Multi-Agent Self-Organization
209
22
Evolution of Cooperative Motion Strategies
210
5
Representation of a Proximity Stimulus
212
1
Stimulus-Response Pairs
212
1
Chromosome Representation
213
1
Fitness Functions
214
1
The Algorithm
215
1
Experiments
215
3
Experimental Design
215
1
Comparison with a Non-Evolutionary Mode
216
1
Experimental Results
217
1
Discussions
218
1
Evolution of Group Behaviors
218
1
Cooperation among Robots
218
1
Summary
219
12
V An Exploration Tool
231
48
Toolboxes for Multi-Agent Robotics
233
46
Overview
233
1
Toolbox for Multi-Agent Reinforcement Learning
234
2
Architecture
234
1
File Structure
234
1
Function Description
235
1
User Configuration
235
1
Data Structure
236
1
Toolbox for Evolutionary Multi-Agent Reinforcement Learning
236
1
File Structure
236
1
Function Description
236
1
User Configuration
237
1
Toolboxes for Evolutionary Collective Behavior Implementation
237
21
Toolbox for Collective Box-Pushing by Artificial Repulsive Forces
237
1
File Structure
237
1
Function Description
237
1
User Configuration
237
2
Data Structure
239
1
Toolbox for Implementing Cylindrical/Cubic Box-Pushing Tasks
240
1
File Structure
240
1
Function Description
240
15
User Configuration
255
2
Data Structure
257
1
Toolbox for Multi-Agent Self-Organization
258
2
Architecture
258
1
File Structure
258
1
Function Description
258
1
User Configuration
258
1
Data Structure
258
2
Toolbox for Evolutionary Multi-Agent Self-Organization
260
9
Architecture
260
1
File Structure
260
1
Function Description
260
4
User Configuration
264
1
Data Structure
265
4
Example
269
10
True Map Calculation
269
2
Initialization
271
2
Start-Up
273
1
Result Display
274
5
References
279
22
Index
301