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E-raamat: Design of Intelligent Multi-Agent Systems: Human-Centredness, Architectures, Learning and Adaptation

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There is a tremendous interest in the design and applications of agents in virtually every area including avionics, business, internet, engineering, health sciences and management. There is no agreed one definition of an agent but we can define an agent as a computer program that autonomously or semi-autonomously acts on behalf of the user. In the last five years transition of intelligent systems research in general and agent based research in particular from a laboratory environment into the real world has resulted in the emergence of several phenomenon. These trends can be placed in three catego­ ries, namely, humanization, architectures and learning and adapta­ tion. These phenomena are distinct from the traditional logic­ centered approach associated with the agent paradigm. Humaniza­ tion of agents can be understood among other aspects, in terms of the semantics quality of design of agents. The need to humanize agents is to allow practitioners and users to make more effective use of this technology. It relates to the semantic quality of the agent design. Further, context-awareness is another aspect which has as­ sumed importance in the light of ubiquitous computing and ambi­ ent intelligence. The widespread and varied use of agents on the other hand has cre­ ated a need for agent-based software development frameworks and design patterns as well architectures for situated interaction, nego­ tiation, e-commerce, e-business and informational retrieval. Fi- vi Preface nally, traditional agent designs did not incorporate human-like abilities of learning and adaptation.
Humanization of soft computing agents
1(30)
Rajiv Khosla
Qiubang Li
Chris Lai
Introduction
1(1)
Human-centered system development framework
2(2)
Distributed multi-agent architecture
4(10)
Problem solving ontology layer
5(4)
Optimization layer
9(2)
Tool or technology layer
11(3)
Human-centered modelling using soft computing multi-agent architecture
14(12)
Human-centeredness and problem solving agent layer
14(2)
CRM model of Internet-banking
16(1)
Decomposition phase problem solving agent and CRM
17(1)
Control phase problem solving agent
18(1)
Decision phase problem solving agent
19(2)
Unstained cell image processing
21(4)
Human-centeredness and technology agent layer
25(1)
Conclusion
26(5)
References
27(4)
Software agents for ubiquitous computing
31(32)
Sasu Tarkoma
Mikko Laukkanen
Kimmo Raatikainen
Introduction
31(2)
Ubiquitous computing
33(7)
Overview
33(1)
Wireless networks and roaming
34(1)
Client devices
35(1)
Location- and context-aware services
36(1)
Technology support
37(1)
Java -- the enabling technology for software agents
37(1)
Other technologies
38(2)
Ubiquitous agents
40(10)
Overview
40(4)
The FIPA architecture
44(2)
Agent platforms
46(1)
Jade-Leap
47(1)
FIPA-OS and MicroFIPA-OS
47(1)
Proxy-based approaches
48(1)
Agent communication
49(1)
Events for agents
49(1)
Agent-based service provision and deployment
50(9)
Agent and service deployment
50(2)
Service partitioning based on the environment
52(2)
Example scenario: recommendation service
54(3)
Crumpet
57(2)
Conclusions
59(4)
References
60(3)
Agents-based knowledge logistics
63(40)
Alexander Smirnov
Mikhail Pashkin
Nikolai Chilov
Tatiana Levashova
Introduction
63(4)
KSNet-approach: major ideas
67(2)
Features of agent community in the system ``KSNet''
69(5)
Communication, interaction and negotiation in the KL system
74(10)
Conventional CNP
75(1)
Constraint-based negotiation
76(1)
Modifications of interaction
77(1)
Example of utilizing constraint-based CNP
77(7)
Implementation of agent community
84(6)
Case study: health service logistics
90(3)
Case study: virtual supply network
93(1)
Conclusion
94(9)
Acknowledgements
96(1)
References
96(7)
Architectural styles and patterns for multi-agent systems
103(30)
Manuel Kolp
T. Tung Do
Stephane Faulkner
T.T. Hang Hoang
Introduction
103(3)
Organizational architectural styles
106(10)
Applying organizational styles
111(3)
Evaluation
114(2)
Social patterns
116(13)
Modeling social patterns
117(1)
Social dimension
118(1)
Intentional dimension
118(2)
Structural dimension
120(5)
Communication dimension
125(1)
Dynamic dimension
126(2)
Applying the patterns
128(1)
Conclusion
129(4)
References
130(3)
Design and behavior of a massive organization of agents
133(58)
Alain Cardon
Introduction
133(3)
The systems with particles
136(7)
The unsteady systems
138(2)
Operators of determination of the behavior for an unsteady system
140(3)
The object approach: a very controlled process of construction and run of systems
143(5)
The object approach and the software engineering
144(1)
Objects and object-oriented design of systems
145(2)
Limits of the object approach
147(1)
Massive multi-agent systems
148(14)
Agents
149(3)
Nondeterminism and instability in massive multi-agent systems
152(3)
An agentification method for the massive multi-agent systems
155(7)
Analysis of the behavior of a massive agent organization: the control problem
162(18)
The characterization of an agent organization
163(2)
The morphological space, the correspondent of the space of phases for the MMAS
165(6)
The organization of morphological agents assuring the representation of the aspectual organization
171(4)
Characters of coherent groups
175(2)
Evocation agents and self-adaptability of the system
177(3)
Entropy and equation of trajectory of MMAS
180(5)
Entropy
181(1)
Equation of trajectory: a reduction with regard to the morphological analysis
182(2)
Validity of the state equations
184(1)
Degraded forms of the state equation
185(1)
Conclusion
185(6)
References
187(4)
Developing agent-based applications with JADE
191(24)
F. Bergenti
A. Poggi
G. Rimassa
P. Turci
M. Tomaiuolo
Introduction
191(2)
JADE
193(6)
Platform architecture
193(3)
Agent architecture
196(3)
LEAP
199(4)
LEAP architecture
201(2)
CoMMA
203(3)
CoMMA architecture
204(2)
Agentcities
206(4)
Network
207(1)
Service composition
208(2)
Conclusions
210(5)
Acknowledgments
211(1)
References
212(3)
A collective can do better
215(24)
N.D. Monekosso
P. Remagnino
Introduction
215(2)
Insect behaviour can be inspiring
217(2)
Can nature be mimicked?
219(6)
Applying real ant behaviour to computational systems
219(3)
Solving classic optimisation problems
222(1)
Telecommunications applications
223(1)
Robot navigation applications
224(1)
Other robotic applications
224(1)
Image processing applications
225(1)
Combining reinforcement learning and synthetic pheromones
225(7)
Reinforcement learning
225(1)
Synthetic pheromones and Q-learning
226(6)
Cooperative robotic transport
232(1)
Conclusions
232(7)
References
233(6)
Coordinating multi-agent assistants with an application by means of computational reflection
239(40)
A. Di Stefano
G. Pappalardo
C. Santoro
E. Tramontana
Introduction
240(3)
The motivation for a multi-assistant architecture
243(3)
Example: extending a Web browser with assistant agents
244(2)
The multi-agent reflective architecture
246(18)
Computational reflection
247(1)
Using Javassist
248(3)
The architecture
251(2)
Coordinator agent
253(4)
Coordinator-assistants interactions
257(2)
A concrete example: an assistant that highlights keywords for a Web browser
259(5)
A case study: e-commerce assistants for a Web browser
264(8)
User profiler assistant
265(3)
Data extraction assistant
268(2)
Cart manager assistant
270(2)
Concluding remarks
272(7)
References
274(5)
Learning by exchanging advice
279(36)
Eugenio Oliveira
Luis Nunes
Introduction
279(2)
Communicating to improve learning: historical notes and review
281(2)
Early work on exchange of information during learning
281(1)
Recent related work
282(1)
Advice exchange
283(9)
Exchanging information during learning
283(1)
What type of information?
284(1)
How to integrate this information with the usual learning process?
285(1)
When should an agent request/accept information?
285(5)
Where to get information?
290(2)
Experiments
292(12)
Predator-prey
294(4)
Traffic control
298(3)
Learning algorithms
301(3)
Results and discussion
304(5)
Predator-prey
304(3)
Traffic control
307(2)
Conclusions and future work
309(6)
Acknowledgments
311(1)
References
311(4)
Adaptation and mutation in multi-agent systems and beyond
315(40)
Ladislau Boloni
Dan Cristian Marinescu
Introduction
315(2)
A taxonomy
317(8)
Alternative names
319(1)
Classification criteria
319(1)
The amplitude of the change: weak vs. strong mutability
320(1)
The granularity of mutation
321(1)
The continuity of interactions: runtime vs. stoptime
322(1)
The initiator of the mutation
322(1)
Mutation technique
323(1)
A taxonomy of mutations
324(1)
Other classification approaches
325(1)
A formal description of mutability
325(11)
Agent models and mutability
325(3)
A multiplane state machine model of agent behavior
328(1)
Modelling agent behavior
329(1)
Decomposition in the plane. expressing ``change''
330(1)
Expressing concurrency
331(1)
Mutation operators and invariance properties
332(2)
How useful are the invariance properties?
334(2)
A software engineering perspective on adaptive and mutable agents
336(11)
Adding new functionality to the agent
338(3)
Removing functionality from an agent
341(2)
Adapting to new requirements
343(3)
Splitting and merging agents
346(1)
Conclusions
347(8)
References
349(6)
Intelligent action acquisition for animated learning agents
355(32)
Adam Szarowicz
Marek Mittmann
Jaroslaw Francik
Introduction
355(2)
Current state of the art in automatic character animation
357(6)
General animation architectures
357(4)
Physics-based controllers
361(1)
Crowd simulation
362(1)
Overview of other concepts
363(4)
Q-learning
363(1)
The agent's senses: collision detection and avoiding
364(2)
Agent architectures
366(1)
Implementation of the Q-learning
367(3)
System implementation and results
370(8)
The framework
370(2)
Results
372(6)
Alternative algorithms
378(1)
Conclusions and summary
378(9)
Acknowledgements
380(1)
References
380(7)
Using stationary and mobile agents for information retrieval and e-commerce
387(59)
Samuel Pierre
Basic concepts and background
388(8)
Agent and multi-agent systems
388(2)
Cooperation and communication mechanisms
390(1)
Communication among agents
390(2)
Cooperation among agents
392(2)
Mobile agent and mobile code
394(2)
Multi-agent architecture for information retrieval
396(15)
Mobile agent information retrieval
397(2)
Characterization of the architecture
399(9)
Experimental number application
408(1)
Principles of the application
408(1)
Design choices and modifications to the initial application
409(1)
Internet picture retrieval application
410(1)
Implementation of the information retrieval architecture
411(8)
Generic classes and interfaces
411(3)
Agents
414(3)
Implementation and testing environment
417(2)
Evaluation of the information retrieval architecture
419(10)
Transportation measures
419(3)
Information retrieval scenarios
422(7)
Multi-agent architecture for product retrieval
429(15)
Description of the problem and general scenario
429(2)
Solution and suggested algorithms
431(2)
Architecture and agent structure
433(3)
Implementation and performance evaluation
436(8)
Conclusion
444(2)
References
446