Download presentation

Presentation is loading. Please wait.

Published byDavid Dillon Modified over 2 years ago

1
ADAPTIVE SYSTEMS & USER MODELING Alexandra I. Cristea USI intensive course Adaptive Systems April-May 2003

2
Introduction Course site: Course schedule, principles, tasks, etc.

3
Module division I. Adaptive Systems and User Modeling course II. Project work

4
Adaptive System course parts 1.Adaptive Systems, Generalities 2.User Modeling 3.Data representation for AS 4.Adaptive Systems, invited talk: Genetic Algorithms

5
Project work parts 1.Presentation MOT 2.Presentation project assignments 3.Group work 4.Project and results presentation and evaluation

6
Part 1: Adaptive Systems

7
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

8
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

9
Foundations of Adaptive Computation: Natural Adaptive Systems

10
What are Adaptive Systems in Nature? Examples?

11
Natural Systems How do adaptive systems in nature compute? (De-)centralized/collective computation Computation over spatial extent Probabilistic computation Computation in continuous-state systems Computation in neural systems

12
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

13
Artificial Adaptive Systems

14

15
Types of Artificial Adaptive Systems Adaptive Hypermedia, Agents, Game of Life, Ant Algorithms, Genetic Algorithms, Artificial Life, Genetic Art, Brain Building, Genetic Programming, Cellular Automata, Cellular Computing, Cellular Neural Networks, Cellular Programming, Complex Adaptive Systems, Quantum Computing, Cybernetics, Reversible Computing, DNA Computing, Self-Replication, Evolutionary Computation, Evolvable Hardware, Virtual Creatures, Flocking Behaviour, etc.

16
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

17
Artificial Adaptive Systems Examples

18
Example1 Evolving artificial creatures, Karl Sims:

19
Example2 Ants

20
TSP pb.

21
Ex.3: NN: spatial forms

22
Ex. 4: NN:OCR

23
Ex.5: intelligent agent Steve

24
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

25
General Classification of AS Software Hardware Combined

26
Example: combined Khepera robot

27
ElementsTechnical Information Processor Motorola 68331, 25MHz [improved] RAM512 Kbytes [improved] Flash512 Kbytes Programmable via serial port [new] Motion2 DC brushed servo motors with incremental encoders SpeedMax: 60 cm/s, Min: 2 cm/s Sensors 8 Infra-red proximity and ambient light sensors with up to 100mm range I/O3 Analog Inputs (0-4.3V, 8bit) PowerPower Adapter Rechargeable NiMH Batteries[improved] Autonomy 1 hour, moving continuously [improved]. Communica tion Standard Serial Port, up to 115kbps [improved] Extension Expansion modules can be added to the robot SizeDiameter: 70 mm Height: 30 mm WeightApprox 80 g

28
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

29
Applications of Artificial Adaptive Systems

30
Applications of Adaptive Systems expert systems –(e.g. medical diagnosis) data mining –(e.g. search engines) computational linguistics games

31
More Applications of Adaptive Computation Parallel computing: –evolution of cellular automata Molecular biology: –molecular evolution, design of useful molecules, protein design Computer security: –immune systems for computers Intelligent agents and robotics Scientific modeling: –evolution, ecologies, economies, insect societies, immune systems, organizations

32
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

33
What can we adapt to? What kind of information can we use to adapt, in general? From whom/ what do we get this information? What means adaptation in this context?

34
What can we adapt to? What kind of information can we use to adapt, in general? –External: Static Variables values: Light intensity, Dynamics: Changes, Other participants behavior –Internal: Needs: hunger –Prediction: (anticipation)

35
What can we adapt to? From whom/ what do we get this information? –Other participants –Existing variables

36
What can we adapt to? What means adaptation in this context? –The adaptive system reacts to the environment (static, dynamics) and to itself towards some benefit

37
Overview: AS 1.Adaptive Systems: Foundations 2.Artificial Adaptive Systems 3.Examples 4.General Classification 5.Applications 6.What can we adapt to? 7.Ultimate goal artificial AS? 8.Conclusion

38
A Comparison between Adaptive and Adaptable Systems Gerhard Fischer 1 HFA Lecture, OZCHI2000

39
Ultimate Goal of Artificial Adaptive Systems? Intelligence

40
Conclusions Man is trying to imitate nature with artificial AS Why? Because man-made machines with predefined behavior cannot cover all aspects Note: Adaptation < Learning < Intelligence

41
Conclusions 2 Adaptation in general doesnt mean to a human […] However, adaptation to a human is more challenging!

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google