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

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

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

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

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

Part 1: Adaptive Systems

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

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

Foundations of Adaptive Computation: Natural Adaptive Systems

What are Adaptive Systems in Nature? Examples?

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

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

Artificial Adaptive Systems

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.

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

Artificial Adaptive Systems Examples

Example1 Evolving artificial creatures, Karl Sims:

Example2 Ants

TSP pb.

Ex.3: NN: spatial forms

Ex. 4: NN:OCR

Ex.5: intelligent agent Steve

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

General Classification of AS Software Hardware Combined

Example: combined Khepera robot

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

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

Applications of Artificial Adaptive Systems

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

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

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

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?

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)

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

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

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

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

Ultimate Goal of Artificial Adaptive Systems? Intelligence

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

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