1. Sensor-Actuator Networks (Braitenburg Vehicles) “Experiments in Synthetic Psychology” OR Steps toward “[really] artifical life” Norm Badler Steve Lane CSE 377

2. General Structure Decision_function SensorsActuators Environment Actuators = Decision_function(Sensors)

3. A Simple Example kS SV=kS Environment V = k(S) Velocity is a linear function of sensor value

4. Basic Braitenberg Vehicle Design Sensor/Actuator Pairs Light or other environment feature sensor(s) Motor(s) (wheels) “Wiring” Vehicle 1

5. Two Motors Make it a Little More Interesting (Left-Right Actuators) k l (S l ) ; k r (S r ) Sl;SrSl;Sr V left = k l (S l ); V right = k r (S r ) Environment V left = k l (S l ); V right = k r (S r ) Velocity of (left, right) actuators are linear functions of two sensor values

6. A Little More Complex k l (S r ) ; k r (S l ) Sl;SrSl;Sr V left = k l (S r ); V right = k r (S l ) Environment V left = k l (S l ); V right = k r (S r ) Velocity of (left, right) actuators are linear functions of two sensor values (but crossed)

7. Excitatory and Inhibitory Functions k l (S l ) ; k r (S r ) Sl;SrSl;Sr V left = k l (S l ); V right = k r (S r ) Environment V left = k l (S l ); V right = k r (S r ) Functions may be excitatory (+) or inhibitory (-) (essentially reflects the slope of the function)

8. Fear & Aggression Vehicle 2

9. Exploring & Love Vehicle 3

10. Values & Special Tastes – Con’t Vehicle 4 The outer 2 sensors are uncrossed & excitatory The next pair in are crossed and excitatory The third pair are uncrossed and inhibitory like Sensor/Actuator Pairs The fourth pair are crossed and excitatory.

11. Values & Special Tastes Vehicle 4 Dislikes high temperature (turns away from hot places. Dislikes light sources (turns toward them and destroys them. Prefers oxygenated environment containing organic matter Can move elsewhere when O 2 & food scarce.

12. VALUES, KNOWLEDGE & INFERENCE From the outside you might conclude that Vehicle 4 has: –a system of VALUES Dislikes high temperatures Dislikes light sources Prefers environments with food sources –KNOWLEDGE of its environment and –an INFERENCE ability Light bulbs are a source of heat If I destroy them then I will be cooler Oxygen & organic matter make energy

13. But What’s Really Going On? Intelligence implies the ability to acquire, represent and process information There was no such acquisition or representation of information here. –In constructing Vehicle 4 we were just playing with the wiring between sensors and actuators The behavioral properties and responses that emerge may look intelligent but they really are not. –When we analyze a system we tend to overestimate its complexity –Anyone have pets?

14. Taking this Further F l (params,S l,S r ); F r (params,S l,S r ) Sl;SrSl;Sr V left = F l (…); V right = F r (…) Environment V left = F l (params,S l,S r ); V right = F r (params,S l,S r ) Velocity of (left, right) actuators are non-linear, parametric functions of two sensor values

15. Non-linear sensory responses V= speed of motor I= intensity of stimulation

16. What’s the Point? Hard-wired function; Learned function “Eyes”; “Ears” “Hunger” Wheels; Legs; Color; Other internal state Environment The decision functions relate actuator behaviors to the sensed environment. Can generalize any component.

17. Decisions ?

18. Sensor Scope ?

19. What (Who) is Being Sensed? Environment –Check for obstacles, food sources, lights, etc. –Beware zig-zag wall following… Other [nearby] vehicles/creatures –Check local environment for motion of neighbors, gives rise to flocking and herding behaviors. –BoidsBoids

20. Some other links Vehicles: Experiments in Synthetic Psychology, by Valentino Braitenberg (1984), Bradford Books, MIT Press, ISBN 0-262-02208-7 Braitenberg demos Braitenberg Vehicles BEAST POPBUGS Gerken (Dewdwey article) Some implementation notes You can find a lot more if you Google…

21. Conclusions The interaction of simple devices and systems can give rise to a variety of complex emergent behavior Many of these individual behaviors can be readily seen in animals such as insects, bees, ants, etc. –love, fear, aggression, foraging, exploring, etc, Group behaviors also can be created in a similar manner –Flocking, herding, schooling, etc. You can implement this for particular cases in your worlds. The computational model is scalable to multiple individuals (“code re-use”, parameterized). Lesson Learned – [Graphical] Synthesis is a lot easier than Analysis!