Build Intelligence from the bottom up! Behavioral Robotics Build Intelligence from the bottom up!

AI Debate #3 Is representation needed? “When we examine very simple intelligence we find that explicit representations and models of the world get in the way.” “Representation is the wrong unit of abstraction in building the bulkiest parts of intelligent systems.” Rodney A. Brooks in Intelligence without representation

Evolutionary Timeline Single cell entities arose in the soup 3.5 billion years ago Fish and vertebrates arose 550 million years ago Insects – 350 million years ago Reptiles – 370 million Primates – 120 million Man 2.5 million Agriculture 10,000 years Writing – 5000 years Expert knowledge in the last few hundred years

Traditional Robotic Architecture Perception Modeling Planning Task Execution Motor Control Actuators Sensors

Effects of Traditional Architecture Single point of failure Spends a lot of time planning! Sensor Fusion Planning is a way of avoiding deciding what to do next!

Subsumption Architecture Reason Plan Changes Monitor Changes Actuators Sensors Build Maps Explore Wander Avoid Objects

Assumptions Complex behaviors need not come from a complex control scheme. Things should be simple! Build cheap robots in natural spaces. The world is 3D! Absolute coordinates don’t work! Useful worlds are not polyhedra. Sensors and actuators fail. Build artificial beings!

Subsumption Module inhibition FSM Outputs Inputs Reset Suppression

Simple Level 0 Feel Force runaway Turn Sonar Forward Collide force heading heading Sonar Forward Collide encoders

Add level 1 Wander Avoid Sonar Feel Force runaway Turn Collide Forward heading encoders

Mapping / Navigation Since we can’t use: How can we map the world? absolute coordinates Sensor fusion How can we map the world? Distributed Maps Maja J. Mataric and R. Brooks

Landmark detector Sonar compass

Kismet

Ants

Genghis

Creature requirements Cope in a timely fashion in a dynamic environment Robust w.r.t it’s environment Maintain multiple goals Do something!

What this approach isn’t! Not neural nets / connectionism Use FSM and low connectivity Not production rules Not a blackboard Not German Philosophy Heidegger has similar ideas, but this is engineering!

Limits to Growth How many layers can be stacked up? How much complexity can happen without central representation Can learning occur in fixed networks?