1. Elephants Don’t Play Chess
By Rodney A. Brooks
Presented by: Yan Ha

2. Purpose of the paper: 2 approaches of AI
explore second approach which emphasizes ongoing physical interaction with the environment as the primary source of constraints (physical grounding)
examples and future work

3. Classical AI classical AI (symbol system hypothesis) is flawed
bases its decomposition of intelligence into functional information processing modules
none of the modules themselves generate the behavior of the total system
improvement in the competence of the system proceeds by improving the individual function modules

4. Nouvelle AI base on physical grounding hypothesis
bases its decomposition of intelligence into individual behavior generating modules, whose coexistence and co-operation let more complex behaviors emerge
improvement in the competence of system proceeds by adding new modules

5. Symbol System Hypothesis
states that intelligence operates on a system of symbols
perception and motor interfaces are sets of symbols which the central intelligence system operates
symbols represented entities in the world (ex: objects, emotions, molecules…)

6. Inadequacy of Symbol Systems
symbol systems assume a knowable objective truth
there is a limit on the complexity that modal logics can be built for the symbolic system
frame problem: impossible to assume anything not explicitly stated
rely on emergent properties

7. Physical Grounding Hypothesis
states that to build an intelligent system, its representations need be grounded in the physical world
no need for traditional symbolic representations
the world is its own best model

8. Physical Grounding System
connect system to the world via set of sensors and actuators
no typed input and output
built from the bottom up
system has to express its goals and desires as physical action, and extract its knowledge from physical sensors
forms of low-level interfaces have consequences that ripple through entire system

9. Subsumption Architecture
built on a computational substrate that is organized into a series of incremental layers, each connecting perception to action
substrate is network of finite state machines augmented with timing elements
subsumption compiler???

10. Old Subsumption Language???
each AFSM has a set of registers and timers connected to a conventional FSM which control a combinational network fed by the registers
registers can be written by attaching input wires to them and sending messages from other machines-get replaced
arrival of a message can trigger a change of state in the interior FSM

11. New Subsumption Language
groups multiple processes (AFSM) into behaviors
message passing/suppression/inhibition between processes within a behavior, or between behaviors
behaviors act as abstraction barriers-one behavior cannot reach inside another

12. Physically Grounded Systems
seemingly goal-directed behavior emerges from the interactions of simpler non-goal-directed bahaviors

13. Allen sonar range sensors and odometry, offboard lisp machine 3 layers
first layer-avoid both static and dynamic obstacles
sit in middle of the room until approached, move away and avoid collision
sonar return represented repulsive force
vector sum tells robot where to move

14. Allen
additional reflex halted robot whenever there was something right in front of it and it was moving forward
Second layer-randomly wander about every 10 secs
Third layer made robot look for distant places and try to head towards them
suppress the direction desired by the wander layer

15. Tom and Jerry
2 identical robots that demonstrate how little computation is necessary to support subsumption architecture
3 1-bit infrared proximity sensors, 3-layer system
first layer-use vector sum of repulsive forces from obstacles for obstacle avoidance
second layer-wander about
top layer-detect moving objects and create a follow behavior
wander behavior was suppressed when chasing objects

16. Tom and Jerry
demonstrate the notion of independent behaviors combing without knowing about each other (chasing obstacles but staying back a little)
subsumption architecture can be compiled to gate level

17. Squirt smallest robot (50 grams)
8-bit computer, on board power supply, 3 sensors and a propulsion system
acts as a “bug”, hiding in dark corners and venturing out in the direction of noises, after noises are gone, looking for a new place to hide (near to previous noises)

18. Squirt
high level behavior emerges from a set of simple interactions with the world
its lowest level monitors a light sensor and causes it to move in spiral pattern searching for darkness, then stops
second level monitors 2 microphones and measures time of arrival of sound at each
waits for pattern of sharp noise followed by silence, ventures out to the direction, suppressing the desire to stay in dark

19. Other examples
Herbert
Genghis
Toto
Seymour

20. Future Work
how to combine many behavior generating modules to be productive
how to handle multiple sources of perceptual information when there’s a need for fusion
how to automate building of interaction interfaces between behavior modules so that larger systems can be built