Elephants Don’t Play Chess

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