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

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

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

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

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…)

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

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

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

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???

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

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

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

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

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

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

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

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)

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

Other examples Herbert Genghis Toto Seymour

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