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Seed Words for BRON ARREA

CROSSROADSTHE STUDENT MAGAZINEACM CROSSROADS XRDS HUMANOID ROBOT HUMANOID ROBOT NAVEED AHMAD INTRODUCTION RODNEY BROOKS INVENTOR DIRECTOR ARTIFICIAL INTELLIGENCE WRITES PONDERED THIS THOUGHT ABOUT DECIDED BUILD FIRST SERIOUS ATTEMPT ROBOT WITH HUMAN-LEVEL CAPABILITIES FIRST SERIOUS ATTEMPT HAL-CLASS BEING AFTER YEARS RESEARCH BEHAVIOR-BASED INSECT ROBOTS BROOKS TEAM STARTED CONSTRUCT ROBOT SHAPED LIKE HUMAN THEY NAMED ABBREVIATION COGNITION ALSO TOOTH GEAR DESIGNED BUILT EMULATE HUMAN THOUGHT PROCESSES EXPERIENCE WORLD HUMAN BROOKS TEAM FURTHER ASSUMED THAT PEOPLE WOULD FIND EASIER INTERACT WITH ROBOT ROBOT LEARNING PROCESS WHEN ROBOT COULD RESPOND SOMEWHAT HUMAN CONSEQUENTLY MACHINE SHOULD HAVE LIMBS SENSORY ORGANS PHYSICAL RESEMBLANCE HUMANS UNLIKE OTHER ARTIFICIAL INTELLIGENCE SYSTEMS LIKE MEDICAL EXPERT SYSTEMS MEANT TEST THEORIES HUMAN COGNITION DEVELOPMENTAL PSYCHOLOGY FIGURE HUMANOID ROBOT WHAT DOES LOOK LIKE DEVELOPED PHYSICALLY COULD ENCOUNTER RATHER THAN SIMULATE SAME ENVIRONMENTAL PHYSICAL CONSTRAINTS ADULT HUMANS FURTHERMORE HUMAN THOUGHT REPRESENTATION RELATED HUMAN PHYSICAL FORM HUMAN LIKE INTELLIGENCE ONLY POSSIBLE WITH HUMAN LIKE FORM HOWEVER DOES HAVE LEGS FROM WAIST DOWN BUILT IMMOVABLE STAND DOES HOWEVER HAVE HEAD TORSO ARMS HEAD VISION SYSTEM WITH FOUR CAMERAS MOUNTED PAIRS WITH DEGREES FREEDOM DEGREE FREEDOM DENOTES EACH PLANE ROBOT MOVE EACH COMPOSED PAIR CAMERAS WIDE ANGLE OTHER TELESCOPIC VIEW ROBOT THREE RATE GYROSCOPES LINEAR ACCELEROMETERS MOUNTED HEAD MIMIC HUMAN VESTIBULAR SYSTEM MICROPHONES MOUNTED HEAD AUDITORY SYSTEM FEEDBACK ABOUT MOTOR SYSTEM PROVIDED SENSORS LOCATED JOINTS GIVE FEEDBACK ABOUT STATE EACH JOINT TOTAL MECHANICAL ARMS TORSO WITH WAIST TORSO TWIST FOUR NECK THREE EYES EACH POWERED ELECTRIC MOTOR THROUGH SERIES SPRINGS WHICH PROVIDE ACCURATE TORQUE FEEDBACK CHANGING EQUILIBRIUM POSITIONS JOINTS EXPLICIT MOTOR ANGLE COMMANDS DETERMINES POSITION ARMS SPRING LIKE ALLOWS MOVE LIKE HUMAN COUNTERPART HETEROGENEOUS NETWORK PROCESSORS RANGING FROM SMALL MICROCONTROLLERS JOINT LEVEL AUDIO VISUAL PROCESSORS ORIGINAL BRAIN NETWORK MOTOROLA MICROCONTROLLERS CONNECTED THROUGH DUAL-PORT EACH NODE SUBSET MULTI-THREADED LISP CURRENT SYSTEM NETWORK RUNNING UNIX REAL-TIME OPERATING SYSTEM CONNECTED ETHERNET SYSTEMS COMMUNICATE THROUGH SHARED MEMORY INTERFACE CARDS TOOL UNDERSTAND HUMANS MOST IMPORTANT ASPECT HUMANS THEIR CAPABILITY LEARN ADAPT BROAD CATEGORIES HUMAN LEARNING INTERACTION WITH PHYSICAL WORLD SOCIAL INTERACTION WITH FELLOW HUMAN BEINGS INFANT LEARNS COORDINATE MOVEMENT LIMBS FROM SIMULTANEOUS FEEDBACK FROM SENSES LEARNS LAWS NATURE FROM CONTINUOUS EXPERIMENTATION REMEMBERS CAUSE EFFECT RELATIONSHIPS THROUGH LIMBS SENSES INFANT LEARNS WALK AFTER UNDERGOING STAGES KICKING CRAWLING STUMBLING LEARNING SIMPLE BEHAVIORS BEFORE HARD ONES OTHER ASPECT LEARNING SOCIAL INTERACTION INITIALLY WITH PARENTS SUBSEQUENTLY THROUGH TEACHERS FRIENDS PEERS OTHERS LANGUAGE EXPRESSIONS EMOTIONS MAKE COMMUNICATION POSSIBLE LEARNING INCREMENTAL PROGRESSING FROM SIMPLE MORE COMPLICATED TASKS SKILLS CONCEPTS LEARNING ALSO CUMULATIVE PREVIOUS KNOWLEDGE SERVES FOUNDATION SUBSEQUENT KNOWLEDGE KNOW UNDERSTAND HUMANS BUILDING BIRD BOTH INSPIRATION MODEL AFTER CENTURIES EXPERIMENTATION MAKING FLYING MACHINES BETTER UNDERSTANDING FLIGHT EVENTUALLY INVENTION AIRPLANE SIMILARLY ATTEMPT MODEL HUMANS CONSTRUCTING PHYSICAL BODY RESEMBLING HUMAN IMPLEMENTING BRAIN SOFTWARE THAT IMITATES HUMAN COGNITION PERHAPS THIS BETTER UNDERSTAND HUMAN BEINGS EVENTUALLY LEARN MORE EFFICIENT WAYS WHICH TEACH HUMAN CHILDREN INSPIRATION FROM BRAIN EFFORTS IMPLEMENT COG'S MIND INSPIRED FROM STUDIES NEUROSCIENCE SUCH EXAMPLE TAUGHT ORIENT HEAD MOVING NOISY STIMULUS INSPIRED FROM STUDIES SUPERIOR COLLICULUS SUPERIOR COLLICULUS PART HUMAN BRAIN THAT SPECIALIZES INTEGRATING SENSORY INFORMATION ORIENTING SENSORY ORGANS SUCH NECK EARS TOWARD SOURCE SENSORY INPUT SUPERIOR COLLICULUS BEEN FOUND ORGANIZED LAYERS TOPOGRAPHICALLY ARRANGED MAPS WHERE EACH ARRANGEMENT NEURONS MAPS SENSITIVE CERTAIN SENSORY STIMULI THERE ALSO MOTOR MAPS WHICH UPON BEING STIMULATED CERTAIN REGIONS ELICIT MOVEMENT ORGAN THESE MAPS INTERCONNECTED ARRANGEMENT MAPS INTERCONNECTIVITY MAPS KNOWN CHANGE OVER PERIOD DEVELOPMENT HUMAN TWO-DIMENSIONAL ARRAY ELEMENTS WHICH EACH ELEMENT REPRESENTS SITE MAPS INTERCONNECTED MANNER THROUGH WHICH ACTIVITY SITE TRANSFERRED ANOTHER FIGURE SHOWS ACTIVITY VISUAL SPACE RELAYED MOTOR THROUGH VISUO-MOTOR REGISTRATION REGISTRATION INTERCONNECTS DIFFERENT MAPS RELAYS ACTIVITY FROM OTHER THERE ALSO REGISTRATION MAPS BETWEEN SENSORY MAPS EXAMPLE VISUAL AUDIO SPACE LEARNING HAPPENS STRENGTHENING CONNECTIONS BETWEEN REGIONS WHICH STRONGLY CORRELATED THUS ACTIVATED SIMULTANEOUSLY WEEDING WRONG CONNECTIONS VISUAL ACTIVITY STIMULATES CERTAIN REGIONS VISUAL WHICH TURN ACTIVATES MOTOR REGION MOST ACTIVITY MOTOR DETERMINES MOTOR COMMAND DIRECT MOTION ERROR DISTANCE BETWEEN CENTER MOTION CENTER VIEW USED CORRECT CONNECTIONS BETWEEN MAPS FIGURE EXAMPLE RELATIONSHIP COG'S VISUAL MOTOR THROUGH REGISTRATION ACTIVITY REGION VISUAL RELAYED MOTOR SUBSEQUENT ACTIVITY MOTOR DETERMINES COMMANDS MOTORS COG'S EYES INCREMENTAL LEARNING MANY APPLICATIONS TAKE SPECIFIC APPROACHES SOLVING SPECIFIC PROBLEMS EXAMPLE TYPICAL ROBOT INTENDING GRASP OBJECT WOULD CAPTURE VIDEO IMAGES PROCESS THEM MAKE MATHEMATICAL MODEL COORDINATES OBJECT SEND THEM ROUTINE WHICH CONTROLS ARM'S MOTORS PRE-DETERMINED ROBOT SUCCESSFULLY GRASP OBJECT THIS SOLUTION EFFECTIVE HOWEVER ROBOT LEARN TASK CANNOT GENERALIZE CONCEPT GRASPING ANOTHER SITUATION PROGRAMMER WILL ALWAYS HAVE DEVISE SOLUTION EVERY DIFFERENT ROBOTIC PROBLEM RESEARCHERS HAVE ADOPTED DEVELOPMENTAL APPROACH WHICH LEARNS INDEPENDENTLY LEARNS DEVELOPMENTAL FASHION PIECEMEAL INCREMENTALLY CUMULATIVELY THIS APPROACH INSPIRED THAT INFANTS LEARN REACH GRAB OBJECTS INTEREST LEARNS PHASES FIRST LEARNS DIRECT EYES TOWARD OBJECT LEARNING OBJECT'S COORDINATES LEARN MOTOR COMMANDS TILT GAZE REPEATEDLY EXPERIMENTS READJUSTS MAPPING FROM DIFFERENCE CENTER ACTUAL POSITION OBJECT IMAGE PLANE THIS DONE UNTIL LEARNS ALIGN TARGET OBJECT CENTER IMAGE PLANE SECOND PHASE EXPERIMENTS WITH MOVING ARMS TRIES COORDINATE BALLISTIC MAPPING MOTORS MOTION DIRECTION EYESIGHT DIRECTION SIGHT TARGET OBJECT FIRST ATTEMPTS REACH USING INITIAL MAPPING CONFIGURATION THEN DETERMINES ERROR DISTANCE HAND TARGET OBJECT IMAGE PLANE SINCE ALREADY LEARNED ORIENT HEAD TOWARD TARGET OBJECT APPLIES THAT KNOWLEDGE KNOWS ORIENT HEAD EYES TOWARD HANDS MAKES CORRECTION ARM-TO-SIGHT MAPPING LATER ATTEMPT TARGET OBJECT SAME EYESIGHT DIRECTION HAND ROBOT WILL KNOW REACH SINCE MADE CORRECTION PREVIOUS ATTEMPT SYSTEM LEARNS MAPPING HOURS SELF-TRAINING TAKING APPROXIMATELY TRIALS FIGURE ORIENTS HEAD TOWARD TARGET OBJECT SKILL THAT MASTERED FIRST PHASE THEN TRIES EXTEND BALLISTICALLY DIRECTION GAZE HAND MISSES TARGET OBJECT USES KNOWLEDGE FROM FIRST PHASE FROM POSITION GAZE DIRECTION TARGET OBJECT REACH ATTEMPT SUCCESSFUL MUST MAKE CORRECTION GAZE DIRECTION MAPPING SOCIAL INTERACTION CHILDREN MAKE MANY DISCOVERIES THEIR JUST LIKE LEARNED REACH OBJECT LEARNING FROM MISTAKES PEOPLE ALSO LEARN MANY THINGS FROM THEIR PARENTS TEACHERS FRIENDS BEEN PROGRAMMED INTERACT SOCIALLY MOST BASIC FORM COMMUNICATION SOCIAL INTERACTION SHARED ATTENTION FOCUS OBJECT MUTUAL INTEREST STAGES LEARNING SOCIAL SKILLS INCREASING DEGREE COMPLEXITY GAZE MONITORING FOLLOWING GAZE FOLLOWING POINTING ASKING OBJECT POINTING THESE SKILLS HAVE BEEN INCREMENTALLY TAUGHT LEARNING THROUGH DEVELOPMENTAL METHODOLOGY ADVANTAGEOUS BECAUSE REDUCES COMPLEX TASKS SIMPLER ONES COMPLEX TASKS REUSE MORE GRANULAR TASKS THAT EASIER LEARN TASKS LEARNED GRADUALLY INCREASING THEIR COMPLEXITY EXAMPLE LEARNING FOLLOW GAZE BUILDS SKILLS HOLDING GAZE HOLDING GAZE REQUIRES RECOGNIZE FACES USING ALGORITHMS WHICH PRE-COMPILED HUMAN FACE TEMPLATES THEN USING PRE-LEARNED SKILLS ORIENTS HEAD EYES TOWARD ROBOT TEACHER THEN EXTRACTS FACIAL IMAGE LOCATES TEACHER USING FACE RATIO TEMPLATES ANOTHER LEARNED MAPPING ONCE LEARNED MONITOR GAZE ABILITY FOLLOW GAZE SIMPLY BUILDS PRIOR SKILL THREE MORE ADDITIONAL SKILLS NEEDED MORE COMPLEX SKILL GAZE FOLLOWING FINDING ANGLE GAZE EXTRAPOLATING ANGLE GAZE OBJECT ORIENTING MOTORS HEAD EYES FOLLOW OBJECT THIS EXAMPLE SHARED ATTENTION IMPLEMENTED USING EXAMPLE RESEARCH INTO LEARNING SOCIAL SKILLS DEVELOPMENTAL FASHION ORDER INTERACT SOCIALLY SHOW EXPRESSIONS THAT ROBOT TEACHER KNOWS REACT ROBOT EXAMPLE CANNOT UNDERSTAND TEACHER COULD SHOW BOREDOM PUZZLEMENT HINTING THAT TEACHER SHOULD SOMETHING DIFFERENT KISMET COG'S STAND-ALONE EXPRESSIVE HEAD RELATIVE KISMET CONVEY FEAR BOREDOM ANGER OTHER EMOTIONS USING EARS EYELIDS MOUTH EYEBROWS KISMET BEHAVIOR ENGINE THAT INTEGRATES PERCEPTIONS EMOTIONS DRIVES BEHAVIOR ROBOT DISTINGUISHES BETWEEN FACE NON-FACE STIMULI LONG KISMET'S DRIVES REMAIN HOMEOSTATIC RANGE KISMET DISPLAYS EMOTIONS HAPPINESS INTEREST ONCE EMOTIONS EXCEED THESE RANGES SHOWS EXPRESSION FATIGUE DISTRESS FEAR DEPENDING EMOTIONAL STATE KISMET EXAMPLE ROBOT LEFT ALONE UNDER-STIMULATED SHOWS EXPRESSION SADNESS MEANING THAT CARE GIVER SHOULD PLAY WITH ROBOT OVEREXPOSED SHOW SIGNS DISGUST MEANING THAT CAREGIVER SHOULD SLOW DOWN LEARNING COORDINATION PHYSICAL WORLD BROOKS BELIEVES THAT HUMAN BEINGS WORLD ORGANIZE MANIPULATE KNOWLEDGE HENCE THERE NEED BUILD ELABORATE MATHEMATICAL MODELS WORLD EXECUTE HEAVY-DUTY COMPUTATIONS BEFORE ACTING DOES NEED COMPUTE THREE-DIMENSIONAL ENVIRONMENT BEFORE MOVES SIMPLY START WALKING CHANGE DIRECTIONS BASED REAL TIME CUES LANDMARKS ROBOT'S BEHAVIOR DIRECT FUNCTION PHYSICAL INTERACTION WITH WORLD EXAMPLE PHYSICAL COUPLING IMPLEMENTATION COG'S ARMS WRISTS ELBOWS SHOULDERS DRIVEN FORCE OSCILLATORS USING PROPRIOCEPTIVE INFORMATION THERE CENTRAL CONTROLLER MODELING ARMS COMPLETE BEHAVIOR ARMS BEHAVIORS JOINTS RESPONDING ENVIRONMENT COG'S ARMS DISPLAY COMPLEX BEHAVIOR LIKE BALANCING SLINKY HANDS PLAYING DRUM PROCESSING REAL-TIME FEEDBACK SENSORS CHANGING EQUILIBRIUM OSCILLATORS JOINTS THIS MOVEMENT PRODUCED WITHOUT ELABORATE SOFTWARE MODELS WORLD CONTROL ENVIRONMENT SENSES USED SINGULARLY HUMANS RATHER THEY COMPLEMENT EACH OTHER RESULTING INTEGRATION INFORMATION FROM MULTIPLE SENSES SIMULTANEOUSLY JUST BIRD CHIRP HELP IDENTIFY WHETHER OBJECT OVERHEAD BIRD AIRPLANE HUMANS EMPLOY LIP-READING TECHNIQUES ASSIST LISTENING LEARNED VISUAL INFORMATION TRAIN AUDITORY LOCALIZATION RELATIONSHIP VISUAL MOVEMENT DIRECTION SOUND USED TRAIN VISUAL AUDITORY MAPPING ONCE LEARNED THIS MAPPING ORIENT HEAD TOWARD SOURCE SOUND ALSO MIMICS HUMAN VESTIBULAR OCULAR REFLEX REFLEX THAT STABILIZES IMAGE WHILE HEAD MOVING TURNING EYES OPPOSITE DIRECTION DIRECTION HEAD MOVEMENT LEARNS COMPENSATE CAMERA MOTION FEEDBACK FROM RATE CHANGE GYROSCOPES MOUNTED HEAD RELATING INFORMATION FROM DIFFERENT SENSES IMPROVES PERFORMANCE REQUIRES LESS COMPUTATION THAN RELYING SENSORY INPUT ISOLATION THAT LEARNED REACH OBJECT EXAMPLE INTERACTING WITH PHYSICAL WORLD WHERE TOUCHING TARGET OBJECT WITH ARMS BECOME PART COG'S PHYSICAL EXPERIENCE LEARNS COORDINATE SENSORY INFORMATION FROM VISION SYSTEM WITH MOTORS ARMS DOES CONSTRUCT ELABORATE MODELS WORLD SIMPLY LEARNS CORRELATIONS BETWEEN SENSORY INPUT ARM'S ACTIONS HUMAN ONGOING RESEARCH PROJECT UNDERSTAND EMULATE HUMAN BEHAVIOR PSYCHOLOGY ALTHOUGH RESEARCH ONLY ICEBERG GOAL MAKE HUMAN MAIN ISSUES WITH MAKE SUBSYSTEMS BEHAVIOR COHERENT STILL NEEDS ELABORATE MOTIVATIONAL MODEL THAT SELECT BETWEEN BEHAVIORS EXAMPLE OBJECTS INTEREST VIEW SHOULD ABLE DECIDE WHICH OBJECT REACH WHICH IGNORE BASED GOALS FOCUS CURRENTLY COG'S BEHAVIORS DESIGNED INDEPENDENTLY REQUIRE RESOURCES ALSO FEWER TACTILE SENSORS THAN HUMAN NERVOUS SYSTEM ALSO SENSORS FEEL MOTION SOME JOINTS THIS INFORMATION BEEN USED MUCH INFORMATION FROM FORCE SENSORS EACH JOINTS BEEN USED OTHER SUBSYSTEMS EXCEPT DIRECT FEEDBACK CONTROL ARMS DOES HAVE ABILITY TASTE SMELL EVEN THOUGH SOPHISTICATED IMAGE PROCESSING SUBSYSTEM STILL BEHIND CAPABILITIES HUMANS ALTHOUGH DECIPHER FACES LOCATE MOTION COLORS CANNOT RECOGNIZE FACES REAL TIME MEMORY EXPERIENCE PLAY ROLE HUMAN COGNITION DOES EXPERIENCE TIME LIVES ETERNAL PRESENT CANNOT STORE LONG-TERM MEMORIES CHRONOLOGICAL ORDER MEMORY LIMITED PARTICULAR EXPERIMENTS CHALLENGE RELATE STATIC DATA STRUCTURES COMPUTATIONAL MODELS MEMORY FLOW TIME THESE SOME SHORTCOMINGS THAT INDICATE FUTURE DIRECTIONS RESEARCH CONCLUSION HUMANS MOST COMPLEX CREATURES EARTH ROBOTS STILL BEHIND TERMS COGNITIVE COMPLEXITY FLEXIBILITY THEORIES HUMAN PSYCHOLOGY IMPROVE IMPLEMENTED HUMAN-LIKE ROBOTS WILL EVOLVE FROM INFANT ADULT ADVANCES BIOTECHNOLOGY GIVE ROBOTS MORE BIOLOGICAL FORM GREATER PHYSICAL RESEMBLANCE HUMANS ONLY HUMANOID ROBOT HONDA'S ASIMO SONY'S SDR- KITANO SYMBIOTIC SYSTEMS' BABY ROBOT PINO SOME OTHER HUMANOID ROBOTS BEING DEVELOPED AROUND WORLD DATE THESE COMMERCIALLY AVAILABLE ROBOTS LIKE SONY'S ENTERTAINMENT AIBO HOWEVER ALREADY MARKET AIBO AUTONOMOUS ROBOT WHICH INHABITS HOME SINGS DANCES READS EMAILS RE-CHARGES LEARNS FROM OWNER NATURAL PROGRESSION TECHNOLOGY ENSURES THAT HUMANOID ROBOTS WILL SOON FOLLOW COMMERCIALLY AVAILABLE ROBOTS JUST VERGE ROBOTIC REVOLUTION WHICH ROBOTS INTELLIGENT AUTONOMOUS MACHINES BECOME COMMON PART DAILY LIVES WHEN START COMMUNICATE WITH THEM HUMAN LANGUAGE TEACH THEM DAILY CHORES SHARE RESPONSIBILITIES WILL WHEN HAVE ACHIEVED ULTIMATE SCIENCE FICTION GOAL MAKING HUMAN-LIKE ROBOTS REFERENCES ADAMS BREAZEAL BROOKS SCASSELLATI HUMANOID ROBOTS KIND TOOL IEEE INTELLIGENT SYSTEMS JULY-AUGUST BROOKS BREAZEAL MARJANOVIC SCASSELLATI WILLIAMSON PROJECT BUILDING HUMANOID ROBOT COMPUTATION METAPHORS ANALOGY AGENTS SPRINGER LECTURE NOTES ARTIFICIAL INTELLIGENCE SPRINGER-VERLAG BROOKS BREAZEAL FERRELL IRIE KEMP MARJANOVIC SCASSELLATI WILLIAMSON ALTERNATE ESSENCES INTELLIGENCE AAAI- BROOKS CAMBRIAN INTELLIGENCE EARLY HISTORY CAMBRIDGE PRESS BROOKS FLESH MACHINES ROBOTS WILL CHANGE YORK PANTHEON BOOKS SHOP HTTP PROJECTS HUMANOID-ROBOTICS-GROUP HTML FERRELL ORIENTATION BEHAVIOR USING REGISTERED TOPOGRAPHIC MAPS PRESENTED CAPE IRIE ROBUST SOUND LOCALIZATION APPLICATION AUDITORY PERCEPTION SYSTEM HUMANOID ROBOT THESIS DEPARTMENT ELECTRICAL ENGINEERING COMPUTER SCIENCE MARJANOVIC SCASSELLATI WILLIAMSON SELF-TAUGHT VISUALLY-GUIDED POINTING HUMANOID ROBOT PROCEEDINGS FOURTH INTERNATIONAL CONFERENCE SIMULATION ADAPTIVE BEHAVIOR SAB- SEPTEMBER SCASSELLATI BUILDING BEHAVIORS DEVELOPMENTALLY FORMALISM PROCEEDINGS AAAI SPRING SYMPOSIUM INTEGRATING ROBOTICS RESEARCH JANUARY SCASSELLATI IMITATION MECHANISMS JOINT ATTENTION DEVELOPMENTAL STRUCTURE BUILDING SOCIAL SKILLS HUMANOID ROBOT NEHANIV COMPUTATION METAPHORS ANALOGY AGENTS SPRINGER LECTURE NOTES ARTIFICIAL INTELLIGENCE SPRINGER-VERLAG WILLIAMSON EXPLOITING NATURAL DYNAMICS ROBOT CONTROL PROCEEDINGS FOURTH EUROPEAN MEETING CYBERNETICS SYSTEMS RESEARCH EMCSR -------------------------------------------------------------------------------- BIOGRAPHY NAVEED AHMAD NAVACRON HOTMAIL CURRENTLY PURSUING MASTER'S DEGREE COMPUTER SCIENCE FROM UNIVERSITY ILLINOIS URBANA CHAMPAIGN EARNED BACHELOR'S COMPUTER SCIENCE FROM LAHORE UNIVERSITY MANAGEMENT SCIENCES UNDERGRADUATE STUDENT INVOLVED RESEARCH CRICKET EXPERT SYSTEM RESEARCH PROJECT NINTH PAKISTAN SOFTWARE COMPETITION ROBOT HOBBYIST KEEN RESEARCH INTEREST MAKING ROBOTS LIKE ANIMALS HUMANS COPYRIGHT ASSOCIATION COMPUTING MACHINERY XHTML WCAG CITIES URBAN AREAS SOUTH KOREA WITH POPULATION OVER BOOKS FROM AMAZON MORE POPULATION INFORMATION INCLUDING UPDATED FIGURES WIDER SELECTION CITIES PLEASE VISIT WORLD GAZETTEER COPYRIGHT RHETT BUTLER TABLE BELOW INCLUDES CITIES WITH POPULATIONS EXCEEDING PEOPLE FIGURES ESTIMATES URBAN AREA RANK CITY RANK CITY COUNTRY CITY POPULATION URBAN AREA POPULATION SEOUL SOUTH KOREA PUSAN SOUTH KOREA TAEGU SOUTH KOREA INCHEON SOUTH KOREA TAEJEON SOUTH KOREA KWANGJU SOUTH KOREA SEONGNAM SOUTH KOREA ULSAN SOUTH KOREA PUCHEON SOUTH KOREA SUWEON SOUTH KOREA ANYANG SOUTH KOREA CHEONJU SOUTH KOREA CHEONGJU SOUTH KOREA KOYANG SOUTH KOREA ANSAN SOUTH KOREA CHANGWEON SOUTH KOREA POHANG SOUTH KOREA MASAN SOUTH KOREA KWANGMYEONG SOUTH KOREA EUIJEONGBU SOUTH KOREA CHINJU SOUTH KOREA CHEJU SOUTH KOREA KUMI SOUTH KOREA MOKPO SOUTH KOREA IKSAN SOUTH KOREA CHEONAN SOUTH KOREA KUNSAN SOUTH KOREA PYEONGTAEK SOUTH KOREA CHUNCHEON SOUTH KOREA WEONJU SOUTH KOREA YEOSU SOUTH KOREA SUNCHEON SOUTH KOREA KANGNEUNG SOUTH KOREA KYEONGJU SOUTH KOREA CHUNGJU SOUTH KOREA ANDONG SOUTH KOREA PORYONG SOUTH KOREA CHECHON SOUTH KOREA TONGYONG SOUTH KOREA TONGHAE SOUTH KOREA -------------------------------------------------------------------------------- MAPS TOWNS CITIES SOUTH KOREA -------------------------------------------------------------------------------- FURTHER INFORMATION RAINFORESTS OTHER REFERENCE MATERIAL AVAILABLE POPULATION UPDATE CITY DATA COUNTRIES LARGEST CITIES WORLD DATA OTHER COUNTRIES COUNTRY POPULATION PROJECTIONS OTHER COUNTRY DATA CAPITALS LAND AREA TOTAL POPULATION FOREST STATS SELECTED COUNTRIES OTHER REFERENCE INFORMATION MONGABAY TROPICAL RAINFOREST INFORMATION TROPICAL FRESHWATER FISH INFORMATION IMAGES PHOTOS LEND SUPPORT COPYRIGHT RHETT BUTLER

Thanks to THROLLEX for these words.

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