74.419 Artificial Intelligence

Intelligent Agents 2

Norvig, Ch. 2 and Nilsson, Ch. 1, 2, 25

General Agent Architecture Agent Processing – Concepts Agent Processing – Examples Sensory Processing

Speech Vision Proprioception

Outline

Agent Architecture (Norvig)

sensor data perception cognition reasoning | goal setting | planning | learning | re-evaluation of goals action selection action performance motor control

Agent Processing

sensor data speech signal, image, ...perception phonemes, visual objects, ...cognition concepts (language or visual) reasoning conclusions, generalizationgoal setting & priorities, utility functionevaluationplanning from goal to set of actions action selection & execution control action performance & motor control

transform high-level actions into low-level robot actions

learning perceptual, conceptual, plan level

Example 1: Mother hears her Baby cry.

sensor data - sound wave, auditory inputperception - some squeaky noise; baby scream cognition - “my baby cries”reasoning - “I hope she is okay.” “She is hungry.” goal setting, evaluation - “I have to see the doctor with her.” “I have to feed her.” “We have to move to another city.” ...action / plan selection - go feed herplanning - drop laundry, walk upstairs, feed her action selection - drop laundry action performance - open hand motor control - move fingers in certain position

Example 2: Taxi Driver sees Stop sign.

sensor data - light waves, visual inputperception - red sign with some letters cognition - “STOP sign” reasoning - “I have to stop.” “I will be late.” goal setting, evaluation – “Stop the car” “Next time I’ll take the other route.” “I quit my job.”action / plan selection - stop and wait; watch trafficaction selection - hit the brakes, ... action performance - move right foot on brake pedal motor control - move foot along a trajectory until it rests on the brake pedal; apply certain force

Agents – Speech Processing

Speech Signalpreprocessing – sampling, digitizing, filtering sensory data – digitized sound waveperception – frequency analysis, feature extraction, phoneme/word recognition

cognition – ‘baby cries’

Agents – Visual Processing

Visual Imagespreprocessing – digitization, filtering, sensory data – digitized bitmapperception – feature extraction, classification, object recognition cognition – ‘stop sign’

Agents – Effector / Actuator Control

Motor Controlselection of (intentional) actions – based on state and goal evaluation (utility function) reflexive / reactive behaviour – action ‘without thinking’ action performance – transform (higher level) action commands into agent’s basic actions motor control – commands for agent’s basic action repertoire, e.g. move grasper to point

Agents – Proprioception

Connecting Sensory Input and Motor Controlproprioception – delivers sensory information on agent’s internal physical state, e.g. angles of joints of limbsused in planning and performing (motor) actions and to provide feedback for motor control (also for other physiological processes like hunger, thirst)

Agent Architecture (Nilsson)

Agent Planning Architecture (Nilsson)

ILAs (intermediate level actions) combine several LLAs (low level actions)

Types of Agents (Norvig)

Depending on the complexity of the behaviour function (i.e. the percept – action mapping)

• simple Reflex Agents (low-level behaviour routines)

• Agents with Memory (world states)• Agents with Goals (search, planning)• Agents with Utility Function (decision

between goals)

Simple Reflex Agent – Example (Nilsson)

Robot in Maze• perceives 8 squares around it • low-level percept: can robot move to

square or not• higher level percept: 2 unit segments• 4 basic actions: left (west), right (east),

up (north), down (south)• task is to move along a border• no 'tight' spaces, at least two free

squares

Simple Reflex Agent - Example (Nilsson)

Note: The description of the left bottom agent seems to be wrong. This agent will walk clockwise along the outside wall.

Note: The description of the left bottom agent seems to belong to this agent. It will walk counter- clockwise around the object.

Simple Reflex Agent - Example

Simple Reflex Agent - Example

Behaviour RoutinesIf x1=1 and x2=0 then move rightIf x2=1 and x3=0 then move downIf x3=1 and x4=0 then move leftIf x4=1 and x1=0 then move upelse move up

Task Environments (Norvig)

Agents design depends on task environment: deterministic vs. stochastic vs. non-deterministic

assembly line vs. weather vs. “odds & gods” episodic vs. non-episodic

assembly line vs. diagnostic repair robot, Flakey

static vs. dynamic room without vs. with other agents

discrete vs. continuous chess game vs. autonomous vehicle

single vs. multi agent solitaire game vs. soccer, taxi driver

fully observable vs. partially observable video camera vs. infrared camera - colour?

Infrared Picture of an Unpleasant Situation

from www.indigosystems.com

Robotic Sensors (digital) camera infrared sensor range finders, e.g. radar, sonar GPS tactile (whiskers, bump panels) proprioceptive sensors, e.g. shaft

decoders force sensors torque sensors

Robotic Effectors ‘limbs’ connected through joints; degrees of freedom = #directions in

which limb can move (incl. rotation axis) drives: wheels (land), propellers, turbines

(air, water) driven through electric motors, pneumatic

(gas), or hydraulic (fluids) actuaction statically stable, dynamically stable