• General Agent Architectures

• Agent Processing – Concepts, Examples

• Example of a simple Reflexive Agent

• Types of Agents

• Task Environments

• Examples of Agent Types

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

74.419 Artificial Intelligence Intro Agents

Agent Architecture (Norvig)

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

Agent Processing

sensor data speech signal, image, ...

perception phonemes, visual objects, ...

cognition concepts (language or visual)

reasoning conclusions, generalization

goal setting & evaluation priorities, utility function

planning 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 - soundwave, 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.” “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 Signal

preprocessing – sampling, digitizing, filtering

sensory data – digitized sound wave

perception – frequency analysis, feature extraction, phoneme/word recognition

cognition – ‘baby cries’

Agents – Visual Processing

Visual Images

preprocessing – digitization, filtering,

sensory data – digitized bitmap

perception – feature extraction, classification, object recognition

cognition – ‘stop sign’

Agents – Effector/Actuator Control

Motor Control

selection 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 Control

proprioception – delivers sensory information on agent’s internal physical state, e.g. angles of joints of limbs

used in planning and performing (motor) actions and to provide feedback for motor control (also for other physiological processes like hunger, thirst)

Agent Architectures (Nilsson)

Nilsson, Figures 25.2 and 25.3, p. 446-7

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:

• 4 basic actions: left (west), right (east), up (north), down (south)

• task is to move along a border

Task Environments (Norvig)Depends on task, environment, and sensors

fully observable vs. partially observable video camera in bright room vs. infrared camera

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

episodic vs. non-episodic assembly line vs. diagnostic repair robot

static vs. dynamic room without vs. with other agents

discrete vs. continuous chess game vs. autonomous vehicle

Robots – Sensors and Effectors

• next class on Friday

Describe Flakey

Sensor Equipment?

Action Repertoire?

Task Environment?

Perceptions and Cognition?

Goals? Intentions?

Type of Agent?