1. 74.419 Artificial Intelligence Intro Agents
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

2. Agent Architecture (Norvig)
sensor data  perception  cognition 
reasoning | goal setting, re-evaluation of goals | planning | learning  action selection 
action performance  motor control

3. 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

4. Example 1: Mother hears her Baby cry.
sensor data - soundwave, auditory input
perception - 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 her
planning - drop laundry, walk upstairs, feed her
action selection - drop laundry
action performance - open hand
motor control - move fingers in certain position

5. Example 2: Taxi Driver sees Stop sign.
sensor data - light waves, visual input
perception - 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 traffic
action 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

6. 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’

7. Agents – Visual Processing
Visual Images
preprocessing – digitization, filtering,
sensory data – digitized bitmap
perception – feature extraction, classification, object recognition
cognition – ‘stop sign’

8. 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

9. 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)

10. Agent Architectures (Nilsson)
Nilsson, Figures 25.2 and 25.3, p

11. 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)

12. 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

13. 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

15. Robots – Sensors and Effectors
next class on Friday

16. Describe Flakey Sensor Equipment? Action Repertoire? Task Environment?
Perceptions and Cognition?
Goals? Intentions?
Type of Agent?