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, 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 controltransform high-level actions into low-level robot actions learning perceptual, conceptual, plan level

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

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

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

Agent Architecture (Nilsson)

Agent Planning Architecture (Nilsson)

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

Behaviour Routines If x1=1 and x2=0 then move right If x2=1 and x3=0 then move down If x3=1 and x4=0 then move left If x4=1 and x1=0 then move up else move up

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, Flakey  static vs. dynamic  room without vs. with other agents  discrete vs. continuous  chess game vs. autonomous vehicle

Infrared Picture of an Unpleasant Situation copied from

Examples of Agents (Norvig)

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

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