74.419 Artificial Intelligence Intro Agents Based on Norvig, Ch. 2 and Nilsson, Ch. 1, 2 General Agent Architecture Agent Processing - Examples Behaviour – Mapping from Perceptions to Actions Types of Agents (complexity of behaviour) Task Environments

Agent Systems General Agent Architecture Behaviour – Mapping from Perceptions to Actions Types of Agents (complexity of behaviour) Task Environments

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 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

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)

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

Types of Agents Complexity of behaviour function (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)

Task Environments (adapted from Norvig) Classification 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

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