Artificial Intelligence 2. Intelligent Agents Dr. M. Tounsi

Definition Is anything: Example: With Robotic agent perceiving its environment through sensors acting upon its environment through effectors Example: With Robotic agent Sensors: Cameras and infrared Effectors: various motors

Definitions and Concepts Rational Agent One that does the right thing (most successful!) Issue: how and when to evaluate the agent’s success ? Performance Measure Criteria that determines how successful an agent is Percept Sequence Everything that the agent has perceived so far Ideal Rational Agent Should do whatever action is expected to maximize its performance measure based on percept sequence and whatever build-in knowledge the agent has

Rational agents An agent should strive to "do the right thing", based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful Performance measure: An objective criterion for success of an agent's behavior E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

PEAS PEAS: Performance measure, Environment, Actuators, Sensors Definition: Actuator = Effector Must first specify the setting for intelligent agent design Example: the task of designing an automated taxi driver: Performance measure: Safe, fast, legal, comfortable trip, maximize profits Environment: Roads, other traffic, pedestrians, customers Actuators: Steering wheel, accelerator, brake, signal, horn Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

PEAS Agent: Medical diagnosis system Performance measure: Healthy patient, minimize costs,… Environment: Patient, hospital, staff Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) Sensors: Keyboard (entry of symptoms, findings, patient's answers)

PEAS Agent: Part-picking robot Performance measure: Percentage of parts in correct bins Environment: Conveyor belt with parts, bins Actuators: Jointed arm and hand Sensors: Camera, joint angle sensors

PEAS Agent: Interactive English tutor Performance measure: Maximize student's score on test Environment: Set of students Actuators: Screen display (exercises, suggestions, corrections) Sensors: Keyboard

Agent functions and programs An agent is represented by the agent function which maps percept sequences to actions Aim: find a way to implement the rational agent function concisely

Table-lookup agent Function Table-Agent(percept) returns an Action Static: percepts // sequence, initially empty Table // table of actions, indexed by percept, initially fully specified Begin Append perept to the end of the percepts Action := LOOKUP(percepts, Table) return Action End Drawbacks: Huge table Take a long time to build the table No autonomy Even with learning, need a long time to learn the table entries

Program Agent The design of the agent depends on the percepts, actions, goals and environment Function SKELETON-AGENT (percept) Static: memory; memory <- Update-Mem (memory, percept) action <- Choose-Best-Action (Memory) Memory <- Update-Memory(memory, action) Return action

Agent types Four basic types in order of increasing generality: Simple reflex agents respond immediately to percepts (i.e.: rules) Model-based reflex agents Goal-based agents act so that they will achieve their goal(s) Utility-based agents try to maximize their own happiness

Example: Simplex reflex agent Function Reflex-Vacum_agent(location, status) return actions { If status = dirty then action= Clean_ON; else { if location = A then action=right; else action = left; }

AI Agent should have initial knowledge as well as an ability to learn Autonomy If the agent is based completely on the “built-in knowledge” part, such that it pays no attention to its percepts, then we say that the agent lacks autonomy If the behavior of the agent is determined by only its own experience  the system is autonomous AI Agent should have initial knowledge as well as an ability to learn