Agent-Based Systems Lecture 1 PDF

Agent Based systems Lecture 1 Prof. Dr. Magdy Zakarya Dr. Dina Saif  Outline 2 ⚫Agents and environments ⚫Rationality ⚫PEAS (Performance measure, Environment, Actuators, Sensors) ⚫Environment types ⚫Agent types ...

Agent Based systems Lecture 1 Prof. Dr. Magdy Zakarya Dr. Dina Saif  Outline 2 ⚫Agents and environments ⚫Rationality ⚫PEAS (Performance measure, Environment, Actuators, Sensors) ⚫Environment types ⚫Agent types  Agents 3 An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators Human agent: eyes, ears, and other organs for sensors; hands, legs, mouth, and other body parts for actuators Robotic agent: cameras and infrared range finders for sensors various motors for actuators  Agents and environments 4 The agent function maps from percept histories to actions: [f: P* → A] The agent program runs on the physical architecture to produce f agent = architecture + program  Vacuum-cleaner world 5 Demos: http://aima.cs.berkeley.edu/demos.html ⚫Percepts: location and contents, e.g., [A,Dirty] ⚫Actions: Left, Right, Stuck, NoOp ⚫Agent’s function -> look-up table ⚪ For many agents this is a very large table stuck stuck stuck Artificial Intelligence a modern approach  Rational agents 6 Rationality – Performance measuring success – Agents prior knowledge of environment – Actions that agent can perform – Agent’s percept sequence to date Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.  Examples of Rational Choice 7 Consider the simplified map of Romania below. Numbers correspond to travel times. Consider the following choices. 1. To go from Rimnicu Vilcea to Craivo: a. RV – Pitesti – Craiova, or b. RV- Craiova? 2. To go from Oradea to Craiova: a. Go via Arad-Sibiu-…, or b. Go via Sibui, or c. Go via Arad-Timisoara-….? Map 8  Rationality 9 ⚫Rational is different from ⚪ Percepts may not supply all relevant information ⚪ E.g., in card game, don’t know cards of others. ⚫Rational is different from being perfect ⚪ Rationality maximizes expected outcome while perfection maximizes actual outcome.  Autonomy in Agents The autonomy of an agent is the extent to which its behaviour is determined by its own experience, rather than knowledge of designer. ⚫Extremes ⚪ No autonomy – ignores environment/data ⚪ Complete autonomy – must act randomly/no program ⚫Example: baby learning to crawl ⚫Ideal: design agents to have some autonomy ⚪ Possibly become more autonomous with experience  PEAS 11 PEAS: Performance measure, Environment, Actuators, Sensors Must first specify the setting for intelligent agent design Examples: – Automated Taxi Driver – Part-picking robot – Interactive English tutor  PEAS for automated taxi driver 12 – 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 Part-picking robot 13 ⚫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: Interactive English tutor 14 ⚫Agent: Interactive English tutor ⚫Performance measure: Maximize student's score on test ⚫Environment: Set of students ⚫Actuators: Screen display (exercises, suggestions, corrections) ⚫Sensors: Keyboard, microphone Thank you