Artificial Intelligence Lecture Notes PDF

Mansoura University Faculty of Computers and Information Course Name: Artificial Intelligence Lecturer: Amir EL-Ghamry Topic: Intelligent Agents  Outline 2 — Agents and environments — Rationality...

Mansoura University Faculty of Computers and Information Course Name: Artificial Intelligence Lecturer: Amir EL-Ghamry Topic: Intelligent Agents  Outline 2 — Agents and environments — Rationality — PEAS (Performance measure, Environment, Actuators, Sensors) Artificial Intelligence a modern approach  Agent 3 Artificial Intelligence a modern approach  Agent How to design this? Sensors percepts ? Environment Agent actions Actuators CS 561, Lecture 2  Agent Examples 6 Artificial Intelligence a modern approach  How is an Agent different from other software? — Agents are autonomous, that is, they act on behalf of the user — Agents contain some level of intelligence, from fixed rules to learning engines that allow them to adapt to changes in the environment — Agents have social ability, that is, they communicate with the user, the system, and other agents as required — Agents may also cooperate with other agents to carry out more complex tasks than they themselves can handle CS 561, Lecture 2  How is an Agent different from other software? — Agents may migrate from one system to another to access remote resources or even to meet other agents CS 561, Lecture 2  Agent and Environment 9 Artificial Intelligence a modern approach  Structure of Intelligent Agents — Agent program: the implementation of f : P* ® A, the agent’s perception-action mapping function Skeleton-Agent(Percept) returns Action memory ¬ UpdateMemory(memory, Percept) Action ¬ ChooseBestAction(memory) memory ¬ UpdateMemory(memory, Action) return Action — Architecture: a device that can execute the agent program (e.g., general-purpose computer, specialized device, etc.) CS 561, Lecture 2  Vacuum-cleaner world 12 — Percepts: location (A or B) and contents (dirt or not), e.g., [A,Dirty] — Actions: Left, Right, Suck, NoOp — Agent’s function à look-up table ¡ For many agents this is a very large table Artificial Intelligence a modern approach  Vacuum-cleaner world 13 Artificial Intelligence a modern approach  Agent function – Lookup table 14 Artificial Intelligence a modern approach  Rational Agent 15 Artificial Intelligence a modern approach  Rational agents 16 Rationality – Good behavior 1. Performance measuring success 2. Agents prior knowledge of environment 3. Actions that agent can perform 4. 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. Artificial Intelligence a modern approach  Back to vacuum cleaner agent 17 Artificial Intelligence a modern approach  Back to vacuum cleaner agent 18 Artificial Intelligence a modern approach  Vacuum cleaner agent - irrational 19 Artificial Intelligence a modern approach  Rationality 21 — Rational is different from omniscience (all knowing with infinite knowledge) ¡ 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 ¡ Perfection (omniscience) maximizes actual outcome. Artificial Intelligence a modern approach  The Right Thing = The Rational Action — Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date ¡ Rational = Best Yes, to the best of its knowledge ¡ Rational = Optimal Yes, to the best of its abilities (constraints). ¡ Rational ¹ Omniscience ¡ Rational ¹ Successful  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  Specifying the task environment (PEAS) 24 Artificial Intelligence a modern approach  Specifying the task environment (PEAS) 25 Artificial Intelligence a modern approach  PEAS – vacuum cleaner 26 Artificial Intelligence a modern approach PEAS – Windshield Wiper Agent  PEAS – Windshield Wiper Agent — Goals: Keep windshields clean & maintain visibility — Percepts: Raining, Dirty — Sensors: Camera (moist sensor) — Actuators: Wipers (left, right, back) — Actions: Off, Slow, Medium, Fast — Environment: Inner city, freeways, highways, weather …  PEAS – self driving car 29 Artificial Intelligence a modern approach  PEAS - automated taxi driver 30 Artificial Intelligence a modern approach  PEAS - automated taxi driver 31 The task of designing an automated taxi driver: – Performance measure: Safe, fast, legal, comfortable trip, maximize profits – Environment: Roads, other traffic, pedestrians, customers , weather – Actuators: Steering wheel, accelerator, brake, signal, horn – Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard Artificial Intelligence a modern approach  Interacting Agents Collision Avoidance Agent (CAA) — Goals: Avoid running into obstacles — Percepts: Obstacle distance, velocity, trajectory — Sensors: Vision, proximity sensing — Actuators: Steering Wheel, Accelerator, Brakes, Horn, Headlights — Actions: Steer, speed up, brake, blow horn, signal (headlights) — Environment: Freeway Lane Keeping Agent (LKA) Goals: Stay in current lane Percepts: Lane center, lane boundaries Sensors: Vision Effectors: Steering Wheel, Accelerator, Brakes Actions: Steer, speed up, brake Environment: Freeway Conflict Resolution by Action Selection Agents Arbitrate: if Obstacle is Close then CAA else LKA Challenges: Doing the right thing  PEAS – medical diagnosis system 34 Artificial Intelligence a modern approach  PEAS – medical diagnosis system 35 Artificial Intelligence a modern approach  PEAS – spam filter 36 Artificial Intelligence a modern approach  PEAS – spam filter 37 Artificial Intelligence a modern approach  PEAS – satellite image analysis system 38 Artificial Intelligence a modern approach  PEAS – satellite image analysis system 39 Artificial Intelligence a modern approach  PEAS - Part-picking robot 40 Artificial Intelligence a modern approach  PEAS - Part-picking robot 41 — Performance measure: Percentage of parts in correct bins — Environment: Conveyor belt with parts, bins — Actuators: Jointed arm and hand — Sensors: Camera, joint angle sensors Artificial Intelligence a modern approach  PEAS - Interactive English tutor 42 Artificial Intelligence a modern approach  PEAS - Interactive English tutor 43 — Performance measure: Maximize student's score on test — Environment: Set of students — Actuators: Screen display (exercises, suggestions, corrections) — Sensors: Keyboard Artificial Intelligence a modern approach  44 Questions Artificial Intelligence a modern approach