Artificial Intelligence chapter 1 and 2(1).pdf
- 1. Artificial Intelligence Chap II Dr. Abdul Rahman EL SAYED
- 2. AGENTS AND ENVIRONMENTS • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. • A human agent has eyes, ears, and other organs for sensors and hands, legs, mouth, and other body parts for actuators. • A robotic agent might have cameras and infrared range finders for sensors and various motors for actuators. • A software agent receives keystrokes, file contents, and network packets as sensory inputs and acts on the environment by displaying on the screen, writing files, and sending network packets. • We will make the general assumption that every agent can perceive its own actions (but not always the effects). • We use the term percept to refer to the agent's perceptual inputs at any given instant. • Mathematically speaking, we say that an agent's behavior is described by the agent function that maps any given percept sequence to an action.
- 4. Good Behavior: The concept of rationality • A rational agent is one that does the right thing-conceptually speaking, every entry in the table for the agent function is filled out correctly.
- 5. Performance measures • A performance measure embodies the criterion for success of an agent's behavior. • When MEASURE an agent is plunked down in an environment, it generates a sequence of actions according to the percepts it receives. This sequence of actions causes the environment to go through a sequence of states. • If the sequence is desirable, then the agent has performed well. • Obviously, there is not one fixed measure suitable for all agents.
- 6. • Rationality • What is rational at any given time depends on four things: • The performance measure that defines the criterion of success. • The agent's prior knowledge of the environment. • The actions that the agent can perform. • The agent's percept sequence to date.
- 7. This leads to a definition of a 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.
- 8. • Omniscience, learning, and autonomy • OMNISCIENCE We need to be careful to distinguish between rationality and omniscience. An omniscient agent knows the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality • Learning Our definition requires a rational agent not only to gather information, but also to learn as much as possible from what it perceives. The agent's initial configuration could reflect some prior knowledge of the environment, but as the agent gains experience this may be modified and augmented • A rational agent should be autonomous-it should learn what it can to compensate for partial or incorrect prior knowledge.
- 9. An omniscient agent Knows the actual outcome of its actions in advance No other possible outcomes However, impossible in real world An example crossing a street but died of the fallen cargo door from 33,000ft irrational? Omniscience
- 10. Based on the circumstance, it is rational. As rationality maximizes Expected performance Perfection maximizes Actual performance Hence rational agents are not omniscient. Omniscience
- 11. Learning Does a rational agent depend on only current percept? No, the past percept sequence should also be used This is called learning After experiencing an episode, the agent should adjust its behaviors to perform better for the same job next time.
- 12. Autonomy If an agent just relies on the prior knowledge of its designer rather than its own percepts then the agent lacks autonomy A rational agent should be autonomous- it should learn what it can to compensate for partial or incorrect prior knowledge. E.g., a clock No input (percepts) Run only but its own algorithm (prior knowledge) No learning, no experience, etc.
- 13. Task environments Task environments are the problems While the rational agents are the solutions Specifying the task environment PEAS description as fully as possible Performance Environment Actuators Sensors In designing an agent, the first step must always be to specify the task environment as fully as possible. Use automated taxi driver as an example
- 14. Task environments Performance measure How can we judge the automated driver? Which factors are considered? getting to the correct destination minimizing fuel consumption minimizing the trip time and/or cost minimizing the violations of traffic laws maximizing the safety and comfort, etc.
- 15. Environment A taxi must deal with a variety of roads Traffic lights, other vehicles, pedestrians, stray animals, road works, police cars, etc. Interact with the customer Task environments
- 16. Actuators (for outputs) Control over the accelerator, steering, gear shifting and braking A display to communicate with the customers Sensors (for inputs) Detect other vehicles, road situations GPS (Global Positioning System) to know where the taxi is Many more devices are necessary Task environments
- 17. A sketch of automated taxi driver Task environments
- 18. Properties of task environments Fully observable vs. Partially observable If an agent’s sensors give it access to the complete state of the environment at each point in time then the environment is effectively and fully observable if the sensors detect all aspects That are relevant to the choice of action
- 19. Partially observable An environment might be Partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data. Example: A local dirt sensor of the cleaner cannot tell whether other squares are clean or not.
- 20. Deterministic vs. stochastic next state of the environment Completely determined by the current state and the actions executed by the agent, then the environment is deterministic, otherwise, it is Stochastic. Strategic environment: deterministic except for actions of other agents -Cleaner and taxi driver are: Stochastic because of some unobservable aspects noise or unknown Properties of task environments
- 21. Episodic vs. sequential An episode = agent’s single pair of perception & action The quality of the agent’s action does not depend on other episodes Every episode is independent of each other Episodic environment is simpler The agent does not need to think ahead Sequential Current action may affect all future decisions -Ex. Taxi driving and chess. Properties of task environments
- 22. Static vs. dynamic A dynamic environment is always changing over time E.g., the number of people in the street While static environment E.g., the destination Semidynamic environment is not changed over time but the agent’s performance score does Properties of task environments
- 23. Discrete vs. continuous If there are a limited number of distinct states, clearly defined percepts and actions, the environment is discrete E.g., Chess game Continuous: Taxi driving Properties of task environments
- 24. Single agent VS. multiagent Playing a crossword puzzle – single agent Chess playing – two agents Competitive multiagent environment Properties of task environments
- 26. Structure of agents Agent = architecture + program Architecture = some sort of computing device (sensors + actuators) (Agent) Program = some function that implements the agent mapping = “?” Agent Program = Job of AI
- 27. Agent programs Input for Agent Program Only the current percept Input for Agent Function The entire percept sequence The agent must remember all of them Implement the agent program as A look up table (agent function)
- 28. Agent programs Skeleton design of an agent program
- 29. Agent programs Despite of huge size, look up table does what we want. The key challenge of AI Find out how to write programs that, to the extent possible, produce rational behavior From a small amount of code Rather than a large amount of table entries E.g., a five-line program of Newton’s Method V.s. huge tables of square roots, sine, cosine, …
- 30. Types of agent programs Four types Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents
- 31. Simple reflex agents It uses just condition-action rules The rules are like the form “if … then …” efficient but have narrow range of applicability Because knowledge sometimes cannot be stated explicitly Work only if the environment is fully observable
- 33. Simple reflex agents (2)
- 34. A Simple Reflex Agent in Nature percepts (size, motion) RULES: (1) If small moving object, then activate SNAP (2) If large moving object, then activate AVOID and inhibit SNAP ELSE (not moving) then NOOP Action: SNAP or AVOID or NOOP needed for completeness
- 35. Model-based Reflex Agents For the world that is partially observable the agent has to keep track of an internal state That depends on the percept history Reflecting some of the unobserved aspects E.g., driving a car and changing lane Requiring two types of knowledge How the world evolves independently of the agent How the agent’s actions affect the world
- 36. Example Table Agent With Internal State Saw an object ahead, and turned right, and it’s now clear ahead Go straight Saw an object Ahead, turned right, and object ahead again Halt See no objects ahead Go straight See an object ahead Turn randomly IF THEN
- 37. Example Reflex Agent With Internal State: Wall-Following Actions: left, right, straight, open-door Rules: 1. If open(left) & open(right) and open(straight) then choose randomly between right and left 2. If wall(left) and open(right) and open(straight) then straight 3. If wall(right) and open(left) and open(straight) then straight 4. If wall(right) and open(left) and wall(straight) then left 5. If wall(left) and open(right) and wall(straight) then right 6. If wall(left) and door(right) and wall(straight) then open-door 7. If wall(right) and wall(left) and open(straight) then straight. 8. (Default) Move randomly start
- 38. Model-based Reflex Agents The agent is with memory
- 40. Goal-based agents Current state of the environment is always not enough The goal is another issue to achieve Judgment of rationality / correctness Actions chosen goals, based on the current state the current percept
- 41. Goal-based agents Conclusion Goal-based agents are less efficient but more flexible Agent Different goals different tasks Search and planning two other sub-fields in AI to find out the action sequences to achieve its goal
- 43. Utility-based agents Goals alone are not enough to generate high-quality behavior E.g. meals in Canteen, good or not ? Many action sequences the goals some are better and some worse If goal means success, then utility means the degree of success (how successful it is)
- 45. Utility-based agents it is said state A has higher utility If state A is more preferred than others Utility is therefore a function that maps a state onto a real number the degree of success
- 46. Utility-based agents (3) Utility has several advantages: When there are conflicting goals, Only some of the goals but not all can be achieved utility describes the appropriate trade-off When there are several goals None of them are achieved certainly utility provides a way for the decision-making
- 47. Learning Agents After an agent is programmed, can it work immediately? No, it still need teaching In AI, Once an agent is done We teach it by giving it a set of examples Test it by using another set of examples We then say the agent learns A learning agent
- 48. Learning Agents Four conceptual components Learning element Making improvement Performance element Selecting external actions Critic Tells the Learning element how well the agent is doing with respect to fixed performance standard. (Feedback from user or examples, good or not?) Problem generator Suggest actions that will lead to new and informative experiences.
- 49. Learning Agents