Скачать презентацию Intelligent Agents Overview Slides based in part on Скачать презентацию Intelligent Agents Overview Slides based in part on

d0a01159c0e731999e81b1cc00b9ffcd.ppt

  • Количество слайдов: 31

Intelligent Agents Overview Slides based in part on Hwee Tou Ng, aima. eecs. berkeley. Intelligent Agents Overview Slides based in part on Hwee Tou Ng, aima. eecs. berkeley. edu/slides-ppt, which are in turn based on Russell, aima. eecs. berkeley. edu/slides-pdf. CSC 9010 Spring 2011. Paula Matuszek

Agents • An agent is anything that can be viewed as perceiving its environment Agents • 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 • Scooter: touch and rotation sensors; wheels CSC 9010 Spring 2011. Paula Matuszek 2

Agents and environments • The agent function maps from percept histories to actions: [f: Agents and environments • 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 CSC 9010 Spring 2011. Paula Matuszek 3

Vacuum-cleaner world • Percepts: location and contents, e. g. , [A, Dirty] • Actions: Vacuum-cleaner world • Percepts: location and contents, e. g. , [A, Dirty] • Actions: Left, Right, Suck, No. Op CSC 9010 Spring 2011. Paula Matuszek 4

A vacuum-cleaner agent Percept sequence Action [A, Clean] Right [A, Dirty] Suck [B, Clean] A vacuum-cleaner agent Percept sequence Action [A, Clean] Right [A, Dirty] Suck [B, Clean] Left [B, Dirty] Suck [A, Clean], [A, Clean] Right [A, Clean], [A, Dirty] Suck … … CSC 9010 Spring 2011. Paula Matuszek 5

Rational agents • An agent should strive to 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. CSC 9010 Spring 2011. Paula Matuszek 6

Rational agents • Rational Agent: For each possible percept sequence, a rational agent should Rational agents • 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. • Thus, rationality depends on – – performance measure that defines success prior knowledge of the environment actions the agent can perform percept sequence to date CSC 9010 Spring 2011. Paula Matuszek 7

Rational agents • Rationality is distinct from omniscience (all -knowing with infinite knowledge) • Rational agents • Rationality is distinct from omniscience (all -knowing with infinite knowledge) • Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration) • An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt) CSC 9010 Spring 2011. Paula Matuszek 8

Task Environment • An agent operates within some task environment, not in a blank Task Environment • An agent operates within some task environment, not in a blank world. • This environment includes: – what the agent is trying to do – what resources it has to do it • The nature of the environment affects how we design an appropriate agent. CSC 9010 Spring 2011. Paula Matuszek 9

PEAS: Specifying an Agent's World • The task environment for an agent can be PEAS: Specifying an Agent's World • The task environment for an agent can be completely specified by defining four things: – Performance measure: How do we assess whether we are doing the right thing? – Environment: What is the world we are in? – Actuators: How do we affect the world we are in? – Sensors: How do we perceive the world we are in? This PEAS specification gives us the information we need to design a rational agent. CSC 9010 Spring 2011. Paula Matuszek 10

PEAS: Taxi Driver • Consider, e. g. , the task of designing an automated PEAS: Taxi Driver • Consider, e. g. , 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 CSC 9010 Spring 2011. Paula Matuszek 11

PEAS: • Agent: Lego Scooter • Performance: number of seconds it keeps moving without PEAS: • Agent: Lego Scooter • Performance: number of seconds it keeps moving without getting stuck • Environment: Flat surface with objects but no dropoffs. • Actuators: Motors which turn two wheels • Sensors: touch sensors, rotation sensors CSC 9010 Spring 2011. Paula Matuszek 12

PEAS • Agent: Medical diagnosis system – Performance measure: – Environment: – Actuators: – PEAS • Agent: Medical diagnosis system – Performance measure: – Environment: – Actuators: – Sensors: CSC 9010 Spring 2011. Paula Matuszek 13

PEAS • Agent: Medical diagnosis system • Performance measure: Healthy patient, minimize costs, lawsuits PEAS • Agent: Medical diagnosis system • Performance measure: Healthy patient, minimize costs, lawsuits • Environment: Patient, hospital, staff • Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) • Sensors: Keyboard (entry of symptoms, findings, patient's answers) CSC 9010 Spring 2011. Paula Matuszek 14

PEAS • • • Agent: Interactive English tutor Performance measure: Environment: Actuators: Sensors: CSC PEAS • • • Agent: Interactive English tutor Performance measure: Environment: Actuators: Sensors: CSC 9010 Spring 2011. Paula Matuszek 15

PEAS • Agent: Interactive English tutor • Performance measure: Maximize student's score on test 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 CSC 9010 Spring 2011. Paula Matuszek 16

Agent functions and programs • An agent is completely specified by the agent function Agent functions and programs • An agent is completely specified by the agent function mapping percept sequences to actions • A rational agent function maximizes the average performance for a given environment class • Aim: find a way to implement the rational agent function concisely CSC 9010 Spring 2011. Paula Matuszek 17

Agent types • Four basic types in order of increasing generality: • Simple reflex Agent types • Four basic types in order of increasing generality: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents CSC 9010 Spring 2011. Paula Matuszek 18

Simple reflex agents CSC 9010 Spring 2011. Paula Matuszek 19 Simple reflex agents CSC 9010 Spring 2011. Paula Matuszek 19

Simple reflex Vacuum Agent function REFLEX-VACUUM-AGENT ([location, status]) return an action if status == Simple reflex Vacuum Agent function REFLEX-VACUUM-AGENT ([location, status]) return an action if status == Dirty then return Suck else if location == A then return Right else if location == B then return Left • Observe the world, choose an action, implement action, done. • Problems if environment is not fully-observable. • Depending on performance metric, may be inefficient. • Even this simple agent can have a knowledge base, in the form of condition-action rules. CSC 9010 Spring 2011. Paula Matuszek 20

Model-Based Agents • Suppose moving has a cost? • If a square stays clean Model-Based Agents • Suppose moving has a cost? • If a square stays clean once it is clean, then this algorithm will be extremely inefficient. • A very simple improvement would be – Record when we have cleaned a square – Don’t go back once we have cleaned both. • We have built a very simple model. CSC 9010 Spring 2011. Paula Matuszek 21

Reflex Agents with State CSC 9010 Spring 2011. Paula Matuszek 22 Reflex Agents with State CSC 9010 Spring 2011. Paula Matuszek 22

Reflex Agents with State § More complex agent with model: a square can get Reflex Agents with State § More complex agent with model: a square can get dirty again. Function REFLEX_VACUUM_AGENT_WITH_STATE ([location, status]) returns an action. last-cleaned-A and last-cleaned-B initially declared = 100. Increment last-cleaned-A and last-cleaned-B. if status == Dirty then return Suck if location == A then set last-cleaned-A to 0 if last-cleaned-B > 3 then return right else no-op else set last-cleaned-B to 0 if last-cleaned-A > 3 then return left else no-op § The value we check last-cleaned against could be modified. § Could track how often we find dirt to compute value CSC 9010 Spring 2011. Paula Matuszek 23

Model-Based = Reflex Plus State • Maintain an internal model of the state of Model-Based = Reflex Plus State • Maintain an internal model of the state of the environment • Over time update state using world knowledge – How the world changes – How actions affect the world • Agent can operate more efficiently • More effective than a simple reflex agent for partially observable environments • May use a KB for both condition-action rules and what world/actions do. CSC 9010 Spring 2011. Paula Matuszek 24

Goal-based agents CSC 9010 Spring 2011. Paula Matuszek 25 Goal-based agents CSC 9010 Spring 2011. Paula Matuszek 25

Goal-Based Agent • Agent has some information about desirable situations • Needed when a Goal-Based Agent • Agent has some information about desirable situations • Needed when a single action cannot reach desired outcome • Therefore performance measure needs to take into account "the future". • Typical model for search and planning. CSC 9010 Spring 2011. Paula Matuszek 26

Utility-based agents CSC 9010 Spring 2011. Paula Matuszek 27 Utility-based agents CSC 9010 Spring 2011. Paula Matuszek 27

Utility-Based Agents • Possibly more than one goal, or more than one way to Utility-Based Agents • Possibly more than one goal, or more than one way to reach it • Some are better, more desirable than others • There is a utility function which captures this notion of "better". • Utility function maps a state or sequence of states onto a metric. CSC 9010 Spring 2011. Paula Matuszek 28

Learning agents CSC 9010 Spring 2011. Paula Matuszek 29 Learning agents CSC 9010 Spring 2011. Paula Matuszek 29

Learning Agents • All agents have methods for selection actions. • Learning agents can Learning Agents • All agents have methods for selection actions. • Learning agents can modify these methods. • Performance element: any of the previously described agents • Learning element: makes changes to actions • Critic: evaluates actions, gives feedback to learning element • Problem generator: suggests actions CSC 9010 Spring 2011. Paula Matuszek 30

Summary • We can view most intelligent systems as agents. • An agent operates Summary • We can view most intelligent systems as agents. • An agent operates in a world which can be described by its Performance measure, Environment, Actuators, and Sensors. • A rational agent chooses actions which maximize its performance measure, given the information it has. • Agents range in complexity from simple reflex agents to complex utility-based and learning agents. CSC 9010 Spring 2011. Paula Matuszek 31