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CS 8520: Artificial Intelligence

CS 8520: Artificial Intelligence. Intelligent Agents Paula Matuszek Fall, 2008. Slides based on Hwee Tou Ng, aima.eecs.berkeley.edu/slides-ppt, which are in turn based on Russell, aima.eecs.berkeley.edu/slides-pdf. Outline. Agents and environments Rationality

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CS 8520: Artificial Intelligence

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  1. CS 8520: Artificial Intelligence Intelligent Agents Paula Matuszek Fall, 2008 Slides based on Hwee Tou Ng, aima.eecs.berkeley.edu/slides-ppt, which are in turn based on Russell, aima.eecs.berkeley.edu/slides-pdf.

  2. Outline • Agents and environments • Rationality • PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types • Agent types Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  3. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  4. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  5. Vacuum-cleaner world • Percepts: location and contents, e.g., [A,Dirty] • Actions: Left, Right, Suck, NoOp Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  6. A vacuum-cleaner agent Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  7. 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  8. 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  9. 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) Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  10. PEAS: Description of an Agent's World • 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? • Together these specify the setting for intelligent agent design Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  11. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  12. 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) Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  13. 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) Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  14. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  15. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  16. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  17. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  18. Environment types • Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic) • Episodic(vs. sequential): The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  19. Environment types • Static(vs. dynamic): The environment is unchanged while an agent is deliberating. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does) • Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions. • Single agent (vs. multiagent): An agent operating by itself in an environment. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  20. Environment types Chess with Chess without Taxi a clock a clock driving Fully observable Deterministic Episodic Static Discrete Single agent Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  21. Environment types Chess with Chess w/out Taxi a clock a clock driving Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No Yes No Static Semi Yes No Discrete Yes Yes No Single agent No No No • The environment type largely determines the agent design • The simplest environment is fully observable, deterministic, episodic, static, discrete and single-agent. • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  22. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  23. Table-lookup agent Function TABLE-DRIVEN_AGENT(percept) returns an action append percept to the end of percepts action  LOOKUP(percepts, table) return action • Drawbacks: • Huge table • Takes a long time to build the table • Takes a long time to find entries • No autonomy • Surely we can do better! Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  24. Agent types • Four basic types in order of increasing generality: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  25. Simple reflex agents Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  26. 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  27. 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  28. Reflex Agents with State Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  29. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  30. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  31. Goal-based agents Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  32. 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  33. Utility-based agents Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  34. 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  35. Learning agents Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  36. 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 Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

  37. Summary • We will view our 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. Paula Matuszek, CSC 8520, Fall 2008. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

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