1 / 24

Lecture 2: Intelligent Agents

Lecture 2: Intelligent Agents. Reading: AIMA, Ch. 2. What is an agent?. An entity in an environment that perceives it through sensors and acts upon it through actuators. Percepts. Environment. Agent. sensor.

mmcneal
Download Presentation

Lecture 2: Intelligent Agents

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 2: Intelligent Agents Reading: AIMA, Ch. 2 Rutgers CS440, Fall 2003

  2. What is an agent? • An entity in an environment that perceives it through sensors and acts upon it through actuators. Percepts Environment Agent sensor Modified from "A Kalman Filter Model of the Visual Cortex", by P. Rao, Neural Computation 9(4):721--763, 1997 actuator Actions Agents: human, robot, softbot, thermostat, etc. Agents act on environment to achieve a goal. Rutgers CS440, Fall 2003

  3. Agent function & program • Agent’s choice of action is based on a sequence of percepts • Agent is specified by an agent function f that maps sequences of percepts Y to actions a: • Agent program implements agent function on a physical architecture • “Easy” solution: table that maps every possible sequence Y to an action a • Problem: not feasible Rutgers CS440, Fall 2003

  4. Example: Vacuum-cleaner world • Percepts: location and contents, e.g., (A,dirty) • Actions: move, clean, do nothing: LEFT, RIGHT, SUCK, NOP A B Rutgers CS440, Fall 2003

  5. Vacuum-cleaner world: agent function • What is the rightfunction? • Can the function be implemented in a “short” program? Rutgers CS440, Fall 2003

  6. The “right” agent function – rational behavior • Rational agent is the one that does the “right thing”: functional table is filled out correctly • What is the “right thing”? • Define success through a performance measure, r • Vacuum-cleaner world: • +1 point for each clean square in time T • +1 point for clean square, -1 for each move • -1000 for more than k dirty squares • Rational agent:An agent who selects an action that is expected to maximize the performance measure for a given percept sequence and its built-in knowledge • Ideal agent: maximizes actual performance, but needs to be omniscient. Impossible! • Builds a model of environment. Rutgers CS440, Fall 2003

  7. Properties of a rational agent • Maximize expected performance • Gathers information – does actions to modify future percepts • Explores – in unknown environments • Learns – from what it has perceived so far(dung beetle, sphex wasp) • Autonomous – increase its knowledge by learning Rutgers CS440, Fall 2003

  8. Task environment • To design a rational agent we need to specify a task environment = problem to which the agent is a solution • P.E.A.S. = Performance measure Environment Actuators Sensors • Example: automated taxi driver • Performance measure: safe, fast, legal, comfortable, maximize profits • Environment: roads, other traffic, pedestrians, customers • Actuators: steering, accelerator, brake, signal, horn • Sensors: cameras, sonar, speedometer, GPS Rutgers CS440, Fall 2003

  9. More PEAS examples • College test-taker • Internet shopping agent • Mars lander • The president • … Rutgers CS440, Fall 2003

  10. Properties of task environments Rutgers CS440, Fall 2003

  11. Properties of task environments (cont’d) Rutgers CS440, Fall 2003

  12. Properties of task environments (cont’d) Rutgers CS440, Fall 2003

  13. Properties of task environments (cont’d) Rutgers CS440, Fall 2003

  14. Properties of task environments (cont’d) Rutgers CS440, Fall 2003

  15. Properties of task environments (cont’d) Rutgers CS440, Fall 2003

  16. Properties of task environments (cont’d) Rutgers CS440, Fall 2003

  17. at yt st Structure of agents • Goal of AI: give task environment, construct agent function, and design an agent program that implements agent function on a particular architecture • Skeleton agent: function SKELETON-AGENT(perceptt) returns actiont static: state, the agent’s memory of the world statet = Update-State(statet-1,…,perceptt,actiont-1) actiont = Choose-Best-Action(statet) statet = Update-Memory(statet,actiont) return action Rutgers CS440, Fall 2003

  18. at st yt Skeleton agent • Graphical depiction (we will see more of it later in the semester) at+1 st+1 … … yt+1 Rutgers CS440, Fall 2003

  19. Agent types • Simplest agent:Table-driven agent: for each percept sequence Y, has a table entry with associated action function TABLE-DRIVEN-AGENT( percept ) returns action static: sequence or percepts percepts = Update-Percepts( percept ) action = Table( percepts ) return action • Four basic types, in order of increasing complexity • Simple reflex agent • Model-based reflex agent (reflex agent with state) • Goal-based agent • Utility-driven agent Rutgers CS440, Fall 2003

  20. Simple reflex agent function REFLEX_VACUUM_AGENT( percept ) returns action (location,status) = UPDATE_STATE( percept ) if status = DIRTY then action = SUCK; else if location = A then return RIGHT; else if location = B then return LEFT; return action Rutgers CS440, Fall 2003

  21. Model-based reflex agent Rutgers CS440, Fall 2003

  22. Goal-driven agent Rutgers CS440, Fall 2003

  23. Utility-based agent Rutgers CS440, Fall 2003

  24. Learning agent Any other agent Rutgers CS440, Fall 2003

More Related