lecture8 ch14

Artificial Intelligence: A Modern Approach

  • Notes
  • PresidentHackerCaribou10582
  • 11

Info icon This preview shows pages 1–5. Sign up to view the full content.

1 ICS-171:Lecture 8: 1 Lecture 8: Reasoning Under Uncertainty ICS 171, Summer 2000 ICS-171:Lecture 8: 2 Outline Autonomous Agents need to be able to handle uncertainty Probability as a tool for uncertainty basic principles Decision-Marking and Uncertainty optimal decision-making principle of maximum expected utility
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

2 ICS-171:Lecture 8: 3 Autonomous Agents Consider an agent which is reasoning, planning, making decisions Real World Current “World Model” List of possible Actions Background Knowledge Sensors Effectors Reasoning and Decision Making Agent or Robot Goals ICS-171:Lecture 8: 4 How an Agent Operates Basic Cycle use sensors to sense the environment update the world model reason about the world (infer new facts) update plan on how to reach goal make decision on next action use effectors to implement action Basic cycle is repeated until goal is reached
Image of page 2
3 ICS-171:Lecture 8: 5 Example of an Autonomous Agent A robot which drives a vehicle on the freeway Freeway Environment Model of: vehicle location freeway status road conditions Actions accelerate steer slow down Prior Knowledge: physics of movement rules of the road Sensors: Camera Microphone Tachometer Engine Status Temperature Effectors: Engine control Brakes Steering Camera Pointing Reasoning and Decision Making Driving Agent Goal: drive to Seattle ICS-171:Lecture 8: 6 The Agent’s World Model World Model = internal representation of the external world – combines background knowledge current inputs Necessarily, the world model is a simplification e.g. in driving we cannot represent every detail every pebble on the road? details of every person in every other vehicle in sight? A useful model is the State Space model - We used it in Search represent the world as a set of discrete states e.g., variables = {Rainy, Windy, Temperature, ..... } state = {rain=T, windy=T, Temperature = cold, ..... } An agent must 1. figure out what state the world is in 2. figure out how to get from the current state to the goal
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

4 ICS-171:Lecture 8: 7 Uncertainty in the World Model The agent can never be completely certain about the external world state. i.e., there is ambiguity and uncertainty Why? sensors have limited precision e.g., camera has only so many pixels to capture an image sensors have limited accuracy e.g., tachometer’s estimate of velocity is approximate there are hidden variables that sensors can’t “see” e.g., large truck behind vehicle e.g., storm clouds approaching the future is unknown, uncertain: i.e., we cannot foresee all possible future events which may happen In general, our brain functions this way too: we have a limited perception of the real-world ICS-171:Lecture 8: 8 Rules and Uncertainty Say we have a rule if toothache then problem = cavity
Image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern