lecture8 ch14

Artificial Intelligence: A Modern Approach

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

View Full Document Right Arrow Icon
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
Background image of page 1

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

View Full DocumentRight Arrow Icon
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
Background 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
Background image of page 3

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

View Full DocumentRight Arrow Icon
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
Background image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 11

lecture8 ch14 - Lecture 8: Reasoning Under Uncertainty ICS...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online