Artificial Intelligence: A Modern Approach

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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

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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

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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

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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
•