REVISED: Wednesday,February 4, 2015
I. AGENT
An AI program is called an intelligent agent.
A. There are four key attributes of an intelligent agent:
1. Partial observability
2. Stochasticity
3. Continuous spaces
4. Adversarial natures
Instead of calling AI "non-human" and having to deal with the negative connotation of "non-human" intelligence we will use the euphemism intelligent "agent." And define "intelligent agent" to be anything that has sensors to perceive its environment and actuators to act upon its environment based on its perceptions.
A. There are four key attributes of an intelligent agent:
1. Partial observability
2. Stochasticity
3. Continuous spaces
4. Adversarial natures
II. RATIONALITY
When we describe a human as rational we imply their actions are appropriate considering the circumstances occurring prior to their actions. We say they are behaving rationally and not inappropriately.
Accordingly we will define a rational agent as an agent that does the right thing, considering the consequences of doing the wrong thing.
By environment we mean the task environment.
Before we begin the design of a solution, an agent, we want a complete description of the problem, the task environment.
A. Fully versus partially observable.
During an agent's perception action cycle:
Fully observable means the agent can see the entire state of the environment.
Partially observable means the agent can only see a fraction of the state of the environment.
B. Deterministic versus Stochastic.
Deterministic means the outcome is not random, it is predetermined.
Stochastic means the outcome is random, it is not predetermined.
C. Discrete versus Continuous.
Discrete means your choices are finite.
Continuous means your choices are infinite.
D. Benign versus Adversarial.
A benign environment is just business its not personal.
An adversarial environment is personal, its out to get you.
V. AI as Uncertainty Management
When you are uncertain about makings a choice, AI helps you make the choice.
VI. Reasons for Uncertainty
A. Sensor Limits.
B. Adversaries.
C. Stochastic Environment.
D. Laziness.
E. Ignorance.
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VII. Examples
A. Checkers
1. Fully Observable
2. Deterministic
3. Discrete
4. Adversarial
B. Poker
1. Partially Observable
2. Stochastic
3. Adversarial
C. Robot Car
1. Partially Observable
2. Stochastic
3. Continuous
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