Introduction to
Artificial Intelligence
CIS 3203
Intelligent Agents
Intelligent agent (IA)
an autonomous entity
which observes through sensors (aka “perceptors”)
and acts upon an environment using actuators (aka “effectors”)
and directs its activity towards achieving goals
(i.e. it is rational in the sense of economics theory:
more of a good thing is better than less of it).
Intelligent agents may also
learn
or use knowledge
They may be
simple: a reflex machine such as a thermostat is an intelligent agent,
or very complex: a robot, a human being, or a community of human beings
working together towards a goal.
Perception-Action Cycle
Environment
Agent
sensors
The part
we mainly
study in
this class
percepts
?
actuators
actions
Applications
Trading
Agent
News,
Prices
Financial
Markets:
Bonds
Stocks
buy
or sell
Commodities
Applications
Robot
Physical World
cameras,
microphones,
tactile sensors
wheels,
grippers
Applications
Your body
Diagnostic
Agent
Blood pressure,
Other diagnostic
tests
diagnoses
Applications
The Web
Web
Search
Engine
Web crawler
DB
User query
Top ten links
Search
Engine
User
Quiz
What’s the difference between an “intelligent
agent” as I’ve defined it, and a computer
program?
Environment Types
1. Observability:
Fully observable vs. Partially observable
Chess is fully observed: a player gets to see the
whole board.
Poker is partially observable: a player gets to see
only his own cards, not the cards of everyone in the
game.
Environment Types
2. Action outcomes
Deterministic outcomes vs. Stochastic outcomes
Chess has deterministic action outcomes: given a board
position, if a player makes a particular move, the resulting
board position is always the same.
Backgammon has stochastic outcomes: on a player’s turn, she
or he rolls dice to see how many moves can be made. The
outcome of the dice roll has randomness to it (called
stochasticity).
Environment Types
3. Environment size / countability
Discrete state space vs. Continuous state space
Chess has a discrete environment: there are finitely many
board positions.
Darts has a continuous environment: there is a range of
places where your dart could end up, but within that
range is an (uncountably) infinite set of possible places
the dart could stick.
Environment Types
4. Is the environment out to get you?
Benign environment vs. adversarial environment
Most games, like chess and poker, are adversarial: the environment
(which includes the agent’s opponent) is trying to stop the agent from
achieving its goal(s).
Weather, or robot navigation problems, or search engine queries, are
usually treated as benign environments: the environment can still hurt
the agent, but usually there is no intelligent agent in the environment
that is actively trying to hurt the agent.
Quiz: Environment Types
For each environment below, decide which
categories it belongs to.
Environment Type
Fully observable or
Partially observable
Stochastic or
Deterministic
Discrete or
Continuous
Benign or
Adversarial
Checkers
Poker
Robot Car
Key Aspects of Intelligent Agents
Representation language: what formal language (or data structure) should we use to
describe what an agent knows?
databases? propositional logic? probability theory?
graphs? First-order logic? Markov logic, Bayesian networks, …?
Inference mechanism: what procedure(s) can an agent use to deduce new knowledge
from what it already knows?
search, probabilistic reasoning, modus ponens, resolution theorem-proving?
Learning mechanism: what procedure(s) can an agent use to improve its performance
based on past observations?
perceptron, Bayes learning, unsupervised learning, EM, regression, …
-----------------------------This isn’t part of the agent itself, but a key aspect of designing the agent:
Evaluation procedure: what test(s) should we use to judge how well an agent is
performing?
Uncertainty in AI
AI is sometimes described as the discipline that studies
what to do in the face of uncertainty .
Reasons for uncertainty:
Stochastic environments
Adversarial environments
Partially observable environments
Agent limits:
sensor limits
memory or storage limits/ignorance
computational limits/laziness
Probabilistic Reasoning and
Uncertainty
In the past 20 years, Probability Theory has become one
of the most important tools for AI.
E.g., if there’s a 20% chance of rain on any given night,
and 40% chance that it rained overnight if the grass is wet
in the morning, what’s the probability that it rained last
night if the grass is dry in the morning?
We’ll begin covering this type of reasoning in the second
week.