1. Introduction to Artificial Intelligence
CIS 3203

2. 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.

3. Perception-Action Cycle
Environment
Agent
percepts
sensors ?
The part we mainly study in this class
actions
actuators

4. Applications Financial Markets: Trading Agent Bonds Stocks Commodities
News,
Prices
buy
or sell

5. microphones, tactile sensors
Applications
Physical World
Robot
cameras,
microphones, tactile sensors
wheels,
grippers

6. Other diagnostic tests
Applications
Your body
Diagnostic Agent
Blood pressure,
Other diagnostic tests
diagnoses

7. Applications The Web Web Search Engine DB Search Engine User
Web crawler DB
User query
Search Engine User
Top ten links

8. Quiz
What’s the difference between an “intelligent agent” as I’ve defined it, and a computer program?

9. 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.

10. 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).

11. 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.

12. 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.

13. Quiz: Environment Types
For each environment below, decide which categories it belongs to.
Environment Type
Checkers
Poker
Robot Car
Fully observable or
Partially observable
Stochastic or
Deterministic
Discrete or
Continuous
Benign or
Adversarial

14. 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?

15. 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

16. 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.