Lecture Notes Artificial Intelligence Definition Dae-Won Kim School

Lecture Notes Artificial Intelligence: Definition Dae-Won Kim School of Computer Science & Engineering Chung-Ang University

What are AI Systems?

Deep Blue defeated the world chess champion Garry Kasparov in 1997

During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50, 000 vehicles, cargo, and people

Proverb solves crossword puzzles better than most humans

Sony’s AIBO and Honda’s ASIMO

Web Agents & Search engines: Google, Yahoo

Recognition Systems: Speech, Character, Face, Iris, Fingerprint

Virtual Reality and Computer Vision

Potted History of AI 1943 1950 s 1956 1965 1966 1969 1980 1988 1985 1988 1995 2000 2003 Mc. Culloch & Pitts: Boolean circuit model of brain Turing’s “Computing Machinery and Intelligence” Early AI programs Dartmouth meeting: “Artificial Intelligence” adopted Robinson’s complete algorithm for logical reasoning AI discovers computational complexity Neural network research almost disappears Early development of knowledge-based systems Expert systems industry booms Expert systems industry busts: “AI Winter” Neural networks return to popularity Resurgence of probability, soft computing. Agents, agents, everywhere … with Data Mining Bioinformatics powered by Human Genome Project Human-level AI back on the agenda: challengeable

Some researchers consider AI as one of the four concepts:

1. Systems that think like humans

2. Systems that think rationally

3. Systems that act like humans

4. Systems that act rationally

AI: Acting humanly

Turing (1950): “The Turing Test”

Can machines think?

Can machines behave intelligently?

Turing test is The ‘Imitation’ Game

Predicted that by 2000, a machine might have 30% chance of fooling a lay person for 5 min. In 2014, something has happened. http: //www. bbc. com/news/technology-27762088

Problem: Turing test is NOT …

Turing test is NOT reproducible and amendable to mathematical analysis

AI: Thinking humanly

It requires scientific theories of internal activities of the brain

What level of abstraction? “Knowledge” or “circuits”.

How to validate? Requires something

Requires: Cognitive Science Predicting and testing behavior of human subjects (top-down)

Requires: Cognitive Neuroscience Direct identification from neurological data (bottom up)

Problem: Thinking humanly is NOT

Both are distinct from AI in CS The available theories do not explain anything resembling human-level general intelligence.

AI: Thinking rationally

Laws of Thought: “What are correct arguments/thought processes? ” by Aristotle

Several Greek schools developed various forms of logic:

Logic: notation and rules of derivation of thoughts

Problem: Thinking rationally is NOT

Not all intelligent behavior is mediated by logical deliberation

AI: Acting rationally

Rational behavior: doing the RIGHT thing

The RIGHT thing: that which is expected to maximize goal achievement, given the available information

An agent is an entity that perceives and acts.

Agents include humans, robots, programs, systems, etc.

This course is about designing rational agents/SWs/programs/platforms.

Abstractly, an agent is a function from percept histories to actions f: P A

The agent program runs on the physical architecture to produce f

For any given class of tasks and environments, we seek the agent with the best performance.

Problem: Acting rationally is NOT

Computational limitations make perfect rationality unachievable e. g. ) NP-hard problems

Design best program for given machine resources

Which of the following can be done at present? • • • Play a decent game of table tennis Drive safely along a curving mountain road Drive safely along Telegraph Avenue Buy a week’s worth of groceries on the web Discover and prove a new mathematical theorem Design and execute a research program in biology Write an intentionally funny story Give legal advice in a specialized area of law Translate spoken English into Swedish in real time Perform a complex surgical operation Converse successfully with another person for an hour

Artificial Intelligence Intelligent Agents Dae-Won Kim School of Computer Science & Engineering Chung-Ang University

The agent function maps from percept histories to actions: f: P A

A Vacuum-cleaner Agent

• Perception: ? • Actions: ?

• Perception: location and contents [A, Dirty]. • Actions: Left, Right, Suck, No. Op

Problem: A Vacuum-cleaner Agent

What is the right function?

Let’s talk about Rationality

A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date

What is performance measure?

1 point per square cleaned up in time T?

Minus 1 point per move?

Penalize for > k dirty squares?

Therefore, we can say

Rational omniscient

Perception may not supply all information

Rational clairvoyant

Action outcomes may not be as expected

Hence, rational perfect

To design a rational agent, we must specify the task environment (PEAS)

• Performance measure • Environment • Actuators • Sensors

Consider the task of designing the Google driverless car

• P: safety, comfort, profits, legality • E: streets, freeways, traffic, weather • A: streering, accelerator, break • S: velocity, GPS, engine sensors

Consider the task of designing an automated internet shopping agent: e. g. , Recommender system

• P: price, quality, efficiency • E: WWW sites, vendors • A: display to user, follow URL • S: HTML, XML pages

Agent Types: four basic types in order of increasing generality

• Simple reflex agents • Reflex agents with state • Goal-based agents • Utility-based agents

Simple Reflex Agents 1. If a student sleeping, then assign a penalty. 2. When applied to Vehicle driving?

Reflex Agents with State 1. Check the student’s academic history, habits. . 2. Vehicle driving ?

Goal-based Agents 1. Consider goals: be a good professor in AI class 2. Vehicle driving ?

Utility-based Agents 1. Utility: performance measure 2. How good grade I will assign / Prof. I could be.

Learning Agents All agents will be turning into learning agents.