CS 462: Introduction to Artificial Intelligence This course advocates the physical-symbol system hypothesis formulated by Newell and Simon in 1976. It states that intelligence is a functional property and it is completely independent of any physical embodiment. Alternative less-symbolic paradigms are neural networks and evolutionary computation (of which genetic algorithms are the most prominent example).

Intelligence vs Artificial Intelligence Intelligence is a property/ability attributed to people, such as to know, to think, to talk, to learn, to understand. Intelligence = Knowledge + ability to perceive, feel, comprehend, process, communicate, judge, learn. Artificial Intelligence is an interdisciplinary field aiming at developing techniques and tools for solving problems that people at good at.

Definitions of AI Existing definitions advocate everything from replicating human intelligence to simply solving knowledge-intensive tasks. Examples: “Artificial Intelligence is a study of complex information processing problems that often have their roots in some aspect of biological information processing. The goal of the subject is to identify solvable and interesting information processing problems, and solve them.” -- David Marr. “Artificial Intelligence is the design, study and construction of computer programs that behave intelligently.” -- Tom Dean. “Artificial Intelligence is the enterprise of constructing a physical symbol system that can reliably pass the Turing test.” -- Matt Ginsberg. In the textbook, AI is defined as an experimental discipline utilizing the ideas and the methods of computation.

Goals of Artificial Intelligence Scientific goal: understand the mechanism behind human intelligence. Engineering goal: develop concepts and tools for building intelligent agents capable of solving real world problems. Examples: Knowledge-based systems: capture expert knowledge and apply them to solve problems in a limited domain. Common sense reasoning systems: capture and process knowledge that people commonly hold which is not explicitly communicated. Learning systems: have the ability to expend their knowledge based on the accumulated experience. Natural language understanding systems. Intelligent robots. Speech and vision recognition systems. Game playing (IBM’s Deep Blue)

The State of the Art Example: The Semantic Web The Semantic Web is the latest most prominent example of applied AI. It allows data to be linked across the web, and thus understood by computers so that they can perform increasingly sophisticated tasks which were previously delegated to humans. https://www.cambridgesemantics.com/blog/semantic-university/intro-semantic-web/ “The word semantic itself implies meaning or understanding. As such, the fundamental difference between Semantic Web technologies and other technologies related to data (such as relational databases or the World Wide Web itself) is that the Semantic Web is concerned with the meaning and not the structure of data.”

Why “meaning” is the central concept of AI For an entity to be “intelligent”, it must be able to understand the meaning of information. Information is acquired / delivered / conveyed in messages which are phrased in a selected representation language. There are two sides in information exchange: the source (text, image, person, program, etc.) and the receiver (person or an AI agent). They must speak the same “language” for the information to be exchanged in a meaningful way. The receiver must have the ability to interpret the information correctly according to the intended by the source meaning / semantics of it. MEANING = SEMANTICS

Introduction to LISP*) Why LISP? Especially designed for symbol manipulation. Provides built-in support for lists. Automatic storage management (no need to keep track of memory allocation). Interactive environment, which allows programs to be developed step by step, i.e. if a change is to be introduced, only changed functions must be recompiled. *) These lecture notes are based on Winston and Horn LISP, 3rd edition, AddisonWesley.

Allegro Common Lisp (Franz corporation)

Basic terminology Atoms: word-like indivisible objects which can be numbers or symbols. Lists: sentence-like objects formed from atoms or other lists, and enclosed in parentheses. S-expressions: compositions of atoms and lists. Procedures: step by step specifications how to do something. Primitives: procedures supplied by the LISP itself Example: (+ 5 6) User-defined procedures: procedures introduced by the programmer. Example: (students 'anna) Program: a collection of procedures working together.

S-expressions An s-expression can have other s-expressions nested in it. Examples: (+ (* 5 7) ( / 2 4)) (This (is a dog) (or a cat)) Expressions can be interpreted both, procedurally and declaratively. If interpreted procedurally, an expression provides a direction for doing something. Such an expression is called a form, and its first element is the name of a procedure to be used to produce the value. The process of computing the value of an expression is called evaluation. If interpreted declaratively, expressions represent data. Data and procedures have the same syntax.

Evaluation of atoms The value of an atom is an atom itself. Examples: CG-USER(1): 5 5 CG-USER(2): "Hello World" "Hello World" CG-USER(3): T T CG-USER(4): Nil NIL CG-USER(5): () Variables are names of memory locations.

Numbers Integers: 179, 45 Ratio: 5/7, 7/9 Floating point: 5.2, 7.9 Examples: * (/ 25 5) 5 * (/ 46 9) 46/9 ; do not divide evenly * (float (/ 46 9)) 5.111111 * (round (/ 46 9)) 5 ; the nearest integer 1/9 ; the remainder

More numeric primitives * (- 6) -6 * (- -6) 6 * (max 5 7 2) 7 * (min 5 7 2) 2 * (sqrt (* (+ 1 3) (* 2 2))) 4.0 * (+ (round (/ 22 7)) (round (/ 7 3))) 5 * (+ 2 2.5) 4.5 * (expt 3 6) 729 * (sqrt 81) 9.0 * (sqrt 82) 9.055386 * (abs 6) 6 * (abs -6)

Representation of atoms and lists in a computer memory Consider the list (A (B (C))). It can be represented by means of the following diagram: A B These boxes are called cons cells. C

Each cons cell consists of 9 bytes: 1 leading byte, called the data type byte. It holds information indicating that the particular group of 9 bytes is part of a list (i.e. a cons cell). 2 groups of 4 bytes each, representing pointers. Each pointer is an address -- the first one to the memory location containing the first element of the list, and the second one to the memory location storing the rest of the list. The second pointer of the last element of each list contains zeros (representing NIL and empty list). i.e. no cons cell corresponds to the empty list.

The CONS primitive builds new lists Example: Given the list (Education is power), build a new list from it and the atom University. * (cons 'University '(Education is power)) (UNIVERSITY EDUCATION IS POWER) To implement this, LISP maintains a list of free boxes (cons cells), called the free-storage list. CONS removes the first box from the free-storage list, and deposits new pointers into it.

Education is power Free storage list ... University

Dotted pairs Consider the list (A B . C). Here (B . C) is called a dotted pair, and is represented as follows: To construct the list (A B C), we write: * (cons 'A (cons 'B (cons 'C NIL))) To construct the list (A B . C), we write: * (cons 'A (cons 'B 'C)) B C