An Introduc+on to Ar+ficial Intelligence 1
1
2
8
4
7
6
...
3
5
3
4
7
5
6
1
2
7
4
5
6
2
3
8
6
4
...
3
4
8
7
5
7
7
5
...
1
...
...
3
1
3
2
4
8
2
4
6
5
7
6
5
1
8
6
1
7
...
2
State
...
3
8
...
1
...
2
8
2
3
8
4
6
...
5
...
Introduc+on • 
• 
• 
• 
Ge3ng machines to “think” . Imita+on game and the Turing test. Chinese room test. Key processes of AI: –  Search, e.g. breadth first search, depth first search, heuris+c searches. –  Knowledge representa+on, e.g. predicate logic, rule-­‐based systems, seman+c networks. Areas of AI Game playing Theorem proving Expert systems Natural language processing Modeling human performance Planning and Robo+cs Neural-­‐networks Evolu+onary algorithms and other biologically inspired methods •  Agent-­‐based technology • 
• 
• 
• 
• 
• 
• 
• 
Game Playing •  Ge3ng the computer to play certain board games that require “intelligence”, e.g. chess, checkers, 15-­‐puzzle. •  A state space of the game is developed and a search applied to the space to look ahead. •  Example: Deep blue vs. Kasparov. . Theorem Proving •  Automa+c theorem proving. •  Generate proofs for simple theorems. •  Mathema+cal logic forms the basis of these systems. •  The “General Problem Solver” is one of the first systems. . Expert Systems •  Performs the task of a human expert, e.g. a doctor, a psychologist. •  Knowledge from an expert is stored in a knowledge base. •  Examples: ELIZA, MYCIN, EMYCIN •  Suitable for specialized fields with a clearly defined domain. . Natural Language Processing •  Develop systems that are able to “understand” a natural language such as English. •  Voice input systems, e.g. Dragon. •  Systems that “converse” in a par+cular language. •  Examples: SHRDLU and ELIZA . Modeling Human Performance •  Systems that model some aspect of problem solving. •  Examples: Intelligent tutoring systems that provide individualized instruc+on in a specific domain. . Planning and Robo+cs •  Involves designing flexible and responsive robots. •  Lists of ac+ons to be performed are generated. •  Aimed at high-­‐level tasks, e.g. moving a box across the room. •  Has led to agent-­‐oriented problem solving. Neural Networks •  Aimed of low-­‐level processing. •  Are essen+ally mathema+cal models of the human brain. •  A neuron: Dendrites
Axon
Cell Body
. Synapse
Evolu+onary Algorithms & Other Nature-­‐Inspired Algorithms •  Based on Darwin’s theory of evolu+on. •  An ini+al popula+on of randomly created individuals is itera+vely refined un+l a solu+on is found. •  Examples: gene+c algorithms, gene+c programming, meme+c algorithms •  Other methodologies: ant coloniza+on, swarm intelligence. . Uncertainty Reasoning •  Uncertain terms may need to be presented. •  Example: represen+ng terms such as “big” or “small”. •  Methods for this purpose: – Fuzzy logic – Bayesian reasoning and networks . Agent-­‐based Technology •  Intelligent agents, also called “so_bots”, are used to perform mundane tasks or solve problems. •  In a mul+-­‐agent system agents communicate using an agent communica+on language. . Ar+ficial Intelligence Languages •  Programming paradigms •  Ar+ficial intelligence languages – Prolog and Lisp •  Prolog (Programming Logic) – declara+ve – predicate logic •  Lisp (List Processing) – func+onal – code takes the form of recursive func+ons. •  More recently AI systems have been developed in a number of languages including Smalltalk, C, C++ and Java.