UOB Open Artificial Intelligence: Logic Programming III Oliver Ray base case Translation From Haskell: Example recursive case length([],X) :- X =0 . UOB Open %length[] =0 % length (_:l) = 1 + length l length([_|L], X) :- X is 1 + Y , length(L, Y) . length([], 0). length([_|L], X) :- length(L, Y) , X […]
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Unit1-Introduction To introduce what this part of the course is going to cover, let’s pose and look again at the meaning of the term “Artificial Intelligence”. Intelligence can be defined in many ways and if you look around at various definition you will see that it surely includes the ability to understand, learn, acquire and
Introduction to AI Knowledge Representation and Reasoning Introduction to AI Some applications of KRR Francesca Toni (thanks to Fariba Sadri) Outline • Logical agents • Logic-based Production System • (Rule-based robotics) • Ontologies 2 Section 7.1 Section 12.5 Section 2.4 Section 14.3 Logical agents 3 Perceive Reason and choose action(s) Execute action(s) inference engine knowledge
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COMP2610 / COMP6261 – Information Theory – Lecture 3: Probability Theory and Bayes’ Rule COMP2610 / COMP6261 – Information Theory Lecture 3: Probability Theory and Bayes’ Rule Robert C. Williamson Research School of Computer Science 1 L O G O U S E G U I D E L I N E S T H
COMP 1.41 INTRODUCTION TO LOGIC 1 Logic and AI Programming: Introduction to Logic Introduction to Prolog F. SADRI Autumn Term October – December 2 Course Material Course material will be available via CATE, including: Notes Tutorial Exercises Tutorial Exercise Solutions Coursework 3 CONTENTS Introduction to logic Propositional logic • Syntax • Semantics (Truth Tables) •
1intro 1 COMP9318: Data Warehousing and Data Mining — L1: Introduction — 2 Chapter 1. Introduction n Motivation: Why data mining? n What is data mining? n Data Mining: On what kind of data? n Data mining functionality n Are all the patterns interesting? n Classification of data mining systems n Major issues in data
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L19 – Unsupervised Learning and Clustering EECS 391 Intro to AI Unsupervised Learning and Clustering L19 Tue Nov 13 1 2 3 4 5 6 7 0.5 1.0 1.5 2.0 2.5 petal length (cm) pe ta l w id th (c m ) Fisher’s Iris data (unlabeled) 1 2 3 4 5 6 7 0
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Introduction to AI -Search- Introduction to AI -Search- Francesca Toni Outline • Solving problems by search • Basic search algorithms (uninformed/blind search) • Heuristic-based search algorithms (informed search) • Search for games (adversarial search ) Recommended reading: (most of) Chapters 3, 5 Additional reading: Chapter 3 Section 11.2.2 2 Solving problems by search •Problem solution
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Unit4-Planning We have concluded the previous lecture with a brief introduction of what a planning problem is, and we have given a simple example of such a task. In this lecture, we will concentrate more on planning. There are two main classes of planning, called respectively “planning with certainty” and “planning under uncertainty”. The first
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1intro 1 COMP9318: Data Warehousing and Data Mining — L1: Introduction — 2 Chapter 1. Introduction n Motivation: Why data mining? n What is data mining? n Data Mining: On what kind of data? n Data mining functionality n Are all the patterns interesting? n Classification of data mining systems n Major issues in data
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