07GamePlaying Game-playing CITS3001 Algorithms, Agents and Artificial Intelligence 2021, Semester 2Tim French Department of Computer Science and Software Engineering The University of Western Australia Introduction • We will motivate the investigation of games in AI • We will apply our ideas on search to game trees – Minimax – Alpha-beta pruning • We will introduce […]
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PowerPoint Presentation 1 Who’s Winning the Artificial Intelligence Race between PRC & US: Alibaba, Tencent, Ping An, Baidu & Zhong An VERSUS Alphabet, Amazon, Apple, Facebook & Microsoft S c h u lt e R e s e a rc h Artificial Intelligence Described on a Single Chart Source: Schulte Research Estimates Physical Data IoT
AI Primer (IMDA Publications) AI PRIMER (IMDA PUBLICATIONS) TYPES OF DATA ARTIFICIAL INTELLIGENCE DESCRIBED ON A SINGLE CHART Source: Schulte Research Estimates Physical Data IoT Digital Data Infra. Neural Networks: Machine Learning AI 1. Financial Services 2. Cognitive Services 3. Lifestyle/ Health 4. Autonomous cars 5. Robotics 6. Advertising Cloud Q u a n tu
International Journal of Forecasting 16 (2000) 149–172 A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers Department of Business Studies, University of Edinburgh, Building, 50 , Edinburgh EH8 9JY, UK Credit scoring and behavioural scoring are the techniques that help organisations decide whether or not to grant credit to consumers
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/* Cache Simulator (Starter Code) by Justin Goins Oregon State University Spring Term 2021 */ #include “CacheController.h” #include #include #include #include using namespace std; CacheController::CacheController(CacheInfo ci, string tracefile) { // store the configuration info this->ci = ci; this->inputFile = tracefile; this->outputFile = this->inputFile + “.out”; // compute the other cache parameters this->ci.numByteOffsetBits = log2(ci.blockSize); this->ci.numSetIndexBits
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Statistical Machine Learning Statistical Machine Learning c©2020 Ong & Walder & Webers Data61 | CSIRO The Australian National University Outlines Overview Introduction Linear Algebra Probability Linear Regression 1 Linear Regression 2 Linear Classification 1 Linear Classification 2 Kernel Methods Sparse Kernel Methods Mixture Models and EM 1 Mixture Models and EM 2 Neural Networks 1
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Logic (PHIL 2080, COMP 2620, COMP 6262) Chapter: Propositional Natural Deduction — Negation, Disjunction Logic (PHIL 2080, COMP 2620, COMP 6262) Chapter: Propositional Natural Deduction — Negation, Disjunction Pascal Bercher Planning & Optimization Yoshihiro Maruyama Logic Intelligent Agents College of Engineering and Computer Science the Australian National University (ANU) 9 & 11 March 2021 Introduction
semantics:completeness INTRODUCTION TO THE SECOND PART Yoshihiro Maruyama co-taught with Pascal Bercher on the legacy of John Slaney 2 ➤ We have learned the basics of logic (natural deduction calculus, truth table semantics, etc.). ➤ What is logic all about? What is it doing? And why? ➤ We applied formal rules so many times, but
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conclusions THE LOGICAL LANDSCAPE Yoshihiro Maruyama co-taught with Pascal Bercher on the legacy of John Slaney SUMMARY – WHAT DID YOU LEARN? ➤ 1. Natural deduction (propositional; first-order; equality; restricted quantifiers) ➤ 2. Semantic tableaux (propositional; first-order) ➤ 3. Semantics (propositional; first-order; and three-valued models) ➤ 4. Sequent calculus (multiple-conclusion; and single-conclusion) ➤ 5. Relevant
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identity:restricted-quantifers 1 INTRODUCTION TO IDENTITY AND ITS PHILOSOPHY Yoshihiro Maruyama co-taught with Pascal Bercher on the legacy of John Slaney THE MEANING OF IDENTITY ➤ ➤ They are different expressions, but mean / denote / refer to the same thing (although they have different cognitive values). ➤ Remark: meaning is denotation in denotational semantics. ➤
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