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- Level Foundation
- المدة
- الطبع بواسطة National Taiwan University
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Offered by
عن
本課程分為人工智慧（上）、人工智慧（下）兩部份，第一部分除了人工智慧概論外，著重在目標搜尋、meta heuristic、電腦對弈、演繹學習（包含證言邏輯、一階邏輯及 planning ）等技術。這些技術主要發展時機為人工智慧的第一波及第二波熱潮，也就是 1950 年代至 1990 年代附近的主流發展，即使到現在也在各個領域廣為應用。 課程教學目標： 使同學對人工智慧有基礎概念 同學能夠理解如何運用目標搜尋技術及演繹學習方式達成人工智慧 同學能將相關技術應用到自己的問題上الوحدات
Course Videos
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9
Videos
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1
Readings
9
Videos
- 1-1 History of AI：TuringTest and Its Application, Chinese Room Argument
- 1-2 What is AI
- 1-3 Agents and Environments, PEAS, Environment Type
- 1-4 Different Level Of AI
- 1-5 Wave of AI：Debut, Knowledge Driven, Data Driven
- 1-6 The Classification of Agent, First Wave of AI (Artificial Neural Network)
- 1-7 Second Wave of AI (Expert System)
- 1-8 Third Wave of AI (Some Theory and Principle of Machine Learning)
- 1-9 Conclusion of AI and Machine Learning
1
Readings
- NTU MOOC 課程問題詢問與回報機制
Course Videos
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6
Videos
6
Videos
- 2-1 Problem Solving Agents, Problem Formulation (i)
- 2-2 Problem Formulation (ii) - Abstraction
- 2-3 Search on Tree and Graph
- 2-4 Uninformed Search (i) - Breadth-First Search, Uniform-Cost Search
- 2-5 Uninformed Search (ii) - Depth-First Search, Depth-Limited Search, Iterative-Deepening Search
- 2-6 Uninformed Search (iii) - Iterative-Deepening Search, Bidirectional Search
Quiz
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1
Assignment
1
Assignment
- Week 2
Course Videos
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6
Videos
6
Videos
- 3-1 Best-First Search (i) - Greedy Search
- 3-2 Best-First Search (ii) - A* Search
- 3-3 Best-First Search (iii) - Optimality of A*
- 3-4 Memory Bounded Search (i) - Iterative Deepening A*, RBFS
- 3-5 Memory Bounded Search (ii) - RBFS, Simplified Memory-bounded A*
- 3-6 Heuristic - Preformance, Generating Heuristics
Quiz
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1
Assignment
1
Assignment
- Week 3
Course Videos
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7
Videos
7
Videos
- 4-1 Black-Box Optimization
- 4-2 Steepest Descent
- 4-3 Simulated Annealing
- 4-4 Evolutionary Computation
- 4-5 Non-deterministic Actions - AND-OR Search, Partial Observations (i) - Sensor-less
- 4-6 Partial Observations (ii) - With Sensors
- 4-7 Partial Observations (iii) - Unknown Environments
Quiz
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1
Assignment
1
Assignment
- Week 4
Course Videos
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6
Videos
6
Videos
- 5-1 Type of Games - Symbols, Game Tree
- 5-2 Optimal Decision, Negamax Search , Alpha-Beta Pruning (i)
- 5-3 Alpha-Beta Pruning (ii)
- 5-4 Asperasion Windows, NegaScout
- 5-5 Imperfect Decisions, Forward Pruning
- 5-6 Stochastic Games, Partially Observable Games
Quiz
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1
Assignment
1
Assignment
- Week 5
Course Videos
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7
Videos
7
Videos
- 6-1 Logical Agents (i) - Generic Knowledge-Based Agent, PEAS
- 6-2 Logical Agents (ii) - Logic, Entailment and Models
- 6-3 Propositional Logic, Inference (i) - Enumeration, Validity and Satisfiability
- 6-4 Inference (ii) - Simple Knowledge, Resolution and CNF (i) - Proof by Resolution, CNF Conversion, Resolution Algorithm
- 6-5 Resolution and CNF (ii) - Properties of Resolution, Ground Resolution Theorem
- 6-6 Resolution and CNF (iii) - Horn and Definite Clauses, Forward Chaining
- 6-7 Backward Chaining, Pros and Cons of Propositional Logic
Quiz
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1
Assignment
1
Assignment
- Week 6
Course Videos
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8
Videos
8
Videos
- 7-1 First-Order Logic (i) - Syntax of FOL and Semantics
- 7-2 First-Order Logic (ii) - Using FOL, Inference (i) - Instantiation
- 7-3 Inference (ii) - Propositionalization, Inference (iii) - Unification
- 7-4 Inference (iii) - Unification, Inference (iv) - Forward chaining
- 7-5 Inference (iv) - Forward chaining, Inference (v) - Backward chaining
- 7-6 Logic Programing (i) - Prolog Systems
- 7-7 Logic Programing (ii) - Redundant Inference and Infinite Loops in Prolog
- 7-8 Inference (vi) - Resolution
Quiz
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1
Assignment
1
Assignment
- Week 7
Course Videos
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6
Videos
6
Videos
- 8-1 Planning Domain Definition Language (PDDL) (i)
- 8-2 Planning Domain Definition Language (PDDL) (ii)
- 8-3 State-Space Search, Heuristics
- 8-4 Planning Graphs
- 8-5 GRAPHPLAN
- 8-6 Course Review
Quiz
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1
Assignment
1
Assignment
- Week8
【補充教材】
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1
Readings
1
Readings
- 臺大開放式課程（NTU OpenCourseWare）：計算機概論
Auto Summary
Embark on a comprehensive journey into the world of Artificial Intelligence with "Artificial Intelligence - Search & Logic," a foundational course offered by Coursera. This course is tailored for those eager to gain a solid understanding of AI, focusing specifically on search methods and logic-based reasoning. In this course, learners will explore the historical and technical evolution of AI, delving into key techniques such as goal search, meta heuristics, computer gaming, and deductive learning. It covers significant AI methodologies developed during the first and second waves of AI (from the 1950s to the 1990s), which remain widely applicable across various fields today. Led by expert instructors, this course aims to: - Equip students with fundamental AI concepts. - Enable understanding and application of goal search techniques and deductive learning methods. - Empower learners to apply these techniques to solve real-world problems. Suitable for beginners and those at the foundational level, the course offers flexible subscription options, including a Starter plan, enabling learners to tailor their educational journey according to their needs. Dive into the intriguing domain of Maths & Statistics with this engaging AI course, and unlock the door to mastering essential AI techniques that have shaped the field for decades.
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