Although Artificial Intelligence (A.I.) has been defined in various ways, it can generally be said to be a field of study
that aims to elucidate human intelligence and to realize it on a computer.
The elucidation of intelligence is deeply related to various academic fields, and due to the multifaceted nature of intelligence, artificial intelligence encompasses a very wide range of content, which cannot all be covered in a single class.
In this class, students will learn about the basic technical fields and algorithms in artificial intelligence, and also consider topics more fundamental to artificial intelligence.
In many of the classes, there will be pre-study assignments, in-class quizzes, a mid-term exam and a final exam, and the grade will be a weighted average of these assignments.
The grades will be a weighted average of these assignments.
The elucidation of intelligence is deeply related to various academic fields, and due to the multifaceted nature of intelligence, artificial intelligence encompasses a very wide range of content, which cannot all be covered in a single class.
In this class, students will learn about the basic technical fields and algorithms in artificial intelligence, and also consider topics more fundamental to artificial intelligence.
In many of the classes, there will be pre-study assignments, in-class quizzes, a mid-term exam and a final exam, and the grade will be a weighted average of these assignments.
The grades will be a weighted average of these assignments.
In this class, students will learn about the fundamentals of artificial intelligence and also consider more fundamental questions,
such as what is human intelligence and how do we know if intelligence has been realized on a computer system?
The purpose of this class is for each student to form his or her own view of artificial intelligence.
The purpose of this class is for each student to form his or her own view of artificial intelligence.
- Students should be able to understand the technical fundamentals related to artificial intelligence covered in this lecture and be able to explain them to others.
- To be able to think independently about artificial intelligence, its background and its impact on us and on our society, and to be able to express their own opinions.
- The students must be able to explain to others how the problem can be solved by applying the appropriate algorithm to a fundamental problem in artificial intelligence and be able to obtain the solution.
| 事前・事後学習課題 | 中間試験 | 期末試験 | Total. | |
|---|---|---|---|---|
| 1. | 10% | 10% | 15% | 35% |
| 2. | 10% | 10% | 10% | 30% |
| 3. | 10% | 10% | 15% | 35% |
| Total. | 30% | 30% | 40% | - |
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction to the class Preliminary Study Assignment: Please summarize your thoughts on the feasibility of human-like general-purpose artificial intelligence at this point in time after researching materials if necessary, and answer the questions from Scomb. The length of the paper should be no less than 400 words. Aim of the class (1) To understand that it is not so easy to define the discipline of artificial intelligence. (2) Although the study of artificial intelligence started after the invention of the computer, the idea of artificially creating something human-like has existed for a long time. (3) It is quite difficult to judge whether artificial intelligence has been realized. (3) It is quite difficult to judge whether artificial intelligence has been realized or not. Class contents: (1) The various types of artificial intelligence. (1) Various definitions of artificial intelligence and the difficulty of a unified definition (2) Two approaches to artificial intelligence (3) Academic fields related to artificial intelligence (4) Two positions on the relationship between intelligence and mind: dualistic theory of mind and materialistic theory of mind (5) Tests to check the realization of artificial intelligence: the Turing test and the Chinese room as its criticism (6) Prehistory of Artificial Intelligence: An Ancient Dream |
Prior Learning Assignment | 120minutes |
| Think about your own definition of artificial intelligence | 60minutes | ||
| 2. | Three Waves of Artificial Intelligence Research Preliminary Study Assignment: Please summarize your own thoughts on whether human intelligence is a collection of its various aspects, such as the ability to handle language, logical reasoning, image recognition, voice recognition, etc., or whether you think human intelligence is something more than just a collection of these, and why you think so. Please submit a summary of your thoughts on this question and why you think so. The length should be no less than 400 words. Aims of the class (1) There have been three major waves of artificial intelligence research since the birth of computers. (2) Although artificial intelligence research achieved certain results in each of the three waves, criticism of the results led to a decline in research and a winter period. (3) New ideas were born out of the research that continued during the winter period, and these new ideas led to a new wave of research. (4) To understand the importance of the research and its results. Course contents: (1) The first wave of research in artificial intelligence (1) The first wave in artificial intelligence research: the age of symbolism (2) The second wave of artificial intelligence research: the age of knowledge processing (3) The third wave in artificial intelligence research: new trends |
Prior Learning Assignment | 60minutes |
| Familiarize yourself with the previous lessons. | 120minutes | ||
| 3. | Knowledge and its representation Preliminary study task: Assume that you are explaining the rules of a sport or game with which you are familiar to someone who is completely unfamiliar with that sport or game, and respond to the explanation from Scomb. However, it is not necessary to explain all of the sport or game, but only a part of the situation and only that part of the situation should be explained. Aim of the class:. (1) Knowledge representation has played a necessary and important role for artificial intelligence. (2) There are various methods of knowledge representation, each of which has its own advantages and disadvantages. (3) There are various problems in extracting the knowledge to be represented. (3) There are various problems in extracting the knowledge to be represented. Lesson contents: (1) What is knowledge? (1) What is knowledge? (2) How knowledge works in an expert system (3) Different types of knowledge representation: logical, procedural, networked, and structured (4) Explicit and tacit knowledge |
Prior Learning Assignment | 60minutes |
| Familiarize yourself with the previous lessons. | 120minutes | ||
| 4. | Reasoning and Exploration Aims of the class:. (1) That there are three types of reasoning: inductive reasoning, deductive reasoning, and abduction. (2) Reasoning that deals with uncertainty includes non-monotonic reasoning, probabilistic reasoning, and fuzzy reasoning. (3) To understand that in order to conduct a search, it is necessary to represent the problem in an appropriate way. (4) To understand the following. Course contents: (1) Three types of inference (1) Three types of reasoning: inductive reasoning, deductive reasoning and abduction (2) Forward and backward reasoning (3) Reasoning with uncertainty: non-monotonic reasoning, probabilistic reasoning, fuzzy reasoning (4) Examples of search: problem representation and solution |
Familiarize yourself with the previous lessons. | 180minutes |
| 5. | Vertical Search, Horizontal Search, Best Priority Search and Game Trees Preliminary study task: Suppose you are in a city in a new country and do not speak the language at all and do not have a map. How would you get to your destination, such as a hotel or a store, if you could not ask people around you, tourist information centers, cab and bus drivers, etc. how to get there? Think of a way to get there and answer the question. Aim of the lesson (1) To understand the characteristics of search methods such as vertical search, horizontal search, and best-first search. Lesson contents: (1) Various types of search: Vertical search, horizontal search, best-first search. (1) Various types of search: vertical search, horizontal search, and best-first search (2) Stepwise deepening (3) Branch-and-bound, heuristic search, and mountain climbing (4) Game tree search |
Prior Learning Assignment | 60minutes |
| Familiarize yourself with the previous lessons. | 120minutes | ||
| 6. | A* Algorithm and Constraint Satisfaction Aims of the class: (1) To understand the scheme and features of the A* algorithm. (1) To understand the scheme and features of A* algorithm (2) To understand the concept of constraints and their characteristics (3) To understand the method of constraint satisfaction and its efficiency Lesson Content: (1) A* Algorithm: (1) A* Algorithm (2) A* algorithm: its methods and features (3) Concept of constraints (4) Example of constraint satisfaction problem: N-Queen problem (5) Example of constraint satisfaction problem: SEND MORE MONEY problem (6) Efficiency of constraint satisfaction: Generative checking and Forward Checking Post-program assignment: Programmatically solve the SEND MORE MONEY problem |
Familiarize yourself with the previous lessons. | 120minutes |
| post-study assignment | 60minutes | ||
| 7. | Mid-term examination A mid-term examination will be administered and an explanation of the examination will be given. The scope of the examination will be the areas covered up to the 6th class. |
Preparation for mid-term examinations and bring-in papers | 240minutes |
| 8. | Machine Learning Aim of the class: (1) To understand machine learning (1) Understand the outline of machine learning (2) Understand that there are various types of machine learning such as rote learning, deduction learning, and induction learning. (2) Understand the various types of machine learning such as rote learning, deductive learning, and inductive learning. Lesson contents: (1) Overview of learning (1) Overview of learning (2) Various types of learning: rote learning, deduction learning, induction learning, concept learning (3) Decision trees and their functions (4) ID3 |
Familiarize yourself with the previous lessons. | 180minutes |
| 9. | Representation of problems, game trees for competitive games Aims of the class:. (1) Understand how competitive games can be decomposed into AND-OR trees and how to solve problems by obtaining solution trees. (2) To understand the recursive representation of problems (3) To understand the AND-OR tree representation of the game tree of a competitive game and the minimax method. Lesson contents: (1) Representation of game progression (1) Representation of game progression: decomposition into sub-problems by AND-OR trees (2) Problem solving: Creating solution trees from AND-OR trees (3) Recursive representation of the problem and decomposition into subproblems: the Tower of Hanoi problem as an example (4) Selection of moves in competitive games: Minimax method Post-program learning assignment: Create a program to solve the Tower of Hanoi |
post-study assignment | 240minutes |
| 10. | Combinatorial Optimization Problems and Genetic Algorithms Aim of the class: (1) To understand combinatorial optimization problems and their characteristics. (1) To understand the outline and characteristics of combinatorial optimization problems (2) To understand the outline, solution method, and features of genetic algorithms Lesson contents: (1) Examples of combinatorial optimization problems (1) Examples of combinatorial optimization problems: traveling salesman problem and knapsack problem (2) Complexity and difficulty of combinatorial optimization problems (3) Genetic algorithms: representation of the problem (4) Genetic algorithms: overview of the process (5) Genetic Algorithm: Example of application to the knapsack problem |
Familiarize yourself with the previous lessons. | 180minutes |
| 11. | Planning and Exercises Aim of the class: (1) To understand the basics of the planning concept (1) To understand the basics of the planning concept (2) To understand the planning process through exercises Lesson contents: (1) Planning Concepts (1) Concept of planning: block world and route planning as examples (2) Basic processing method of planning (3) Planning exercises: solving Watershed problems (4) Post-lesson study: Solving Watershed Problems |
post-study assignment | 60minutes |
| Familiarize yourself with the previous lessons. | 120minutes | ||
| 12. | Agents and Distributed Artificial Intelligence Aims of the class:. (1) To understand the basic concept of agents (2) To understand the interaction and cooperative behavior of agents (3) To understand the basic concepts of multi-agent systems Lesson Contents (1) Structure of agents (2) Multiagent systems (3) Multi-agent systems and learning: distributed learning and cooperative learning (4) Agents and the mind |
Familiarize yourself with the previous lessons | 180minutes |
| 13. | Social and Philosophical Aspects of Artificial Intelligence Aims of the class:. (1) That there are both positive and negative views on the feasibility of strong artificial intelligence (2) That, in the end, artificial intelligence research requires an awareness and understanding of human intelligence (3) To understand that technical feasibility should be considered separately from feasibility of realization. (3) To understand that technical feasibility and feasibility should be considered separately. Course contents: (1) Aim of Artificial Intelligence research (1) What artificial intelligence research should aim for: Can Machine Think? (2) Can artificial intelligence simulate human intelligence? Birds and airplanes (3) How should artificial intelligence handle ethical issues? Posterior Learning Problem: Is strong artificial intelligence feasible? Can and should we aim to achieve it? Why? |
post-study assignment | 240minutes |
| 14. | Final exam and its explanation The scope of the examination is from the first class to the thirteenth class. |
Prepare for final examinations and bring-in papers | 300minutes |
| Total. | - | - | 2820minutes |
Submission status and content of pre- and post-study assignments.
Evaluation will be based on a weighted average of the mid-term and final examinations.
60% will be given if the student has a general understanding of what is being tested.
Evaluation will be based on a weighted average of the mid-term and final examinations.
60% will be given if the student has a general understanding of what is being tested.
Tomohiro Odaka, "Introduction to Artificial intelligence, Second edition", Kyoritsu-shuppan, 2023
Courses that students are expected to acquire prior to enrollment: "Discrete Mathematics" and "Data Structures and Algorithms".
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | not applicable |
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Last modified : Sun Mar 02 04:06:33 JST 2025