# 人工智慧導論

## 目錄
> :::spoiler 清單
> [TOC]
> :::
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## Chapter 1：
### 認識AI
- Systems that think like humans
- Systems that think rationally
- Systems that act like humans
- Systems that act rationally

> 屬於光譜，沒有標準答案

### Turing Test
#### Suggested major components of AI
- knowledge
- reasoning
- language
- understanding
- learning
#### Problem of Turing Test
- not reproducible
- not constructive
- not amenable to mathematical analysis
> 不可重現、沒有建設性、不適合數學分析
#### The Computer would need the following capabilities
- Natural language processing (NLP)
- Knowledge representation
- Automated reasoning
- Machine learning (ML)
#### To pass the total Turning Test, a robot will need
- Computer vision
- robotics

### Thinking humanly: Cognitive Science

### Thinking rationally: Laws of Thought
Problems：
- Not all intelligentbehavior is mediated by logical deliberation
- What is the purpose of thinking? What thoughts should I have out of all the thoughts (logical or otherwise) that I could have?

#### Right Thinking
- Socrates is a man
- All man are mortal
- Socrates is mortal

### Acting Rationally
Rational behavior: doing the right thing

### AI Prehistory
#### philosophy
- logic
- methods of reasoning
- mind as physical system
- foundations of learning
- language
- rationality

Thinking
- Can formal rules be used to draw valid conclustions?
- How does the mind arise from a physical brain?
- Where does knowledge come from?
- How does knowledge lead to action?
#### Mathematics
- formal representation and proof
- algorithms
- computation
- (un)decidability
- (in)tractability
- probability
#### Psychology
- adaptation
- phenomena of perception and motor control
- experimental techniques (psychophysics, etc.)

Thinking
- How do humans and animals think and act?
#### Economics
- formal theory of rational decisions

Thinking
- How should we make decisions in accordance with our preferences?
- How should we do this when others may not go along?
- How should we do this when the payoff may be far in the future?
#### Linguistics
- knowledge representation
- grammar

Thinking
- How does language relate to thought?
#### Neuroscience Control theory
- plastic physical substrate for mental activity

Thinking
- How do brains process information?
#### Control theory
- homeostaic systems
- stability
- simple optimal agent designs

### History
#### The inception of AI (1943 - 1956)
- McCulloch & Pitts
- Hebbian learning
#### A does of reality (1966 - 1973)
- Main reasons for this failure
- 缺乏對事物的認知
- 當時AI系統主要基於知情內省，而沒有可靠的解決方案(如演算法)
- Machine evolution(now call genetic programming)
#### Expert systems (1969 - 1986)
- weak methods
- certainty factors
- In 1981, the Japanese government announced the "Fifth Generation" project
- AI Winter
> many companies fell by the wayside as they failed to deliver on extravagant promises

#### The return of neural networks (1986 - present)
- Back-pro
#### Probabilistic reasoning and machine learning (1987 - present)
### Big data (2001 - present)
This has led to the development of learning algorithms specially designed to take advantage of very large data sets.
#### Deep learning (2011 - present)
- 2012 ImageNet Competition
- Deep learning relies heavily on powerful hardware

### Risks and Benefits of AI
> "First solve AI, then use AI to solve everything else."
> *Demis Hassabis, CEO of Google DeepMind*

- Benefits
- Decrease repetitive work
- Increase production of goods and services
- Accelerate scientific reasearch
> disease cures, climate change and resource shortages solutions
- Risks
- Lethal autonomous weapons
- Surveillance and persuasion
- Biased decision making
- Impact on employment
- Safety-critical applications
- Ccyversecurity threats

## Chapter 2：Intelligent Agents

### Outline
- Agents and environments
- Rationality
- PEAS (Performance measure, Environment, Actuators, Sensors)
- Environment types
- Agent types

### Agents and Environments

> Agents include humans, robots, softbot, thermostats, etc.

> An agent can be anything that can be viewed as perceiving its environment through **sensors** and acting upon that environment through actuators.

> The agent function maps from percept histories to actions:
> `f:P* -> A`
> The agent program runs on the physical architecture to produce f

### Agents interact with environments through sensors and actuators


### Rationlity
- The performance measure that defines the criterion of success
- The agent's prior knowledge of the environment
- The actions that the agent can perform
- The agent's percept sequence to date

### PEAS
> To design a rational agent, we must specify the task enviironment Consider, e.g., the task of designing an automated taxi:

> - Performance measure：safety, destination, profits, legality, comfort...
> - Environment：streets, traffic, pedestrians, weather...
> - Actuators：steering, accelerator, brake, horn, speaker/display...
> - Sensors：video, accelerometers, gauges, engine sensors, keyboard, GPS...

## Chapter 3：Solving Problems by Searching

### Outline
- Problem-solving agents
- Example Problems
- Problem formulation
- Search Algorithms
- Uninformed Search Strategies
- Informed (Heuristic) Search Strategies
- Heuristic Functions

### Problem-solving agents
- seq
> an action sequence, initally empty
- state
> some description of the current world state
- goal
> a goal, initally null
- problem
> a problem formulation

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###### tags: `AI`