Academic
  • Introduction
  • Artificial Intelligence
    • Introduction
    • AI Concepts, Terminology, and Application Areas
    • AI: Issues, Concerns and Ethical Considerations
  • Biology
    • Scientific Method
    • Chemistry of Life
    • Water, Acids, Bases
    • Properties of carbon
    • Macromolecules
    • Energy and Enzymes
    • Structure of a cell
    • Membranes and transport
    • Cellular respiration
    • Cell Signaling
    • Cell Division
    • Classical and molecular genetics
    • DNA as the genetic material
    • Central dogma
    • Gene regulation
  • Bioinformatics
    • Bioinformatics Overview
  • Deep Learning
    • Neural Networks and Deep Learning
      • Introduction
      • Logistic Regression as a Neural Network
      • Python and Vectorization
      • Shallow Neural Network
      • Deep Neural Network
    • Improving Deep Neural Networks
      • Setting up your Machine Learning Application
      • Regularizing your Neural Network
      • Setting up your Optimization Problem
      • Optimization algorithms
      • Hyperparameter, Batch Normalization, Softmax
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
      • Introduction
    • Sequence Models
      • Recurrent Neural Networks
      • Natural Language Processing & Word Embeddings
      • Sequence models & Attention mechanism
  • Linear Algebra
    • Vectors and Spaces
      • Vectors
      • Linear combinations and spans
      • Linear dependence and independence
      • Subspaces and the basis for a subspace
      • Vector dot and cross products
      • Matrices for solving systems by elimination
      • Null space and column space
    • Matrix transformations
      • Functions and linear transformations
      • Linear transformation examples
      • Transformations and matrix multiplication
      • Inverse functions and transformations
      • Finding inverses and determinants
      • More Determinant Depth
  • Machine Learning
    • Introduction
    • Linear Regression
      • Model and Cost Function
      • Parameter Learning
      • Multivariate Linear Regression
      • Computing Parameters Analytically
      • Octave
    • Logistic Regression
      • Classification and Representation
      • Logistic Regression Model
    • Regularization
      • Solving the Problem of Overfitting
    • Neural Networks
      • Introduction of Neural Networks
      • Neural Networks - Learning
    • Improve Learning Algorithm
      • Advice for Applying Machine Learning
      • Machine Learning System Design
    • Support Vector Machine
      • Large Margin Classification
      • Kernels
      • SVM in Practice
  • NCKU - Artificial Intelligence
    • Introduction
    • Intelligent Agents
    • Solving Problems by Searching
    • Beyond Classical Search
    • Learning from Examples
  • NCKU - Computer Architecture
    • First Week
  • NCKU - Data Mining
    • Introduction
    • Association Analysis
    • FP-growth
    • Other Association Rules
    • Sequence Pattern
    • Classification
    • Evaluation
    • Clustering
    • Link Analysis
  • NCKU - Machine Learning
    • Probability
    • Inference
    • Bayesian Inference
    • Introduction
  • NCKU - Robotic Navigation and Exploration
    • Kinetic Model & Vehicle Control
    • Motion Planning
    • SLAM Back-end (I)
    • SLAM Back-end (II)
    • Computer Vision / Multi-view Geometry
    • Lie group & Lie algebra
    • SLAM Front-end
  • Python
    • Numpy
    • Pandas
    • Scikit-learn
      • Introduction
      • Statistic Learning
  • Statstics
    • Quantitative Data
    • Modeling Data Distribution
    • Bivariate Numerical Data
    • Probability
    • Random Variables
    • Sampling Distribution
    • Confidence Intervals
    • Significance tests
Powered by GitBook
On this page
  • Agents and Environments
  • Rational agent
  • Omniscience, Learning, and Autonomy
  • Task Environments
  • Properties of Task Environments
  • The Structure of Agents
  • Agent Programs
  • Simple Reflex Agents
  • Model-based Reflex Agents
  • Goal-based Reflex Agents
  • Utility-based Reflex Agents
  • Learning Agents

Was this helpful?

  1. NCKU - Artificial Intelligence

Intelligent Agents

PreviousIntroductionNextSolving Problems by Searching

Last updated 5 years ago

Was this helpful?

Agents and Environments

Agent 可以是任何一種東西，用 censor 來感測環境，並且透過 actuators 來動作

  • An agent’s behavior is described by the agent function that maps any given percept sequence to an action.

    • e.g., vacuum-cleaner

      • if the current square is dirty, then suck

      • otherwise, move to the other square

Rational agent

  • Rational agent 指的是可以做出正確事情的 agent

  • 正確事情 : Agent 所做的事情會讓 enviroment 歷經一系列的 desirable states

  • desirability : performance measure 來量測

  • Definition : Rational agent 會在 percept 的每個步驟都需要 maximize performance measure，從而 action

  • Example : vacuum-cleaner agent

    • Performance measurement : the amount of dirt being cleaned up

    • Performance measure: a clean floor

Omniscience, Learning, and Autonomy

Rationality maximizes expected performance, while perfection maximizes actual performance.

  • 針對 Agent 的 rationality 不需要達到 omniscience

  • 但 Agent 需要能夠從 perceive 的 data 中學習 (learn)

  • 另外 Agent 最好能夠 Autonomous

    • learn and compensate for incorrect

Task Environments

我們透過以下幾點，來定義一個 Task environment :

  • Performance measure

  • The environment

  • Agent's actuators

  • Agent's sensors

  • 合稱為 PEAS (Performance, Environment, Actuators, Sensors)

例如 :

Agent

Performance

Environment

Actuators

Sensors

Taxi driver

Safety, fast

Traffics, Customers

Brake, Accelerator

GPS, Engines

Properties of Task Environments

Fully observable vs. partially observable

  • Agent 的每個 action 是否會跟 sensors 偵測所有的東西有關

Single agent vs. multiagent

  • 如果有多個 Agents，那他們之間是否會互相影響對方

  • Chess 就是 multiagent

Deterministic vs. stochastic

  • 若 environment 的下一個 state 會受前一個 state 影響，那就是 deterministic

  • stochastic 則相反，通常代表 outcomes 是 uncertainty 的

  • Taxi driving 就是 stochastic

Episodic vs. sequential

  • 情節之間是否是連貫的

  • Chess & Taxi driving 都是 sequential

Static vs. dynamic (taxi)

  • Environment 會因為 Agent 的思考而改變，則說 environment 是 dynamic 的

  • Taxi driving 是 dynamic 的

Discrete vs. continuous

  • Chess 的 environment 有 finite 的 discrete states & actions

  • Taxi driving 則是有 continuous state

Known vs. unknown

  • The agent's state of knowledge about the “laws of physics” of the environment.

The Structure of Agents

  • Agent = Architecture + Program

  • Program : 設計來 implement agent function (用於 map percepts to action)

  • Architecture : 指的是 Devices (sensor, actuator)

Agent Programs

  • A trivial agent program turns percept into action each time :

Simple Reflex Agents

  • 下一個 action 只建立在 current percept, 而會無視以前的 percept history

  • 只建立在 environment 是 full-observable

Model-based Reflex Agents

  • Agent 會持續追蹤外部世界的變化

  • 並更新內部的 internal state

Rule 跟 Simple Reflex agents 的是一樣的

Goal-based Reflex Agents

  • 因為只有 current state of environment 沒辦法每次都能產出好的 action

  • 所以基於 model 再新增 goal information

  • Behavior 可以因 Goal 改變而被簡單改變

  • Decision making 多了考慮未來的部分 "What/will"

Utility-based Reflex Agents

  • 有 Goal 不見得能有 High quality behavior

  • Utility-based agent 會挑選能夠 maximize expected utility 的 action

Learning Agents

  • 使用 critic 的 feedback 來決定 performance element 要如何修改來加強 action

    • learning element => making improvements

    • performance element => selecting actions

  • learn and improve in short run => better actions in long run

    • problem generator suggest new actions