TAILIEUCHUNG - Artificial Intelligence - Lecturer 3: Search

Outline: Problem-solving agents, Problem types, Problem formulation, Example problems, Basic search algorithms. Replace letters by numbers from 0 to 9 such as no different letter is replaced by the same number and satisfying the following constraint. | Artificial Intelligence For HEDSPI Project Lecturer 3 - Search Lecturers Thanh Huong Dr. Tran Due Khanh Dr. Hai V. Pham Faculty of Information Technology - HUST Outline Problem-solving agents Problem types Problem formulation Example problems Basic search algorithms breadth-first search depth-first search depth-limited search iterative deepening depth-first search 1 Problem-solving agents function Simplb-Problem-Solving-Agbnt percept returns an action static seq an action sequence initially empty state some description of the current world state goal a goal initially null problem a problem formulation state V- UPDATE-STATE state percept if seq is empty then do goal r- Formulate - GOAL siaie problem Formulate -PROBLEM itdie goal seq Search problem action First aeọ seq 3 Rest seq return action 3 3 Example 1 Route Planning Performance Get from Arad to Bucharest as quickly as possible Environment The map with cities roads and guaranteed travel times Actions Travel a road between adjacent cities 2 Example 2 Finding letters Replace letters by numbers from 0 to 9 such as no different letter is replaced by the same number and satisfying the following constraint SEND MORE MONEY CROSS ROADS DANGER Example 3 Pouring water Given 2 containers A m litres B n litres . Finding a method to measure k litres k max m n by 2 containers A B and a container c Actions how C- A C- B A- B A- C B- A B- C Conditions no overflow pouring all water Eg m 5 n 6 k 2 what Mathematical model X y -ì x y AB AB

• Kỹ thuật lập trình
• Artificial Intelligence
• Problem solving agents
• Problem types
• Problem formulation
• Example problems
• Basic search algorithms
• Applying artificial intelligence on e learning
• Compare E learning
• Traditional learning
• Learn 1 1 with virtual assistant
• Artificial Neural Networks (ANN) algorithms
• CS 486686 Artificial Intelligence
• Course administration
• Intelligent Assistive Technology
• A brief history of A
• Properties of task environments
• Artificial intelligence in education
• Keynote speaker abstracts
• Human computer interface issues
• Pedagogical agents
• Models of criminal liability of artificial intelligence
• Criminal liability of artificial intelligence
• Science fiction to prospect for criminal law and policy in Vietnam
• Criminal law and policy in Vietnam
• Criminal law and policy
• Lecture Artificial Intelligence
• Trí tuệ nhân tạo
• Computing machinery
• Thinking humanly
• Cognitive Science
• Lecture note Artificial Intelligence
• Performance measure
• Environment types
• Agent program
• Problem solving
• Informed search algorithms
• Best first search
• Local search algorithms
• Simulated annealing
• Genetic algorithms
• Constraint Satisfaction Problems
• Backtracking search for CSPs
• Game playing
• Resource limits
• Games of chance
• Logical agents
• Knowledge based agents
• Wumpus world
• First order logic
• Semantics of FOL
• Fun with sentences
• Wumpus world in FOL
• Inference in first order logic
• Generalized Modus Ponens
• Logic programming
• Bayes’ Rule
• Methods for handling uncertainty
• Making decisions under uncertainty
• Bayesian networks
• Parameterized distributions
• Inference in Bayesian networks
• Variable elimination
• Temporal probability models
• Hidden Markov models
• Kalman filters
• Speech recognition
• Speech sounds
• Word pronunciation
• Rational decisions
• Rational preferences
• Multiattribute utilities
• Learning from Observations
• Learning agents
• Inductive learning