6.034 Recitation 4: Constraint Satisfaction Problems
Topics covered:
- Terminology: constraint graph, variable, value, domain, constraint
- Four strategies for constraint satisfaction:
1. Depth-First Search (DFS)
2. DFS + Forward-Checking (FC)
3. DFS + FC + Propagation through Singleton Domains (Prop-1)
4. DFS + FC + Propagation through Any reduced domains (Prop-Any)
- Domain reduction before search
Example problems:
- Time Travelers problem from 2009 Quiz 2
- a section of the Lion King/Zoo problem from 2011 Quiz 2
Видео 6.034 Recitation 4: Constraint Satisfaction Problems канала Jessica Noss
- Terminology: constraint graph, variable, value, domain, constraint
- Four strategies for constraint satisfaction:
1. Depth-First Search (DFS)
2. DFS + Forward-Checking (FC)
3. DFS + FC + Propagation through Singleton Domains (Prop-1)
4. DFS + FC + Propagation through Any reduced domains (Prop-Any)
- Domain reduction before search
Example problems:
- Time Travelers problem from 2009 Quiz 2
- a section of the Lion King/Zoo problem from 2011 Quiz 2
Видео 6.034 Recitation 4: Constraint Satisfaction Problems канала Jessica Noss
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Constraint Satisfaction: introduction
6.034 Recitation 6: Neural Nets
6.034 Recitation 5: k-Nearest Neighbors & Identification Trees
Constraint Satisfaction Problem(CSP) in Artificial Intelligence | Problem #2
The Arc-Consistency Procedure
Mod-01 Lec-39 Constraint Satisfaction Problems
Constraint satisfaction problems
هوش مصنوعی (جلسه چهاردهم) - حل مسائل ارضای محدودیت
Constraint Satisfaction: the AC-3 algorithm
How Constraint Satisfaction Algorithm Works | Explained with Interesting Example | AI
CS 152 NN—23: Generating Sequences: Beam Search
Constraint Satisfaction, An Introduction
forward checking, CSP heuristics
Lecture 4: CSPs
6.034 Recitation 9: Bayesian Inference
Laurent Charlin: "Exact Combinatorial Optimization with Graph Convolutional Neural Networks"
6.034 Recitation 7: Support Vector Machines (SVMs)
ICAPS 2016: Constraint Programming for Planning and Scheduling (part 1)
16. Learning: Support Vector Machines