AI Constraint Satisfaction

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AI Constraint Satisfaction

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About the course

In this course, you will look at general approaches to solving finite domain CSPs and explore how search can be combined with constraint propagation to find solutions. This course is a companion to the course “Artificial Intelligence” that was offered previously.

Learning Outcomes

After completing this course, you will be able to:

  • Understand the details of Constraint Satisfaction Problems (CSPs).
  • Understand and solve the constraint satisfaction problems without any difficulty.
  • Improve and enhance their problem-solving skills in Artificial Intelligence.
  • Boost your hireability through innovative and independent learning.
Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science and engineering or data science.

Teachers/Faculties: All computer science and engineering teachers/faculties.

Professionals: All working professionals from computer science / IT / Data Science domain.

Why Learn Artificial Intelligence: Constraint Satisfaction?

Artificial Intelligence (AI) has the potential to revolutionize the human civilization and will impact industries, companies and how we live our life. As per the latest study, the global market for Artificial Intelligence is estimated to post an impressive 36.1% CAGR between 2016 and 2024, rising to a valuation of US$3,061.35 billion by the end of 2024 from US$126.14 billion in 2015. The expert system segment was at the forefront of growth in 2015, representing 44% of the overall market revenue and is poised to maintain its dominance until 2024. Thus, the career prospect is bright for the candidates having the knowledge of Artificial Intelligence, as there an only a few professionals in this field and the demand in the industry is huge for this technology. So, it's worth taking this course.

Course Features
  • 24X7 Access: You can view lectures as per your own convenience.
  • Online lectures: 21 hours of online lectures with high-quality videos.
  • Updated Quality content: Content is latest and gets updated regularly to meet the current industry demands.
Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Topics to be covered
  1. AICS-Module-1: Constraint Satisfaction Problems
    • How to formulate the Constraint Satisfaction Problem (CSP) and what are its advantages?
    • How to Combine Reasoning with Search while solving CSP?
    • What are CSP and its class?
  2. AICS-Module-2: CSP Examples: Map coloring, N-Queens, Classroom scheduling
    • What is Numeric Constraint and Classroom Scheduling as a CSP?
    • What is Logical Deduction as a CSP?
    • What is Map coloring and N-Queens as a CSP?
  3. AICS-Module-3: CSP Examples: Huffman-Clowes Labelling, Waltz Algorithm, Crosswords
    • What is Scene Labeling as a CSP?
    • What is a WALT2 algorithm for HUFFMAN CLOWS LABELING?
    • What are Crosswords as a CSP?
  4. AICS-Module-4: Model-Based Diagnosis - An application of CSP
    • What is Model-Based Diagnosis?
    • How to model Component Behavior?
    • How Is Component structure of Model-Based Diagnosis illustrated using an example?
  5. AICS-Module-5: Constraint Networks - An Introduction
    • What are Constraint Networks?
    • What are Assignments/Instantiations and their examples?
    • What are Consistent Assignments and their examples?
  6. AICS-Module-6: Binary Constraint Networks (BCN), Equivalent Networks
    • What is the Notion of Equivalent Networks?
    • What is the Notion of Minimal/ Tightest Networks?
    • What is Binary Constraint Network (BCN)?
  7. AICS-Module-7: Projection Networks
    • What is the BCN process of Composition of relation, it's examples and Notion of a Matching Diagram?
    • What is Montanari and it's example?
    • What are Projection network and it's example?
  8. AICS-Module-8:Constraint Propagation
    • What is Constraint Propagation and Consistency Enforcement?
    • What are ARC Consistency and it's example?
    • What is the Revise algorithm and it's example?
    • How to make a Network ARC Consistent (AC-1 Algorithm)?
  9. AICS-Module-9: Algorithms AC1 and AC3
    • What is the AC-1 Algorithm, it's example and complexity?
    • What is AC-3 Algorithm?
    • What is the Complexity of AC-3 Algorithm?
  10. AICS-Module-10: Can we do better than AC3?
    • What is i-Consistency?
    • Can we do better than AC3?
  11. AICS-Module-11: Algorithm AC4
    • What is the AC-4 Algorithm?
    • What does ARC Consistency imply and how it helps to make a network arc consistent?
  12. AICS-Module-12: Generalized AC, Path-Consistency
    • What is the generalized ARC Consistency?
    • What is Path Consistency and it's example?
    • What is Revise 3 Algorithm and it's example (Part-1)?
    • What is Revise 3 Algorithm and it's example (Part-2)?
  13. AICS-Module-13:i-Consistency, Algorithm PC1
    • What is generalized i-Consistency?
    • What is the impact of i-Consistency?
    • What is Revise 3 Algorithm and how we can generalize it to high order Revise?
  14. AICS-Module-14: Algorithm PC2, Strong i-Consistency
    • What is the PC-2 Algorithm for Path Consistency?
    • What is Constraint Propagation?
    • What is the meaning of Strong i-Consistency?
  15. AICS-Module-15: Directional Consistency and Graph Ordering
    • What is Directional Consistency?
    • How to illustrate the Directional Consistency by Map coloring example?
  16. AICS-Module-16: Min-Width and Min-Induced-Width Ordering
    • What is Search technique in solving CSP?
    • What is the concept of Graph and it's example?
    • What is Induced Graph?
  17. AICS-Module-17: Directional Arc-Consistency and Tree CSPs
    • What is Directional Local Consistency?
    • What is the Notion of Directional ARC Consistency and it's example?
    • How to handle Trees CSP?
  18. AICS-Module-18: Directional Path-Consistency and Directional i-Consistency
    • What is Directional Path-Consistency (DPC) and it's example?
    • What is DPC Algorithm and Directional i-Consistency?
    • How much is DPC sufficient for Backtrack Tree Search (Part-1)?
  19. AICS-Module-19: Backtrack-Free search and Adaptive Consistency
    • How much is DPC sufficient for Backtrack Tree Search (Part-2)?
    • What is Adaptive Consistency and it's example?
    • What is Bucket Elimination Algorithm?
  20. AICS-Module-20: Adaptive Consistency: Bucket Elimination
    • How is Bucket Elimination Algorithm illustrated through an example (Part-1)?
    • How is Bucket Elimination Algorithm illustrated through an example (Part-2)?
    • How is Bucket Elimination Algorithm illustrated through an example (Part-3)?
  21. AICS-Module-21: Search Methods for Solving CSPs
    • What is the Search Space Method for solving CSP and it's example?
    • How to generate different trees by different orders and different degrees of Consistency on Constraint graph (Part-1)?
    • How to generate different trees by different orders and different degrees of Consistency on Constraint graph (Part-2)?
  22. AICS-Module-22: Algorithm Backtracking
    • What is Backtracking Algorithm?
    • What are the improvements that we can make to Backtracking
  23. AICS-Module-23: Look-Ahead Methods in Search
    • What is Generalized-Look ahead-Search Algorithm?
    • What is Select Value Forward Checking?
    • What Select Value AC?
    • What is Full Look ahead and Partially Look ahead?
  24. AICS-Module-24: Look-Ahead Search: Examples
    • How is the concept of Look ahead ill-starred through examples?
    • What is Forward Checking and Full AC Algorithm and it's examples?
    • How Is Full Look-ahead explained with the help of an example?
  25. AICS-Module-25: Combining Search with Reasoning: Algorithm DPLL
    • How to combine Propagation and Search?
    • What is the DPLL Algorithm?
    • How is the DPLL Algorithm explained with the help of an example?
  26. AICS-Module-26: Algorithm Backmarking
    • What is Back marking and it's example?
    • What is Back marking Algorithm?
  27. AICS-Module-27: Dynamic Value Ordering, Dynamic Variable Ordering
    • What is Dynamic Value Ordering?
    • What is Dynamic Variable Ordering and what is Dynamic Variable Ordering with forwarding Checking (DVFC) Algorithm?
    • What is the Cycle Cut Set of a Graph?
  28. AICS-Module-28: Look-Back Methods - Definitions
    • What is the problem with Chronological Backtracking?
    • What are the different definitions in Look-Back Methods (Part-1)?
    • What are the different definitions in Look-Back Methods (Part-2)?
    • What is the concept of Backjumping?
  29. AICS-Module-29: Gaschnig Backjumping: The Culprit Variable
    • What is the Culprit Variable and it's example?
    • How to identify the Culprit variable during the search in Silent-Value?
    • What is Gashing Backjumping (GBJ) and it's example?
  30. AICS-Module-30: Gaschnig Backjumping, Graph-Based Backjumping
    • What is Gashing Backjumping (GBJ) Algorithm?
    • How is Graph-Based Backjumping s explained with the help of an example (Part-1)?
    • How Is Graph-Based Backjumping explained with the help of an example (Part-2)?
  31. AICS-Module-31: Graph-Based Backjumping: Internal and Relevant Dead-Ends
    • What is the diagram for different definitions in Graph-Based Backjumping?
    • What are the different definitions in Graph-Based Backjumping?
    • What is Graph-Based Backjumping Algorithm?
  32. AICS-Module-32: Conflict-Directed Backjumping: Definitions
    • How Backjumping with Depth First Search explained?
    • What is the drawback of Graph-based Backjumping (GBJ)?
    • What are the different definitions in Conflict-Directed Backjumping (CBJ)?
  33. AICS-Module-33: Algorithm Conflict-Directed Backjumping
    • How Is Conflict-Directed Back jumping (CBJ) explained through an example (Part-1)?
    • How Is Conflict-Directed Back jumping (CBJ) explained through an example (Part-2)?
    • What is the CBJ Algorithm?
  34. AICS-Module-34: Combining Look-Ahead and Look-Back: FC-CBJ
    • How to Combine Look ahead with Look back methods?
    • What is FC-CBJ Algorithm?
    • How to arrange all the Algorithms in order and how effective they are?
  35. AICS-Module-35: Learning During Search
    • What is Learning Algorithm?
    • What can we learn at a dead end?
    • What is Graph-Based Learning and it's example?
    • What is Deep and Jump back Learning?
  36. AICS-Module-36: Model-Based Systems
    • What is the basic idea behind the generation of Diagnosis Systems?
    • How to generate the diagnosis system?
    • What is a Compositional and Component-oriented Model and how to represent a Component?
    • What are Behavior Models and their example?
    • What are Qualitative Modeling and its deviations?
    • What is Model-based diagnosis and what are its tasks?
  37. AICS-Module-37: Model-Based Diagnosis
    • What are the meaning of Diagnosis and Fault Localization and their examples?
    • How to find minimal fault Localizations?
    • How to compute the fault localization and it's example?
    • How is the application of diagnosis explained with the help of an example?
    • How to reduce Fault Localization using Fault Model and it's example?
    • What are the features of Model-based Diagnosis?
  38. AICS-Module-38: Truth Maintenance Systems
    • What are Truth Maintenance Systems (TMS) and its assumptions?
    • How is TMS explained with the help of an example?
    • What are the functionalities of TMS and what is Assumption Based TMS (ATMS) and it's example?
    • What is Model-Based Diagnosis and it's example (Part 1)?
    • What is Model-Based Diagnosis and it's example (Part 2)?
  39. AICS-Module-39: Planning as Constraint Satisfaction
    • What is Planning and STRIPS Planning Domain?
    • What is the Graph Plan Algorithm?
    • How to convert Planning as a Constraint Satisfaction Problem?
  40. AICS-Module-40: Planning as Constraint Satisfaction (cont.)
    • What is the different way to convert Planning as a Constraint Satisfaction Problem?
    • How to solve CSP with an example?
  41. AICS-Module-41: Planning as Satisfiability
    • How to implement Planning as Satisfiability and how to assemble the SAT Formula?
    • What is Planning Graph for SAT and how to convert Planning Graph into SAT problem?
  42. AICS-Module-42: Wrapping Up and Further Study
    • What is the SAT problem?
    • What is the average case performance of SAT?
    • What are temporal constraints and their examples?
    • What is Constraint Optimization?
  43. AI: Constraint Satisfaction-Final Quiz

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