Systems Modelling and Data Analysis + Engineering of Intelligent Systems

1. Introduction to Knowledge Representation and Reasoning. Basic Practical Introduction to Constraint Programming and MiniZinc: 3 simple examples. [25.02.2020; ALi]
2. Introduction to Constraint Programming. Building simple CP models in MiniZinc. Abduction/Constructive Abduction. CP/CSP/SAT vs. Constrained Optimization. MiniZinc by examples: map coloring, sendMoreMoney, SendMostMoney,production planning; using external data sets. [3.03.2020; ALi]
3. Backtracking Search and Linear Programming Models; cryptoarithmetic examples vs. linear programming optimization. MiniZinc and Python examples. Modeling Sudoku, production optimization, knapsack, jobshop and other applications in MiniZinc.[10.03.2020; ALi]
4. Uwaga: Zajęcia odwołane 11.03.2020 godz. 10:00 do 24-czy-25(?).03.2020! Attention: Lectures/classes cancelled until March 24-or-25-th(?), 2020! See: AGH - Decision/Decycja
5. Constraint Programming Tools: MiniZinc. Examples of application. [17.03.2020; Online class (use slides 2 below; start from page 47 and continue untill the end; Also lin 3 below); ALi]
6. Logical background for Knowledge Representation and Reasoning. Syntax and Semantics of Propositional Calculus. [24.03.2020;ALi]
7. Logical background for Knowledge Representation and Reasoning. Inference Tasks and Theorem Proving. [31.03.2020; ALi]
8. Logical background for Knowledge Representation and Reasoning. Principles of Propositional Calculus. Syntax, Semantics, Logical Implication. Truth Tables. Functionally Complete Systems; NAND, NOR. [On-Line via UPEL; 7.04.2020;ALi]
9. Logical background for Knowledge Representation and Reasoning. Logical Equivalent Transformation Rules. Implicants and Implicents. CNF and DNF. [On-Line via UPEL; 21.04.2020;ALi]
10. Logical Inference Rules. Theorem Proving. Resolution and Dual Resolution. Semantic Tableau. Fitch System. Unicorn again. [On-Line via UPEL; 28.04.2020;ALi]
11. In Search for Models: Satisfiability Verification. Decision Trees, Ordered Binary Decision Diagrams. The SAT Problem and SAT Sovers. [On-Line via UPEL; 5.05.2020; ALi]
12. First-Order Predicate Calculus and Resolution Theorem Proving: Towards Logic Programming and Prolog [On-Line via UPEL; 12.05.2020; ALi]
13. Logic Programming and Prolog. Constraint Programming, Rule-Based System and Automated Planning in Prolog. [On-Line via UPEL; 19.05.2020; ALi]
14. Decision Trees, Decision Tables, Rule-Based Systems
15. Graph-Search and Planning
16. Constraint Problem Solving; Constraint Logic Programming
17. Abduction, Abductive Reasoning, Model-Based Reasoning, Consistency-Based Reasoning, Diagnostic Systems.
18. Fuzzy Sets, Fuzzy Logic and Fuzzy Rule-Based Systems vs. Probabilistic Models
19. Invited presentations…
20. Final Examination.

Basic reference handbook:

• The core Handbook: Artificial Intelligence 2E
• Slides for the Handbook: Artificial Intelligence 2E
• Online Resources: Artificial Intelligence 2E

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Support materials/Lecture slides:

1. Introduction to Knowledge Representation and Reasoning
2. Introduction to Knowledge Representation and Reasoning: updated 2020
3. Introduction to KRR and Constraint Programming. Introduction to MiniZinc through examples
4. MiniZinc Tutorial: use Sections 2.1, 2.2., 2.3
5. Introduction to Propositional Logic. Syntax and Semantics. Inference and Theorem Proving.
6. - An Introduction to Logic
7. Propositional Calculus: Syntax, Sematics, Equivalence, CNF, DNF
8. Propositional Calculus: Theorem Proving
9. Propositional Calculus: Model Analysis: Decision Trees, OBDD, and SAT
10. Propositional Calculus for KRR: Theorem Proving, Inference Rules, Resolution, Dual Resolution, Semantic Tableau, Fitch, Unicorn [28.04.2020]
11. Propositional Calculus for KRR: Search for Models, Decision Trees, Ordered Binary Decision Diagrams, SAT; SAT-Solvers and DIMACS format; Unicorn - final solution.
12. First-Order Predicate Calculus for KRR. Resolution and Dual Resolution Method.[Update: 14-05-2020]
13. Example of Resolution Theorem Proving: Marcus and Caesar [Lecture on 12-05-2020]
14. Prolog - support materials

New: Screencast from on-line lectures: video+sound (Use Google Chrome or try your browser):

• Propositional Calculus: Syntax, Sematics, Equivalence, CNF, DNF+examples (1.5h in English)|via MS Office OneDrive?AGH)
• Propositional Calculus: Theorem Proving, Inference Rules, Resolution, Dual Resolution, Semantic Tableau, Fitch (1.5h in English)| via MS Office OneDrive/AGH
• Propositional Calculus: Theorem Proving, Inference Rules, Resolution, Dual Resolution, Semantic Tableau, Fitch (1.5h in Polish)| via MS Office OneDrive/AGH
• Propositional Calculus for KRR: Theorem Proving, Inference Rules, Resolution, Dual Resolution, Semantic Tableau, Fitch, Unicorn (1.5h in English; 28.04.2020)| via MS Office OneDrive/AGH
• Propositional Calculus for KRR: Search for Models, Decision Trees, Ordered Binary Decision Diagrams, SAT; SAT-Solvers and DIMACS format; Unicorn - final solution.(1.5h in English; 5.05.2020)| via MS Office OneDrive/AGH)
• First-Order Predicate Calculus for KRR. Resolution and Dual Resolution Method.(1.5h in English; 12.05.2020)| via MS Office OneDrive/AGH)
• Introduction to Prolog (0.3hh in English; 19.05.2020)| via MS Office OneDrive/AGH)
• Introduction to Prolog: Lists, member/2, append/3, select/3. (0.3hh in English; 19.05.2020)| via MS Office OneDrive/AGH)
• Prolog: by examples. Simple Knowledge-Baseds QA Systems, Constraint Programming, Rule-Based Systems, Meta Programming, Graph Search and Automated Planning (1.3hh in English; 19.05.2020)| via MS Office OneDrive/AGH)

Auxiliary support and internet sources:

• The Official Handbook of MiniZinc
• Some thesis (useful simple introduction to MiniZinc; in polish): MiniZinc - wprowadzenie

1. Introduction to Knowledge Representation and Reasoning. Basic Practical Introduction to Constraint Programming and MiniZinc: 3 simple examples. [27.02.2018; ALi]
2. Introduction to Constraint Programming. Building simple CP models in MiniZinc.* [6.03.2018; ALi]
3. Backtracking Search and Linear Programming Models; cryptoarithmetic examples vs. linear programming optimization. MiniZinc and Python examples. [13.03.2018]
4. Examples of model building in MiniZinc. Arrays, conditional expressions, Boolean constraints and higher-order programming. Job shop model. [20.03.2018; Ali]
5. Knapsack model and Cumulative constraints. Practical applications of Constraint Programming. Some theory: Constraint propagation. [27.03.2018; Ali]
6. [3.04.2018 - Easter breake]
7. Techniques for Constraint Propagation [10.04.2018; ALi]
8. Knowledge Representation with Logic. Propositional Calculus. [17.04.2018; ALi]
9. Reasoning in Propositional Calculus. Resolution Method. Introduction to SAT. [24.04.2018; ALi]
10. [1.05.2018 - Holidays]
11. SAT: problems, techniques, tools and example applications. [8.05.2018; invited presentation]
12. Knowledge Representation with Logic. First-Order Predicate Calculus. [15.05.2018; ALi]
13. Logic Programming and Prolog. [22.05.2018; ALi]
14. Rule-Based Systems and Planning. [29.05.2018; ALi]
15. Individual work: Preparation for the exam. [5.06.2018; ALi]
16. Exam - zero 15:30-17:00 [12.06.2018; ALi]
17. Exams: 27.06.2018 - 11:00, and 4.07.2018- 14:00 Room 429/322 C-2 [ALi]

Lectures: Tuesdays, C-2, Room 429, 15:30-17:00

Preparation for exam 2018 - main focus List of topics

• KRR-introduction
• KRR-introduction-to-constraints
• MiniZinc-1
• MiniZinc-2
• Constraint Programming
• Rule-Based Systems
• Logic
• Prolog

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• MiniZinc
• Hakank on MiniZinc
• SWI-Prolog

1. Introduction to Knowledge Representation and Reasoning. Abduction. Basic Introduction to Constraint Programming [1.03.2017; ALi]
2. Constraint Programming Tools. Introduction to MiniZinc. Building a Model: Einstein Puzzle [8.03.2017; ALi]
3. Constraint Programming Tools. Introduction to MiniZinc. Simple example models. [15.03.2017; ALi]
4. Constraint Programming Tools. Introduction to MiniZinc. Sets and arrays. Aggregation functions. Logical constraints. Production planning example. Job-Shop example. [22.03.2017; ALi]
5. MiniZinc: selected predicates and application examples. Introduction to Constraint Propagation. [29.03.2017; ALi]
6. Introduction to Knowledge Representation. The role of logic. Rule-Based Systems. [5.04.2017;ALi]
7. E-Learning: Logic Programming and Rules: CS227: 8a, 8b; 9a, 9b [12.04.2017]
8. E-Learning: Fuzzy Sets, Fuzzy Logic, Fuzzy Rules. [19.04.2017]
9. Planning. Situation Calculus. STRIPS. Block World Examples. [26.04.2017; ALi]
10. Advanced Planning. Hierarchical Planning. AND/OR Graphs search. Decomposition. Discrete-Even Systems. [10.05.2017]
11. E-Learning: Causal Networks and Probabilistic Models. Bayes Networks. [17.05.2017]
12. E-Learning: Problog: Probabilistic Logic Programming Models. [24.05.2017]
13. E-Learning: ASP - Answer Set Programming. [31.05.2017]
14. Recapitulation: Knowledge Representation and Reasoning. Test Problems. Exam 0. [7.06.2017]
15. E-Learning: Description Logics. [14.06.2017]

Attention: new location: Building C-3, Room 101. Time: Wednesdays, 12:30-14:00

• Introduction to KRR
• Introduction to Constraint Programming

• Hakank Links
• MiniZinc
• Hakank on MiniZinc
• SWI-Prolog
• Bayes Networks
• Problog
• ASP: Answer Set Programming
• Fuzzy Logic
• Introduction to Fuzzy Logic
• Description Logics

• AI Book
• Prolog - page with external links
• CS221: Artificial Intelligence
• CS227: Knowledge Representation and Reasoning
• KRR: PL

There is some background material indication: Exam focus

The following classes will focus on modelling of discrete optimization and constraint satisfaction problems. Student will learn how to represent correctly different problems using constraint programming techniques.

• Lab 1. Constraint Programming: 101
• Lab 2. Constraint Programming: Basic Problems
• Lab 3. Constraint Programming: Basic Techniques
• Lab 4. Constraint Programming: Search Modeling

The following classes will cover automated planning problems. Student will learn how to represent planning problems using constraint programming and dedicated tools.

1. Automated Planning: 101
2. Automated Planning: PDDL
3. Automated Planning: Fluents

This part of the course will present probabilistic programming — a new programming paradigm meant to model domains uncertainty and imperfect knowledge.

1. Probabilistic Programming: 101
2. Probabilistic Programming: Diagnosis and Prediction
3. Probabilistic Programming: Probabilistic Graphs and Decision Theory

There are three projects to choose from:

1. fox-geese-corn — simple planning problem.
2. gangs-wars — problem about optimal ordering of tasks. Quite simple, but it's very difficult to find the optimal solution.
3. port-scheduling — almost real-life problem about scheduling coal deliveries in an Australian port.

All the project are available via gitlab. The test assignment is an obligatory project, that everyone has o finish before starting working on the one real projects. Instructions, how to do the projects are included in the README.md files.

The deadline is simply last class in the semester. While grading I will check:

1. if the model is correct;
2. if the model allows to quickly find a good solution;
3. if the model is comprehensible;
4. what was your work hygiene (how often did you commit, did you contact in case of a problem, etc.)

PS yes, you can do projects in pairs.