Artificial Intelligence


Artificial Intelligence - 2022-2023 [AIDA]

  1. Introduction to Artificial Intelligence. Organization. Syllabus and how to pass the course. [29.02.2024; Ali] Introduction to AI'2024}}
  2. Presentations: Gurobi. Grammatical Evolution. [7.03.2024].
  3. Presentations: Grammatical Evolution. Definitions of AI. The AI-CI-ML Triangle. Knowledge Representation and Reasoning. Problem-Solving. Example problems. Japanese IQ Test. Generic Problem-Solving. SEND+MORE=MONEY ? [14.03.2024; Ali]
  4. Presentations: Military AI. Neural Nets Devices. [21.03.2024; Ali]
  5. Easter Holidays [28.03.2024]
  6. [4.04.2024; Ali]

Support 2024:

See also links below…

Artificial Intelligence 2023/2024-winter [PSI+CS]

  1. Introduction to the course. Syllabus and how to pass the course. Introduction to AI. Intelligence and Artificial Intelligence. Example Problems. SEND+MORE=MONEY. [3.10.2023;ali-PSI][5.10.2023;ali-CS]
  2. Japanese IQ Test. Generic Problem Solving. SEND+MORE=MONEY ? Introduction to AI. Example Problems Core techniuqes. On Methods of Problem-Solving. The AI-ML-CI Triangle. [10.10.2023;ali-PSI][12.10.2023;ali-CS]
  3. ABCD * 4 = DCBA. Introduction to Artificial Intelligence. Tools and examples of problem-solving. Logical Knowledge Representation and Reasoning. Towards a Taxonomy. [17.10.2023;ali-PSI][19.10.2023;ali-CS]
  4. E-learning: What is AI? AI - more advanced AI - the classics [24.10.2023-PSI;ali]
  5. Knowledge Representation and Reasoning in Prolog: syntax, terms, facts, clauses. Logical foundations, operation, examples. Cut and fail. Backtracking search. Deductive Data Bases. [26.10.2023-CS;ali]
  6. E-learning PSI: 31.10.2023: Knowledge Representation and Reasoning in Prolog: syntax, terms, facts, clauses. Logical foundations, operation, examples. Cut and fail. Backtracking search. Deductive Data Bases. Prolog-1: Syntax, Variables, Terms, Clauses Book: Chapters 0,1,2,3 + videos [31.10.2023-PSI] Więcej na stronie: Kursy Prologu
  7. Knowledge Representation and Reasoning in Prolog: syntax, terms, facts, clauses. Logical foundations, operation, examples. Cut and fail. Backtracking search. Deductive Data Bases. [7.11.2023-PSI;ali]
  8. Prolog: terms vs. lists. Basic operations on lists. Member vs. Select. Append. Examples of applications. [9.11.2023-CS;ali][14.11.2023-PSI;ali]
  9. Prolog: advanced list operations. Lists Ordering. Applications.Recursion, Iteration and Loops. Assert and Retract. Metapredicates; findall/3 and maplist/1/2/3. [16.11.2023-CS;ali][21.11.2023-PSI;ali]
  10. Graph Search Algorithms in AI: Tree Search vs. Graph Search. Blind Search DFS Implementation in Prolog.[23.11.2023-CS;ali][28.11.2023-PSI;ali]
  11. A Review of Graph Search Algorithms in AI: DFS, BFS, UC vs. Dijkstra. Implementation in Prolog - Examples. Extensions and Modifications. [30.11.2023-CS;ali][5.12.2023-PSI;ali]
  12. Graph Search: Heuristics and Heuristic Methods. Greedy Search. The A* Algorithm. Examples in Prolog. [7.12.2023-CS;ali][12.12.2023-PSI;ali]
  13. Automated Plan Generation. Robot Planning. [14.12.2023-CS;ali][19.12.2023-PSI;ali]
  14. Hierarchical Task Networks. AND-OR plans. [21.12.2023-CS;ali]
  15. ChatGPT-3: prezentacja [9.01.2024-PSI;gcz]
  16. Partially ordered plans. Hierarchical plans, HTN.AND-OR plans. Knowledge Compilation. Rule-Based Systems. BPMN. [4.01.2024-CS;ali]
  17. Constraint Programming. MiniZinc.Prolog+clpfd. [11.01.2024-CS;ali]
  18. E-learning PSI. Przygotowanie do egzaminu. Constraint Programming. MiniZinc. [16.01.2024-PSI;ali]
  19. Constraint Programming. [18.01.2024-CS;ali]
  20. Zero Exam AI/PSI [24.01.2024-PSI;ali]
  21. Zero Exam AI/CS [25.01.2024-CS;ali]

New:

Problems Solved by Students:


Artificial Intelligence - 2022-2023 [AIDA]

  1. E-Learning: Introduction to AI. [2.03.2023]
  2. Introduction to Artificial Intelligence. The AI-CI-ML Triangle. Knowledge Representation and Reasoning. Problem-Solving. [9.03.2023] Introduction to Artificial Intelligence
  3. Introduction to Artificial Intelligence. Tools and examples of problem-solving. Logical Knowledge Representation and Reasoning. Introduction to Prolog; First (3) examples. [16.03.2023]
  4. Knowledge Representation and Reasoning in Prolog: syntax, terms, facts, clauses. Logical foundations, operation, examples. Cut and fail. Backtracking search. [23.03.2023]
  5. Prolog: terms vs. lists. Basic operations on lists. Member vs. Select. Append. Examples of applications.[30.03.2023]
  6. Easter Holidays [6.04.2023]
  7. Prolog - continued; advanced operations of lists. Applications of lists. Examples. [13.04.2023]
  8. Intro to KRR in Prolog. Graph Search: Blind Methods. Modeling in Prolog. [20.04.2023]
  9. Intro to KRR in Prolog. Graph Search: Blind Methods. DFS as a basic method. Backtracking. Improving efficiency. Modeling in Prolog. [27.04.2023]
  10. Intro to KRR in Prolog. Graph Search: Heuristic Methods. Modeling in Prolog. [4.05.2023]
  11. KRR continued. Graph Search: Heuristics and Heuristic Methods. The A* Algorithm. [11.05.2023]
  12. Constraint Programming; on-line UPEL[18.05.2023]
  13. Automated Design of Algorithms by Thomas Stutzle. Place: Room 3.27B D17 (Informatyka) [11:00 25.05.2023]
  14. 2 student presentations. MiniZinc for CP. [1.06.2023]
  15. [14-15.06.2023 - exam zero. Info by/on Upel/Moolde]

Support:

Problog - a quick intro

Lectures 2022-2023 Winter [CSC/PSI]

  1. Inauguracja Roku Akademickiego [4.10.2022] [CSC/PSI]
  2. Introduction to Artificial Intelligence. The AI-CI-ML Triangle. Knowledge Representation and Reasoning. Problem-Solving. [6.10.2022-CSC][11.10.2022-ISI] Introduction to Artificial Intelligence
  3. Introduction to Artificial Intelligence. Knowledge Representation and Reasoning. Problem-Solving. Problems Taxonomy and Problem-Solving Methods and Tools. [13.10.2022-CSC][18.10.2022-ISI]
  4. Introduction to Artificial Intelligence. Tools and examples of problem-solving. Logical Knowledge Representation and Reasoning. Introduction to Prolog; First (3) examples. [20.10.2022-CSC][25.10.2022-ISI]
  5. Knowledge Representation and Reasoning in Prolog: syntax, terms, facts, clauses. Logical foundations, operation, examples. Cut and fail. Backtracking search. [27.10.2022 - CSC][15.11.2022-ISI]
  6. Prolog: terms vs. lists. Basic operations on lists. Member vs. Select. Examples of applications.[3.11.2022-CSC][22.11.2022-ISI]
  7. E-Learning:[10.11.2022-CSC] and [8.11.2022-ISI]
  8. Prolog: advanced list operations. Append. Recursion vs. iteration. Examples of applications. [17.11.2022-CSC][29.11.2022]
  9. Prolog: advanced list operations. Ordering. Applications. [24.11.2022-CSC][6.12.2022-ISI]
  10. Graph Search: Blind Methods. Modeling in Prolog. [1.12.2022][6.12.2022]
  11. Graph Search: Heuristic Methods. Modeling in Prolog. [8.12.2022] [13.12.2022]
  12. Rule-Based Systems and Decision Trees. Expert Systems. DSS. [15.12.2022][20.12.2022 - planned: on-line]
  13. Automated Planning. Modeling in Prolog. [5.01.2023][3.01.2023]
  14. Constraint Programming. [12.01.2023][10.01.2023]
  15. Constraint Programming. MiniZinc. Constraint Logic Programming. SWI-Prolog+clp(fd). Picat [19.01.2023][17.01.2023 ISI Exam 0 (na UPEL)]
  16. [26.01.2023] Exam 0 (on UPEL)

Support for Prolog: Lectures on Prolog [slides 2021/2022] + Sets of Examples + interesting links


With Best Christmas Wishes 2022 - just for fun:

Artificial Intelligence - 2021-2022 [AIDA]


  1. Introduction to AI and CSP/CP. A first note on MiniZinc [2.03.2022; ALi]
  2. Introduction to CP and MiniZinc [9.03.2022; ALi
  3. AI: 16.03.2022: E-lerning:
  4. MiniZinc - continuation[23.03.2022;ALi] AWARIA UPEL –> zajęcia były na Teams.
  5. MiniZinc: final examples. Practical applications: TSP, Nurse Rostering. [30.03.2022;ALi]
  6. Constraint Programming: Theoretical Foundations. Constraint Propagation Algorithms. [6.04.2022;ALi]
  7. Logical Foundations of AI. Introduction to Logic Programming and Prolog. [13.04.2022; ALi]
  8. Easter Holidays - no break in lectures.
  9. 20.04.2022
  10. 27.04.2022
  11. 4.05.2022
  12. 11.05.2022
  13. 18.05.2022
  14. 25.05.2022
  15. 1.06.2022
  16. 8.06.2022 - Last meeting!

Support for Lectures

Artificial Intelligence - 2021-2022 [AI-CS-5 + PSI-ISI-3]


Lectures 2021

  1. Introduction to Artificial Intelligence. Knowledge Representation and Reasoning. Problem-Solving. Info on Local Search Methods for Heuristic Optimization. [5.10.2021; ALi] Introduction to Artificial Intelligence
  2. Introduction to Artificial Intelligence. Knowledge Representation and Reasoning. Problem-Solving. Problems Taxonomy and Problem-Solving Methods and Tools. [12.10.2021; ALi]
  3. Introduction to Artificial Intelligence. Tools and examples of problem-solving. Introduction to Prolog; 3 examples. [19.10.2021; ALi]
  4. Prolog: syntax, terms, logical foundations, operation, examples. Cut and fail. Backtracking search. [26.10.2021; ALi]
  5. Prolog: basic operations on lists. Recursion vs. iteration. Examples of applications. [2.11.2021; ALi]
  6. Prolog: advanced list operations. Examples of applications. [9.11.2021; ALi]
  7. Graph Search: blind strategies. Implementation in Prolog. [16.11.2021;ALi]
  8. E-learning: Selected applications of Prolog. Selected lectures and examples [23.11.2021]
  9. E-learning:Search: heuristic strategies. Implementation in Prolog. [30.11.2021; ALi]
  10. E-Learning: Knowledge Engineering. Rule-Based Systems, Automated Planning, Deductive Databasases (Q&A). [7.12.2021; ALi]
  11. Planned in Room 429/C-2: Knowledge Engineering. Graph Search and Automated Planning [14.12.2021; ALi]
  12. On-line via Moodle VC: Graph Search: heuristic strategies. Implementation in Prolog. [21.12.2021; ALi]
  13. On-line via Moodle VC: Introduction to Constraint Programming. [4.01.2022; ALi]
  14. Constraint Programming: Introduction to MiniZinc. [11.01.2022;ALi] Room 429/C-2
  15. Rule-Based Systems and Decision Trees. Machine Learning. Model-Based Reasoning. [18.01.2022; ALi] Room 429/C-2
  16. Exam Zero [25.01.2022; planned on Moodle via Internet]

Introduction to AI 2021 - Selected labs

  1. Logic programming 1 [22.10.2021, WTA]
  2. Logic programming 2 [29.10.2021, WTA]
  3. State Space Search [05.11.2021,19.11.2021, MSL]

Podstawy Sztucznej Inteligencji 2021 - Wybrane laboratoria

Lectures - 2021 - Summer [closed]


  1. Introduction to Artificial Intelligence. Knowledge Representation and Reasoning. Basic Practical Introduction to Constraint Programming and MiniZinc: 3 simple examples. [4.03.2021; ALi] Constraint Programming: Introduction to MiniZinc MiniZinc Examples - 3.03.2021
  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. Constraint Satisfaction vs. Optimization with Constraints. MiniZinc vs. Python and Prolog examples. [11.03.2021; ALi] MiniZinc Examples - 11.04.2021 Python+Prolog-examples-11-03-2021
  3. Backtracking Search and Linear Programming Models; CSP vs COpt. Production planning; using external data sets. Real numbers: loan and laplace. Simple production planning: sets, sets, arrays, program parametrization. Aggregation and quantification. [18.03.2021; ALi]
  4. Modeling in MiniZinc: Sudoku, symmetry braking, knapsack, jobshop, cumulative and other applications in MiniZinc. [25.03.2021; ALi]MiniZinc Examples - 25.03.2021
  5. Happy Easter! [1.04.2021]
  6. Real-World Examples of Constraint Programming. Introduction to Theory of CP. Constraint Propagation. [8.04.2021;ALi]
  7. Constraint Programming. Backtracking Search and Constraint Propagation. Forward Checking. Arc Consistency. Tree-Structured Problems. Problem Decomposition. Nurse Rostering Problem. [15.04.2021;ALi] Constraint Programming
  8. AI- a Big Picture. Graph Search as a Model of Problem Solving. Blind Search. [22.04.2021;ALi]
  9. AI - Heuristic Search Methods. Heuristics and their Properties. Greedy Search and the A* Algorithm. Automated Planning. State-Space Modeling. Hill Climbing and Genetic Algorithms. [29.04.2021;ALi
  10. AI - Introduction to Rule-Based Systems. Foundations, Technologies, Tools and Applications. [6.05.2021; KKl]
  11. AI - Semantic Web and Description Logics. Concepts, Ontologies, Technologies and Application Areas. [13.05.2021; WTA]
  12. AI - [20.05.2021; MAD]
  13. AI - [27.05.2021; MSL]
  14. Holidays [3.06.2021]
  15. AI - [10.06.2021; ALi]

Basic reference handbook:


—-

Lecture slides + supporting materials

Knowledge Representation and Reasoning

Systems Modelling and Data Analysis + Engineering of Intelligent Systems


Lectures - 2020


  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:


—-

Support materials/Lecture slides:



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


Auxiliary support and internet sources:



Lectures 2018

  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


Lecture slides + supporting materials
Knowledge Representation and Reasoning. Edition 2016/2017
  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


Lecture Slides
Useful Links
Background Material
New: Preparation for Exam

There is some background material indication: Exam focus



Permanently under construction

Laboratories

Constraint Satisfaction and Discrete Optimization

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.

Automated Planning

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

Probabilistic Programming

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

Projects

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.

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