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      1. Selection Sort
        1. Map Traversal Problem
        2. 8-tiles Puzzle
        1. Map Traversal Problem
              1. one solution
              2. all solutions
              3. find cost/length of the solution
            1. Missionaries and Cannibals Problem
            2. 8-tiles Puzzle
            3. Monkey and Banana Problem
            1. Monkey and Banana Problem
            1. Map Traversal Problem with a global variable and one subnet
            2. Missionaries and Cannibals Problem with a subnet variable and one subnet
            3. Monkey and Banana Problem with a global variable and 2 subnets
            1. Map Traversal Problem
            2. Missionaries and Cannibals Problem with primitives, mixing condition and action
            3. Monkey and Banana Problem with constants as actual subnet’s parameters
            1. Map Traversal Problem
            1. Missionaries and Cannibals Problem
            1. 8-tiles Puzzle
            1. Map Traversal Problem
            1. Map Traversal Problem
            1. Map Traversal Problem
            1. HILL-CLIMBING with allowed downhill-movements - Map Traversal Problem
            2. HILL-CLIMBING with forbidden downhill-movements - Map Traversal Problem
              1. Version 1 - with primitives, mixing condition and action
              2. Version 2 - with constants as actual subnet’s parameters
            1. HILL-CLIMBING with allowed downhill-movements - Map Traversal Problem
            2. HILL-CLIMBING with forbidden downhill-movements - Map Traversal Problem
              1. Version 1 - with primitives, mixing condition and action
              2. Version 2 - with constants as actual subnet’s parameters
            1. HILL-CLIMBING with allowed downhill-movements - Map Traversal Problem
            2. HILL-CLIMBING with forbidden downhill-movements - Map Traversal Problem
              1. Version 1 - with primitives, mixing condition and action
              2. Version 2 - with constants as actual subnet’s parameters
            1. STOCHASTIC HILL-CLIMBING - Map Traversal Problem
            1. STOCHASTIC HILL-CLIMBING - Map Traversal Problem
            1. FIRST-CHOICE HILL-CLIMBING - Map Traversal Problem
          1. The Traveling Salesperson
          1. FORWARD CHECKING with static and dynamic variable and value ordering heuristics (MRV, degree and LCV)
          2. FORWARD CHECKING with static static variable and value ordering heuristics (degree and static LCV)
          3. FORWARD CHECKING
          4. Stochastic FORWARD CHECKING (Forward Checking with random variable and value ordering)
          1. CBLS with most conflicted variable chosen
          2. Stochastic CBLS (CBLS with randomly chosen conflicted variable)
          1. FORWARD CHECKING with static and dynamic variable and value ordering heuristics (MRV, degree and LCV)
          2. FORWARD CHECKING with static static variable and value ordering heuristics (degree and static LCV)
          3. FORWARD CHECKING
          4. Stochastic FORWARD CHECKING (Forward Checking with random variable and value ordering)
          1. CBLS with most conflicted variable chosen
          2. Stochastic CBLS (CBLS with randomly chosen conflicted variable)
        1. Animal Identification Problem (forward execution)
        2. Animal Identification Problem (backward execution)
        1. NFA
          1. Regular Expressions
          2. Arithmetic Expressions
        2. Nondeterministic Turing machine
        3. Probabilistic Turing machine – declarative solution
        4. Probabilistic Turing machine – procedural solution
        5. Game Tree Evaluation
        1. CNP model of a core Prolog program
        2. Simulating the PROLOG’s CUT
          1. Confirming the choice of a rule
          2. The CUT-FAIL combination (modeling of the NOT)
        3. Avoiding (stopping) an infinite loop
      1. Technical Example (subnets are without parameters)
      1. Animal identification, forward execution
      2. Animal identification, backward execution
      3. Arithmetic Expression Recognition
      4. Selection Sort
      5. Sheep, Wolves and Boatman, Iterative CN
      6. Sheep, Wolves and Boatman, Recursive CN
      1. Technical Example (different from Melbourne publication)
      1. Map Traversal Problem, one solution (Complete CN)
      2. Map Traversal Problem, all solutions (Complete CN)
      3. Map Traversal Problem, all solutions of limited length (Iterative CN)
      4. Map Traversal Problem, all solutions of limited length (Recursive CN)
      1. CNP model of a core Prolog program
      1. Simulating the PROLOG’s CUT
      2. Simulation of the double-step function
      3. Simulation of the NOT
      4. Avoiding (stopping) an infinite loop
      1. A*-algorithm for solving the 8-puzzle problem
      2. Map Traversal Problem, all paths with their costs
      3. Map Traversal Problem, optimal Search (version of BRANCH-AND-BOUND)
      4. COST-LIMITED SEARCH with a Complete CN
      1. Non-recursive Hill-Climbing solution with usage of control state ORDER
      2. Non-recursive Hill-Climbing solution using control state RANGE
      3. HILL-CLIMBING with a Recursive CN
      4. IRREVOCABLE HILL-CLIMBING allowing downhill-movements with a Complete CN
      5. IRREVOCABLE HILL-CLIMBING not allowing downhill-movements with a Complete CN
      6. IRREVOCABLE HILL-CLIMBING with a Recursive CN
      7. HILL-CLIMBING with limited revocability allowing downhill-movements with a Complete CN
      8. HILL-CLIMBING with limited revocability not allowing downhill-movements with a Complete CN
      9. HILL-CLIMBING with limited revocability with a Recursive CN
      10. STOCHASTIC HILL-CLIMBING with a Complete CN
      11. STOCHASTIC HILL-CLIMBING with a Recursive CN
      12. FIRST-CHOICE HILL-CLIMBING with a Complete CN
      13. SIMULATED ANNEALING for the Traveling Salesperson
      1. Generic Systematic Search (DEPTH-FIRST WITH LEAP-FROGGING, BREADTH-FIRST, UNIFORM-COST, BEST-FIRST, A*)
      2. Non-recursive Hill-Climbing solution using control state RANGE
      3. HILL-CLIMBING with a Recursive CN
      4. SIMULATED ANNEALING for the Traveling Salesperson
        1. Map Traversal Problem – MTP Generic Search
        2. 8-tiles Puzzle – 8-tiles Generic Search
      1. Non-recursive Hill-Climbing solution using control state RANGE
      2. HILL-CLIMBING with a Recursive CN
      3. SIMULATED ANNEALING for the Traveling Salesperson
      1. Technical Example (different from Melbourne publication)
        1. Map Traversal Problem
        2. Map Traversal Problem
      1. Declarative solution for the Monkey-and-banana problem – representing the room
      2. Declarative solution for the Monkey-and-banana problem – representing the state space
      3. Declarative solution for the Monkey-and-banana problem - iterative CN
      4. Declarative solution for the Monkey-and-banana problem - recursive CN
      5. Non-recursive Hill-Climbing solution with usage of control state ORDER
      6. Non-recursive Hill-Climbing solution using control state RANGE
      7. HILL-CLIMBING with a Recursive CN
      8. IRREVOCABLE HILL-CLIMBING allowing downhill-movements with a Complete CN
      9. IRREVOCABLE HILL-CLIMBING not allowing downhill-movements with a Complete CN
      10. RREVOCABLE HILL-CLIMBING with a Recursive CN
      11. HILL-CLIMBING with limited revocability allowing downhill-movements with a Complete CN
      12. HILL-CLIMBING with limited revocability not allowing downhill-movements with a Complete CN
      13. HILL-CLIMBING with limited revocability with a Recursive CN
      14. STOCHASTIC HILL-CLIMBING with a Recursive CN
      15. FIRST-CHOICE HILL-CLIMBING with a Complete CN
      16. SIMULATED ANNEALING for the Traveling Salesperson
      1. Probabilistic Turing machine – nonprocedural solution
      2. Probabilistic Turing machine – procedural solution
      3. Stochastic hill climbing – nonprocedural non-recursive solution
      4. Stochastic hill climbing – nonprocedural recursive solution
      1. NFA
      2. NCFG, Regular Expressions
      3. Nondeterministic Turing machine
      1. FORWARD CHECKING with static and dynamic variable and value ordering heuristics (MRV, degree and LCV)
      2. FORWARD CHECKING with static static variable and value ordering heuristics (degree and static LCV)
      3. FORWARD CHECKING
      4. Stochastic FORWARD CHECKING (Forward Checking with random variable and value ordering)
      5. CBLS with most conflicted variable chosen
      6. Stochastic CBLS (CBLS with randomly chosen conflicted variable)
      1. NDA
      1. NDA
      1. Technical Example (Cambridge 2 Paper)
      1. Generic Systematic Search (DEPTH-FIRST WITH LEAP-FROGGING, BREADTH-FIRST, UNIFORM-COST, BEST-FIRST, A*)
      2. Procedural BACKRACKING
      3. Declarative BACKRACKING with a Complete CN (one solution)
      4. Declarative BACKRACKING with a Complete CN (all solutions)
      5. Declarative BACKRACKING with a Complete CN (find cost/length of the solution)
      6. Declarative BACKRACKING with an Iterative CN (global variable and one subnet)
      7. Declarative BACKRACKING with an Recursive CN
      8. COST-LIMITED SEARCH with a Complete CN
      9. DEPTH-LIMITED SEARCH with an Iterative CN
      10. DEPTH-LIMITED SEARCH with an Recursive CN
      11. Optimal Search (version of BRANCH-AND-BOUND)
      12. HILL-CLIMBING allowing downhill-movements with a Complete CN
      13. HILL-CLIMBING not allowing downhill-movements with a Complete CN
      14. HILL-CLIMBING with a Recursive CN and primitives, mixing condition and action
      15. HILL-CLIMBING with a Recursive CN and constants as actual subnet’s parameters
      16. IRREVOCABLE HILL-CLIMBING allowing downhill-movements with a Complete CN
      17. IRREVOCABLE HILL-CLIMBING not allowing downhill-movements with a Complete CN
      18. IRREVOCABLE HILL-CLIMBING with a Recursive CN and primitives, mixing condition and action
      19. IRREVOCABLE HILL-CLIMBING with a Recursive CN and constants as actual subnet’s parameters
      20. HILL-CLIMBING with limited revocability allowing downhill-movements with a Complete CN
      21. HILL-CLIMBING with limited revocability not allowing downhill-movements with a Complete CN
      22. HILL-CLIMBING with limited revocability with a Recursive CN and primitives, mixing condition and action
      23. HILL-CLIMBING with limited revocability with a Recursive CN and constants as actual subnet’s parameters
      24. STOCHASTIC HILL-CLIMBING with a Complete CN
      25. STOCHASTIC HILL-CLIMBING with a Recursive CN
      26. FIRST-CHOICE HILL-CLIMBING with a Complete CN
      1. Declarative BACKRACKING with a Complete CN
      2. Declarative BACKRACKING with an Iterative CN (subnet variable and one subnet)
      3. Declarative BACKRACKING with an Recursive CN
      4. COST-LIMITED SEARCH with a Recursive CN
      1. Declarative BACKRACKING with a Complete CN (copy of the Simple State Space)
      2. Declarative BACKRACKING with a Complete CN (copy of the State Space)
      3. Declarative BACKRACKING with an Iterative CN (global variable and 2 subnets)
      4. Declarative BACKRACKING with an Recursive CN (constants as actual subnet’s parameters)
      1. Generic Systematic Search (DEPTH-FIRST WITH LEAP-FROGGING, BREADTH-FIRST, UNIFORM-COST, BEST-FIRST, A*)
      2. Declarative BACKRACKING with a Complete CN (copy of the Simple State Space)
      3. DEPTH-LIMITED SEARCH with an Complete CN (copy of the Simple State Space)
      1. SIMULATED ANNEALING
      1. FORWARD CHECKING with static and dynamic variable and value ordering heuristics (MRV, degree and LCV)
      2. FORWARD CHECKING with static static variable and value ordering heuristics (degree and static LCV)
      3. FORWARD CHECKING
      4. Stochastic FORWARD CHECKING (Forward Checking with random variable and value ordering)
      5. CBLS with most conflicted variable chosen
      6. Stochastic CBLS (CBLS with randomly chosen conflicted variable)
      1. Forward Chaining
      2. Backward Chaining
      1. NFA
      2. NDA 1
      3. NDA 2
      1. Regular Expressions
      2. Arithmetic Expressions
      1. Nondeterministic Turing machine
      1. Probabilistic Turing machine – declarative solution
      2. Probabilistic Turing machine – procedural solution
      1. CNP model of a core Prolog program
      2. Simulating the PROLOG’s CUT
      3. Modeling of the common uses of the CUT (Confirming the choice of a rule)
      4. Modeling of the common uses of the CUT (The CUT-FAIL combination, modeling of the NOT)
      5. Avoiding (stopping) an infinite loop
      1. Selection Sort
      1. ??????????????
      1. Water Jug Iterative
      2. Water Jug Recursive