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| - | ===== H04. Final homework ===== | + | ===== Homework 4. Five in a row AI ===== |
| - | For the final homework, you **may** choose between two options: | + | Homework 4 consists in developing a complete five-in-a-row game, together with an AI and a very basic interface. |
| - | * **A. Lab homework (0.5p)**. All students which **did not submit** Homework 3, (or submitted a solution which did not compile or got under 30p), may choose this homework. The **maximum** number of points that may be achieved is 0.5p. See details below. | + | |
| - | * **(B) Tic Tac Toe part 2 (1p)**. All students which **submitted** Homework 3 or a consistent part of it may choose this homework. See details below. | + | |
| - | + | ||
| - | * Deadline (for both options): **Friday 27 May** | + | |
| - | + | ||
| - | ===== A. Lab homework (0.5p) ===== | + | |
| - | + | ||
| - | The lab homework consists in providing **complete** solutions to labs [[fp:lab04|4]], [[fp:lab05|5]], [[fp:lab06|6]], [[fp:lab07|9]] and [[fp:lab08|10]], except for exercises marked with **(!)**. | + | |
| - | * **Each completed lab will be graded with 0.1p** | + | |
| - | * **There will be no partial points for partially solved labs** - each lab has to be entirely complete to be graded. | + | |
| - | * **Solutions will be presented live and graded during lab** - no solutions sent by email or MS Teams will be graded. | + | |
| - | * **Solutions should be solved 100% individually**. If some exercises or **parts** of them are taken from other sources, the **entire homework** will not be graded. | + | |
| - | + | ||
| - | + | ||
| - | + | ||
| - | ===== B. Tic Tac Toe part 2 (1p) ===== | + | |
| - | + | ||
| - | Option B. for Homework 4 consists in developing a complete Tic Tac Toe game, together with an AI and a very basic interface. | + | |
| * For this stage, you will decide on the structure of your project, as well as write tests to guide your implementation. | * For this stage, you will decide on the structure of your project, as well as write tests to guide your implementation. | ||
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| * There will be no other partial points, other than those mentioned explicitly in the statement. | * There will be no other partial points, other than those mentioned explicitly in the statement. | ||
| - | ==== B.1. Board analysis (0.2p) ==== | + | ==== 4.1. Board analysis (0.2p) ==== |
| - | For this part, you need to implement a function ''sequences'', that returns a map of the form: ''(a,5), (b,4), (c,3), (d,2)'' where: | + | For this part, you need to implement a function ''sequences'', that returns a map of the form: ''(5,a), (4,b), (3,c), (2,d)'' where: |
| * ''a'' is the number sequences of length 5 that the player has established (on lines, columns or diagonals). | * ''a'' is the number sequences of length 5 that the player has established (on lines, columns or diagonals). | ||
| * ''b'' is the number of sequences of length 4 **which can be filled-out in order to achieve a 5-sequence**. For example: ''XX XX'' is such a sequence for player ''One'', however ''XX0XX'' is not, since this cannot be filled-out in order to achieve a 5-sequence. | * ''b'' is the number of sequences of length 4 **which can be filled-out in order to achieve a 5-sequence**. For example: ''XX XX'' is such a sequence for player ''One'', however ''XX0XX'' is not, since this cannot be filled-out in order to achieve a 5-sequence. | ||
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| * Proper testing is **essential** for this function, as the AI will take decisions based on it. It will be harder to spot problems with ''sequences'' in a later stage of the project. | * Proper testing is **essential** for this function, as the AI will take decisions based on it. It will be harder to spot problems with ''sequences'' in a later stage of the project. | ||
| - | * You can replace the implementation of ''winner'' with a call to this function, to make the code more compact and avoid partial re-implementations | + | * You can replace the implementation of ''winner'' with a call to this function, to make the code more compact and avoid partial re-implementations. |
| <code scala> | <code scala> | ||
| Line 40: | Line 22: | ||
| </code> | </code> | ||
| - | ==== B.2. Functionality (0.35p) ==== | + | ==== 4.2. Functionality (0.35p) ==== |
| For this part, you need to implement a functional project which consists of a running loop: | For this part, you need to implement a functional project which consists of a running loop: | ||
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| ** For this particular part, tests are not necessary**. | ** For this particular part, tests are not necessary**. | ||
| - | ==== B.3. Game AI (0.45p) ==== | + | ==== 4.3. Game AI (0.45p) ==== |
| This part is at the very core of the project, and essentially consists of a function ''play'', which implements the AIs decision of selecting a move. | This part is at the very core of the project, and essentially consists of a function ''play'', which implements the AIs decision of selecting a move. | ||
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| **You may slightly change the signature of this function**, to accommodate your project. | **You may slightly change the signature of this function**, to accommodate your project. | ||
| - | You may start with a very basic (trivial) implementation, which will allow you to move on with **B.2.** and then refine it after **B.2.** is complete. | + | You may start with a very basic (trivial) implementation, which will allow you to move on with **4.2.** and then refine it after **B.2.** is complete. |
| We offer no guidelines about how this function should be implemented, this is entirely up to you, but you may consider: | We offer no guidelines about how this function should be implemented, this is entirely up to you, but you may consider: | ||
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| * Minimax-based AI **0.45p** | * Minimax-based AI **0.45p** | ||
| - | ==== B.4. Bonus (0.2p) ==== | + | ==== 4.4. Bonus (0.2p) ==== |
| + | |||
| + | During the last lecture, we will play a tournament of Five-in-a-row, in which all your AI programs will compete. | ||
| - | During the last lecture, we will play a tournament of Tic-Tac-Toe, in which all your AI programs will compete. | + | **The author the best AI implementations will be awarded a bonus of 0.2p**. |
| - | **The author of the best AI will be awarded a bonus of 0.2p**. | + | ** The winner of the tournament will receive a maximal grade of 10 for the entire lecture (no attendance to the exam will be necessary)**. |