inheritance, polymorphism, board games, randomization
- Get experience with inheritance, polymorphism, and class hierarchies in the domain of board turn-based strategic games.
- Enrich your pallette of C++ tools to define complex behavior:
- Abstract and concrete classes for hierarchical behavior extension.
virtual
functions and overriding to specialize behavior while supporting polymorphism.- functors to specify dynamic object behavior.
- The
<random>
library to randomize object behavior. - A
virtual
binary operator to implement object interaction. - STL classes for data management.
- Develop in one go a small-scale semi-interactive game with bare-bones ASCII visualization.
- Continue using git and Github.
- Develop good coding style.
PA4 leaves the clustering domain and gives a glimpse of the game domain. While it is a chance to put any C++ features we have covered to new use, its main goal is to give a fertile setting for dynamic (run-time) polymorphism of user types. You will create a class hierarchy with a rich tapestry of polymorphic behavior. You have to write quite a few more files than usual, though most are small: Exceptions.cpp
, Piece.cpp
, Agent.cpp
, Simple.cpp
, Strategic.cpp
, Resource.cpp
, Food.cpp
, Advantage.cpp
, Game.cpp
, DefaultAgentStrategy.cpp
, and AggressiveAgentStrategy.cpp
. See the Detailed Instructions at the bottom of this file.
PA4 is in the test-driven-development (TDD) style, just like the preceding assignments. It has 153 tests that your implementation should pass for full points. Because the different elements of the Game
are quite intertwined, it is recommended that you follow roughly this order of implementation and testing:
Game
smoke test.Game
printing.Piece
smoke test.Piece
printing.Game
population.Surroundings
smoke test.- Action smoke test.
- Other
Piece
tests. - Randomization test.
Game
play.
This assignment may be smaller in terms of the total lines of code relative to the preceding ones but is not trivial. It might take just as much time as the others since it introduces new language features and has several points of algorithmic complexity.
You don't need to submit anything. Once you fork the repository (this is your remote repository on Github, aka origin), you will clone it to your development machine (this is your local repository), and start work on it. Commit your changes to your local repository often and push them up to the remote repository occasionally. Make sure you push at least once before the due date. At the due date, your remote repository will be cloned and tested automatically by the grading script. Note: Your code should be in the master branch of your remote repository.
An autograding script will run the test suite against your files. Your grade will be based on the number of tests passed. (E.g. if your code passes 3 out of 6 test cases, your score will be 50% and the grade will be the corresponding letter grade in the course's grading scale). The test suite for PA4 has 153 tests. Note: The testing and grading will be done with fresh original copies of all the provided files. In the course of development, you can modify them, if you need to, but your changes will not be used. Only your Exceptions.cpp
, Piece.cpp
, Agent.cpp
, Simple.cpp
, Strategic.cpp
, Resource.cpp
, Food.cpp
, Advantage.cpp
, Game.cpp
, DefaultAgentStrategy.cpp
, and AggressiveAgentStrategy.cpp
files will be used.
Your program should run on GCC 4.9.0 or later, or Clang 3.3 or later. No other compilers are supported.
The assignment is due on Sun, Apr 17, at 23:59 Mountain time. No late work. The last commit to your PA4 repository before the deadline will be graded.
Free Github repositories are public so you can look at each other's code. Please, don't do that. You can discuss any programming topics and the assignments in general but sharing of solutions diminishes the individual learning experience of many people. Assignments might be randomly checked for plagiarism and a plagiarism claim may be raised against you.
You are encouraged to make maximum use of the Standard Library, especially the Standard Template Library (STL).
Familiarize yourself with and start following coding style guidelines. There are others on the Web. Pick one and be consistent. Note: If you stumble on the Google C++ Style Guide, be advised that it has been heavily criticized by many leading C++ programmers. I don't advise you to follow it, especially the more advanced features. This Guide is for entry-level coders at Google who need to be able to work with their legacy code. It is not advisable for new projects and novice programmers.
Operator overloading guidelines. For the implementation of virtual Piece &operator*(Piece &other)
, see this example.
A very good C++ tutorial, including many topics we are covering.
Two websites with C++ Reference, here and here.
PA5 creates a simple board turn-based Game where Piece
-s move around and interact. This is how a short run of the Game looks like in print:
Round 0
[T998 ][T1000][F1001]
[S999 ][ ][ ]
[ ][ ][F1002]
Status: Playing...
Round 1
[ ][ ][F1001]
[ ][ ][ ]
[S999 ][ ][F1002]
Status: Playing...
Round 2
[ ][ ][F1001]
[ ][ ][ ]
[ ][S999 ][F1002]
Status: Playing...
Round 3
[ ][ ][F1001]
[ ][ ][ ]
[ ][ ][S999 ]
Status: Playing...
Round 4
[ ][ ][F1001]
[ ][S999 ][ ]
[ ][ ][ ]
Status: Playing...
Round 5
[ ][ ][S999 ]
[ ][ ][ ]
[ ][ ][ ]
Status: Over!
Piece
-s are of different type and have polymorphic (i.e. different) behavior. The class hierarchy is as follows:
The dotted boxes represent abstract classes, the rest represent concrete classes. The codes in the grid above correspond to instantiations of the concrete classes, i.e. these are the code names/id-s of objects of type Food
(e.d. F333
), Advantage
(e.g. D456
), Simple
(e.g. S146
), and Strategic
(e.g. T987
). The game executes rounds, gives each piece which is still on the board a turn, and then it prints itself, letting the Piece-s fill in their code names in the appropriate positions.
The grid is two-dimensional. It has a width and height. Each position has coordinates (x, y), with x indexing rows from top to bottom, and y indexing columns from left to right. Both x and y and 0-indexed. The following grid is a 4 x 5, i.e. width = 4 and height = 5, and so x runs from 0 to 4 and y runs from 0 to 3.
(0,0) | (0,1) | (0,2) | (0,3) |
(1,0) | (1,1) | (1,2) | (1,3) |
(2,0) | (2,1) | (2,2) | (2,3) |
(3,0) | (3,1) | (3,2) | (3,3) |
(4,0) | (4,1) | (4,2) | (4,3) |
Internally, the grid is represented as a vector of size 4 * 5 = 20. The indices correspond to grid positions as follows:
0 | 1 | 2 | 3 |
4 | 5 | 6 | 7 |
8 | 9 | 10 | 11 |
12 | 13 | 14 | 15 |
16 | 17 | 18 | 19 |
The Game
uses both representations as they are good for different purposes. You have to come up with simple formulae to convert back and forth.
The behavior of the Piece
-s is based on what's around them. The Surroundings
structure is a 3 x 3 grid which creates a map of the 8 positions which surround the current position of a Piece
. The 8 positions have the following relation with the position (x, y) of the Piece
:
(-1,-1) | (-1,0) | (-1,1) |
(0,-1) | (x,y) | (0,1) |
(1,-1) | (1,0) | (1,1) |
Surroundings holds an array of length 9 and the indices correspond to the positions around a Piece as follows:
0 | 1 | 2 |
3 | 4 | 5 |
6 | 7 | 8 |
A Surroundings
structure's array is filled with values of the enumerated type PieceType
: EMPTY
, INACCESSIBLE
(i.e. outside the Game
grid), SIMPLE
, STRATEGIC
, FOOD
, ADVANTAGE
, and SELF
. SELF
is always in the middle, at index 4.
The behavior of Piece
-s is based on actions, which are picked by each Piece
's strategy. Each Piece
on the Game
board gets a turn each Game
round.
Available actions are captured in the enumerated class ActionType
: N
, NE
, NW
, E
, W
, SE
, SW
, S
, and STAY
. The compass directions correspond to the 8 directions a Piece
can move in a grid from its current position. STAY
means no motion, i.e. stay where you are.
Resource
-s don't move so they always return STAY
.
Agent
-s can move in all directions that are legal depending on the current state of the Game
, or STAY
in place.
As Piece
-s move around the grid they run into others. Each time that happens there is an interaction taking place. This is captured by the pure virtual operator*
(aka interaction operator) that Piece
-s overload. The interactions are as follows:
- When an
Agent
moves onto aResource
, it consumes it completely. - When an
Agent
moves onto anotherAgent
, they compete and might either both die, or one dies and the other wins.
Picking an ActionType
based on the Surroundings
is captured in the Strategy
class. The Simple
agent doesn't have a separate Strategy
object, while the Strategic
agent has a dedicated Strategy
object.
The Simple
agent's behavior/strategy is as follows:
- It does not challenge other agents (i.e. it doesn't move onto them).
- If there are adjacent Resource-s, returns a motion to one of them.
- If there aren’t adjacent Resource-s, returns a move to an adjacent empty position.
- If there aren't adjacent empty position, returns a STAY.
The default Strategic
agent's strategy (captured in a DefaultAgentStrategy
object) is as follows:
- If there are adjacent Advantage-s, returns a motion to one of them. That is, it prefers Advantage to Food.
- lf there aren't adjacent Advantage-s, returns a move to an adjacent Food.
- If there arent' adjacent Food-s, return's a move to an adjacent empty position.
- If there aren't adjacent empty positions, it returns a move to an adjacent Simple agent (i.e. attacking it).
- If no adjacent Simple agents, returns STAY.
The aggressive Strategic
agent's strategy (captured in an AggressiveAgentStrategy
object) is as follows:
- If the Agent's energy >= aggressiveness threshold AND there are any adjacent Agent-s, challenge one of them.
- Else if adjacent Advantage-s, move onto an Advantage.
- Else if adjacent Food-s, move onto an Food.
- Else if adjacent empty positions, move onto an empty position.
- Else STAY.
Because in the above strategies there could be more than one of each kind of Piece
types that are relevant to an Agent
's active behavior, an implemented functor class PositionRandomizer
is provided in Gaming.h
, and a randomPosition
function in Game
making use of it. Using this function reduces any bias in the motion of an Agent
that might arise from the particular way it traverses its Surroundings
. For example, if you were to always pick the first Piece
of a particular type from the Surroundings
to act upon, there would be an "upward" or "northward" bias of the motion of Agent
-s since they traverse their Surroundings
structure naturally from top to bottom and from left to right per the conventions mentioned in the previous section.
The function randomPosition
takes a std::vector<int>
of positions (as indices in the Surroundings
array) where you found one or another kind or Piece
(one vector for each kind) and returns a random Position
(as an (x, y) in the same Surroundings
grid) for the Agent
to move onto (in implementation of its Strategy
).
The Game
gives each Piece
on the grid a turn in a top-left row-wise bottom-right manner. A Piece
's turn consists of the following:
- The
Game
constructs theSurroundings
of aPiece
. - The
Game
calls the polymorphictakeTurn
on thePiece
, which returns anActionType
. - If the action is legal, the
Game
moves thePiece
by callingsetPosition
on thePiece
. - If the new position is non-empty, the Game calls the interaction
operator*
on the twoPiece
-s (i.e.p1 * p2
), which polymorphically performs one of the two interactions described above. - The
Game
checks if any of the twoPiece
-s have become unviable, by callingisViable
on them, and removes the unviable ones from the board.
The Game
is organized in a series of rounds. Each round effectively gives a turn to all the Piece
-s which are still viable and on the Game
grid. Though the Game
is very simple, the Game::round()
function is fairly complex due to all the little details that have to be taken care of. A round consists of the following steps:
- Go through all the
Piece
-s that are still viable and on the grid (this doesn't change between rounds) and: - If a
Piece
has not taken a turn, give it a turn. UsePiece::getTurned()
. - Call the
Piece::setTurned(true)
to avoid giving a movingPiece
more than one turn per round (e.g. if it happens to move to a grid position you haven't covered in the current round). - Perform the turn and implement all the consequences of the turn (e.g. interaction with another
Piece
). - Delete any
Piece
-s which interacted and, as a result, became unviable. - Go through all the
Piece
-s that are still viable and on the grid after the current round, and: - Call the polymorphic
Piece::age()
. - Call the
Piece::setTurned(false)
to reset the turn for the next round. - Delete any
Piece
-s which have aged to zero.
Some guidelines for implementing Game::round()
:
- Do it incrementally and use the tests to build in more and more detail.
- It might be intuitive that you cycle through the
Game
grid for each round, but it is much better to cycle through astd::set
of all the currently occupied positions of the grid. Cycling through the grid won't be able to handle some of the finer cases. Cycling through theset
, if done correctly, will ensure a fair turn for all thePieces
. - Notice that a
Piece
might become unviable before its turn comes (e.g. aResource
gets consumed by anAgent
that moves ahead of it, or anAgent
gets challenged by another, and one or both die as a result). - Notice that a
Piece
might move to a new position, either through an interaction or through a move to a previously free position. In the first case, the position was already occupied, so it is still in theset
. In the second case, the position was empty, so needs to be added to theset
.
The following static
constants have been used in the implementation of the game and are declared but not defined in the header files. You have to define them in the corresponding cpp files:
Advantage::ADVANTAGE_ID = 'D'
Advantage::ADVANTAGE_MULT_FACTOR = 2.0
Agent::AGENT_FATIGUE_RATE = 0.3
AggressiveAgentStrategy::DEFAULT_AGGRESSION_THRESHOLD = Game::STARTING_AGENT_ENERGY * 0.75
Food::FOOD_ID = 'F'
Game::NUM_INIT_AGENT_FACTOR = 4
Game::NUM_INIT_RESOURCE_FACTOR = 2
Game::MIN_WIDTH = 3
Game::MIN_HEIGHT = 3
Game::STARTING_AGENT_ENERGY = 20
Game::STARTING_RESOURCE_CAPACITY = 10
Resource::RESOURCE_SPOIL_FACTOR = 1.2
Simple::SIMPLE_ID = 'S'
Strategic::STRATEGIC_ID = 'T'
The main.cpp
, GamingTests.h
, GamingTests.cpp
, ErrorContext.h
, and ErrorContext.cpp
contain the test suite for PA4. There is nothing to change here except to comment out tests in main.cpp
that you haven't reached as you implement incrementally.
There are tests that generate exceptions which are caught and reported to std::cerr
, which is not temprally interleaved with std::out
. This may cause the exceptions to appear in "unexpected" places relative to the handler code. Note that this is only an artifact of the different regimes of flushing of the two output stream objects.
Note that Game
play tests can be declared verbose
to print out the Game
rounds. This is for you to implement.
Finally, note that, due to the randomization mentioned above, the three runs of a verbose == true
Game
play test are likely to differ (i.e. the course of the Game
is different).
Again, because the different elements of the Game
are closely intertwined, it is recommended that you follow the following rough order of implementation and testing:
Game
smoke test.Game
printing.Piece
smoke test.Piece
printing.Game
population.Surroundings
smoke test.- Action smoke test.
- Other
Piece
tests. - Randomization test.
Game
play.
The Game
-related headers are: Game.h
, and Gaming.h
. You will need to write a Game.cpp
file. Gaming.h
contains declarations of enumeration classes, structures, and inline classes which are provided for you.
You have headers for all the Piece
class hierarchy: Piece.h
, Agent.h
, Resource.h
, Simple.h
, Strategic.h
, Food.h
, and Advantage.h
. Each will require the corresponding cpp file with implementation of the methods.
In addition, you have the abstract class header (no cpp necessary) Strategy.h
, and the headers for two concrete classes which extend the abstract class: DefaultAgentStrategy.h
, and AggressiveAgentStrategy.h
. You will need to write two corresponding cpp files to implement these classes.
The Game
creates Agent
-s with STARTING_AGENT_ENERGY
and Resource
-s with STARTING_RESOURCE_CAPACITY
.
Piece
-s age every round. Notice that Piece::age()
is pure virtual
. Agent
-s age by subtracting AGENT_FATIGUE_RATE
from their energy, while Resource
-s age by dividing their capacity by the RESOURCE_SPOIL_FACTOR
.
Piece
-s are viable while they are not finished (Piece::isFinished()
) AND their energy/capacity is greater than 0.0. Non-viable Piec
e-s are removed at the end of each Game
round.
Piece::finish()
is called by any Resource
which gets consumed or Agent
which loses a challenge with another Agent
. Specifically, it is called in the implementation of the double-dispatch virtual
interaction operator operator*()
. See next section for details on the operator.
Upon interaction between two Piece
-s, energy/capacity is transferred as follows:
-
When two Agent-s interact, their energies are compared:
-
If equal, both Agent-s call Piece::finish() and are taken out of the Game at the end of the round.
-
If unequal, the Agent with the larger wins, the smaller energy is subtracted from its energy, and the losing Agent calls Piece::finish() and is taken out of the Game at the end of the round.
-
When an Agent and a Resource interact, the Resource-s capacity is added to the Agent-s energy, the Resource calls Piece::finish() and is taken out of the Game at the end of the round. Notice that Resource::getCapacity is virtual and Advantage overrides it. The amount of capacity that is added to the Agent's energy is as follows:
-
For Food, its capacity.
-
For Advantage, its capacity *
ADVANTAGE_MULT_FACTOR
.
For the implementation of the Piece::operator*()
, take a look at this page.
A Game
can be populated manually (default) or automatically. For automatic population, use the following numbers have been useful:
__numInitAgents = (__width * __height) / NUM_INIT_AGENT_FACTOR;
__numInitResources = (__width * __height) / NUM_INIT_RESOURCE_FACTOR;
unsigned int numStrategic = __numInitAgents / 2;
unsigned int numSimple = __numInitAgents - numStrategic;
unsigned int numAdvantages = __numInitResources / 4;
unsigned int numFoods = __numInitResources - numAdvantages;
The Game
is over when there are no more Resource
-s left on the grid. Note that this includes the rare case when Agent
-s are taken off the board before all Resource
-s have been consumed. The latter will age and disappear on their own.
The default Game::Game()
constructor creates a 3 x 3 grid.
To randomize the positions of the Piece-s during automatic population, you can use code like this:
// simple pseudo-random number generator
// sufficient for our casual purposes
std::default_random_engine gen;
std::uniform_int_distribution<int> d(0, __width * __height);
// populate Strategic agents
while (numStrategic > 0) {
int i = d(gen); // random index in the grid vector
if (__grid[i] == nullptr) { // is position empty
Position pos(i / __width, i % __width);
__grid[i] = new Strategic(*this, pos, Game::STARTING_AGENT_ENERGY);
numStrategic --;
}
}
// populate Simple agents
// ...
// Note: you can reuse the generator
For the implementation of some functions, e.g. Game::getNumSimple()
, you will need to know the runtime/dynamic derived type of an upcast object (i.e. that is pointed to by a Piece*
pointer). This is called runtime type information (RTTI) and the following code illustrates the use of the C++ RTTI facility std::dynamic_cast<>
:
// dynamic_cast returns a pointer of the argument type
// or NULL if the derived type of the object is not of
// the argument type
unsigned int Game::getNumSimple() const {
unsigned int numAgents = 0;
for (auto it = __grid.begin(); it != __grid.end(); ++it) {
Agent *agent = dynamic_cast<Simple*>(*it);
if (agent) numAgents ++;
}
return numAgents;
}
More on dynamic_cast
in the C++ Reference.
As mentioned above, std::set
might be useful in the implementation of Game::round()
. The following code contains a contrived example which you might find helpful:
#include <iostream>
#include <set>
int main() {
std::set<int> iset;
iset.insert(1);
iset.insert(2);
iset.insert(3);
iset.insert(5);
iset.insert(6);
iset.insert(8);
for (auto it = iset.begin(); it != iset.end(); ) {
int elementToSearch = 6;
std::cout << "Iterating through " << *it << ".";
if (*it == 2) {
std::cout << " Inserting 0." << std::endl;
iset.insert(0);
++it;
} else if (*it <= 3) {
std::cout << std::endl;
++it;
} else {
std::cout << " Searching for " << elementToSearch << ". ";
auto search = iset.find(elementToSearch);
if (search != iset.end()) {
// Note: std::distance is the way to compare iterators but it requires bidirectional or random-access
// iterators to work in both directions (since they have to be mutually reachable by incrementing)
// these iterators are not implemented for std::set in the Standard Library, and implementing one
// is beyond the scope of this assignment, we resort to using the following hack to give us what we
// need.
int d = elementToSearch - *it;
std::cout << "Found " << elementToSearch << ". Distance from " << *it << " is " << d << ". Erasing." <<
std::endl;
if (d == 0) {
it = iset.erase(search); // if erasing the current element, erase returns an iterator to the next
} else {
iset.erase(search); // if erasing another element, we need to increment the iterator ourselves
++it;
}
} else {
std::cout << "No " << elementToSearch << " found." << std::endl;
++it;
}
}
}
for (int i: iset) std::cout << i << std::endl;
return 0;
}
In particular, notice that std::set::insert()
does not invalidate any iterators, and std::set::erase()
only invalidates the iterator to the current element, returning an iterator to the next element. More on std::set
in the C++ Reference.
This section concerns future revisions of this assignment.
-
(Section 6.3)
Piece::finish()
is called by anyResource
which gets consumed orAgent
which loses a challenge with anotherAgent
. Specifically, it is called in the implementation of the double-dispatchvirtual
interaction operatoroperator*()
. See next section for details on the operator. TODO: This is a game rule, and therefore should be pulled up to the abstract classes. The leaf classes should not be relied upon to implement the game rules faithfully. This will open the possibility for an open implementation of leaf classes by students and team tournaments. -
Add tests for randomization in:
-
Automatic game population.
-
Gameplay.
-
Add tests for rule enforcement in:
-
Actions.
-
Interaction.
-
Termination.
-
Consider alternative game termination conditions.