An AI Game Project

An AI Game Project

Background• Fivel is a unique combiation of a NxM game and a sliding puzzle.

• The goals in making this projects were:

• Create an original game experience. • Create a challenging AI player using optimized calculations.• Achieve an attractive visual look (cute and funny woodland

creatures).• Accessibility – Easy-to-use UI.• The Future? The ability to export the game to the web

upon completion.

The GameEach turn, a player puts a piece in an

empty slot, and slides a tile to an empty

place.

The GameEach turn, a player puts a piece in an

empty slot, and slides a tile to an empty

place.

The GameEach turn, a player puts a piece in an

empty slot, and slides a tile to an empty

place.

The GameEach turn, a player puts a piece in an

empty slot, and slides a tile to an empty

place.

The GameEach turn, a player puts a piece in an

empty slot, and slides a tile to an empty

place.

The GameThe first player to place 5 pieces in a

row, column or diagonal at the end of

his turn, wins.

The GameThe first player to place 5 pieces in a

row, column or diagonal at the end of

his turn, wins.

The GameThe first player to place 5 pieces in a

row, column or diagonal at the end of

his turn, wins.

The GameThe first player to place 5 pieces in a

row, column or diagonal at the end of

his turn, wins.

Development

Flash/AS3 • PROS: • Allows to create great presentations and UI’s easily and

fast.• Accessibility.

• CONS: • AS3 is considerably slow.• Flash has a problems with deep-recursions (15 seconds

limit and rendering halting without the use of chunking).

• Problem: Good presentation VS. Strong and reliable computation.

• Problem: Good presentation VS. Strong and reliable computation.

Java • PROS: • Fast and reliable for deep recursions.• Everybody knows Java!

• CONS: • Harder to achieve the visual look we were looking for.• Graphical programming in JAVA is relatively complicated.

Development

HTMLWrapper

JavaScriptInterface

Flex/AS3 FrontendGUI

Java BackendData Structure, Logic and AI

Architecture

Data Structure• The board is based on

“Fivlets” (“Winning Fives”).

• These are sets of indices that form a winning row, column or diagonal.

• The board statically stores all the 32 Fivlets in the game.

Example for Row Fivlets

Data Structure• Each Fivlet knows the status of

the 5 slots it contains.

• This structure allows to perform various calculations faster than other methods.

• Moves are easy to perform (bounded by number of Fivlets the modified slots are in).

Example for Column and Diagonal Fivlets

• Iterate over all Fivlets [O(1)] in order to check for winning conditions and base the heuristics upon their state.

AI and Heuristics• Automatic players in Fivel use α&β pruning algorithm to

search for an optimal move.

• The depth of the search is determined by the user when choosing difficulty through the GUI. We supply 4 different levels of difficulty:

Beginner – Depth 2 Experienced – Depth 3.Tough – Depth 4 Godlike – Depth 5.

AI and Heuristics• As the search algorithm deepens, Move objects are generated

and performed on the data structure. Each Move objects has an Undo method in order to restore the board.

• These operations are low-cost due to the use of Fivlets.

• Although moves in Fivel consist actually of two different operations (piece placement and then tile sliding), they are considered as a single move in the game logic.

AI and Heuristics• When a terminal board node is reached (either a board at an

end state or when the algorithm reached its designated depth) it is rated according to the following algorithm:

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• Basically, the algorithm iterates over all Fivlets and rates each

one. Eventually it sums up all the scores and returns it as the board’s heuristic score.

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• The algorithm counts how many pieces the current player and

his opponent placed in each Fivlet. Empty (no piece) or Void (no tile) slots are not counted and treated the same.

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• The Fivlet score is now calculated according to various cases: if

The current player has a full Fivlet, the Fivlet is rated with a high score.

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• Similarly, if the opponent of the current player has a full Fivlet,

the Fivlet is rated with a negative high score.

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• If a player has pieces in a Fivlet without an interference from

his opponent, the Fivlet is exponentially scored based on the number of pieces in that Fivlet.

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• Why BIG_CONST and not INFINITY?

If more than one Fivlet is full (which is obviously better than one), the board’s score will still be INFINITY (INF + INF = INF).

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI and Heuristics• M and N? M is greater than N since playing aggressively

(striving for full Fivlets) has better priority than playing defensively (stopping the opponent from achieving full Fivlets).

For each Fivlet in BoardFor each Slot in Fivlet

If Slot is Mine then mine += 1Else if Slot is Opponent then opp += 1

If mine == 5 then score = BIG_CONSTElse if opp == 5 then score = -BIG_CONSTElse if mine == 0 and opp > 0 then score -= -opp^NElse if opp == 0 and mine > 0 then score += mine^M

Return score

AI Analysis

Average Game Length(Mutual Turns)

Results Number of Games Tested

Match Type

20 Victories 50%Draw 0%

20 Human VS.

Human

20 Human won 40%CPU won 60%

Draw 0%

20 Human VS.

CPU (Beginner)

20 Human won 40%CPU won 60%

Draw 0%

20 Human VS.

CPU (Experienced)

20 Human won 40%CPU won 60%

Draw 0%

20 Human VS.

CPU (Tough)

20 Human won 40%CPU won 60%

Draw 0%

20 Human VS.

CPU (Godlike)

Human

AI Analysis

Average Game Length(Mutual Turns)

Results Number of Games Tested

Match Type

20 Victories %100Draw 0%

20 CPU (Beginner)VS.

CPU (Beginner)

20 Weaker CPU won 40%Stronger CPU won 60%

Draw 0%

20 CPU (Beginner)VS.

CPU (Experienced)

20 Weaker CPU won 40%Stronger CPU won 60%

Draw 0%

20 CPU (Beginner) VS.

CPU (Tough)

20 Weaker CPU won 40%Stronger CPU won 60%

Draw 0%

20 CPU (Beginner)VS.

CPU (Godlike)

Beginner

AI Analysis

Average Game Length(Mutual Turns)

Results Number of Games Tested

Match Type

20 Victories 100%Draw 0%

20 CPU (Experienced)VS.

CPU (Experienced)

20 Weaker CPU won 40%Stronger CPU won 60%

Draw 0%

20 CPU (Experienced) VS.

CPU (Tough)

20 Weaker CPU won 40%Stronger CPU won 60%

Draw 0%

20 CPU (Experienced)VS.

CPU (Godlike)

Experienced

AI Analysis

Average Game Length(Mutual Turns)

Results Number of Games Tested

Match Type

20 Victories 100%Draw 0%

20 CPU (Tough) VS.

CPU (Tough)

20 Weaker CPU won 40%Stronger CPU won 60%

Draw 0%

20 CPU (Tough)VS.

CPU (Godlike)

Tough

AI Analysis

Average Game Length(Mutual Turns)

Results Number of Games Tested

Match Type

20 Victories 100%Draw 0%

20 CPU (Godlike)VS.

CPU (Godlike)

Godlike

Enjoy the Game!

woot?