Are you an intelligent mouse? Play the game to find out!

This was an game I help developed for one of my internship courses run by Guiang Corporation. The game targeted platform is Flash compatible Smart TVs. You play yourself as a mouse and escape the maze before time runs out. Answer any questions correctly to get points and hints. You can also collect cheeses in the maze for points.

Update: I no longer work on this project since my employment at Parasoft. The game will be developed by some remaining and new developers. Initially the game was made for Samsung TV, but the latter developers realized the potential of becoming a web app.

Check out intelligentmouse.com for more information on this game.

"The sun rises over the mountains...bringing forth a brand new day."

This was an artwork I worked on over last year. I wanted to create a new painting for the New Year of 2010 and this piece symbolizes it. I used Photoshop CS4 and painted this piece by using my personal pen tablet. Special thanks to www.cgpad.org for the community custom brushes.

Deviant Art [link]

Hello there, my name is Legocpp!

An classwork for my robotics class. This was an experimentation on robot steering and obstacle avoidance. My partner and I used Lejos for coding and the Lego Mindstorm unit was provided by our school. Lejos is an Java API we used.

Here is the main robot loop:

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while(nav.isMoving())
{
	if(Button.ESCAPE.isPressed()) // if we press button to terminate early
	{
		nav.stop();
		return;
	}
 
	boolean crashed = touch.isPressed();
	if(detectObstacle() || crashed)
	{
		Thread.sleep(50); // wait 50ms for sonar update
		if(obstacleAvoid(crashed, error)) // if we avoided the obstacle
		{
			if(goaround()) // if we successfully "gone around"
			{
				nav.goTo(0, 60, true); // continue to goal
				error = 0;
			}
		}
		else error+=2;
	}
}

Disadvantages

We used an dead reckoning behavior to keep track of the current position. Even though it wasn’t as effective, for example: If the robot was stuck and the wheels are moving, then the robot’s position is still updated.

The main goal of our project was to make the robot avoid obstacle. As long as the robot does move behind an obstacle, it is consistent with our goal.

Strengths

The robot was able to decide how far to travel before we should check if we are clear of the obstacle. This was handled by calculating how many times it turned back and found an obstacle is still there.

Video and Blog

You can view the video demonstration and more information on our Robot Design Blog [link].

Code vs Output

This was one of my projects I’ve completed for an AI class I took during my college term. The project goal is to implement an AI game using one of the key algorithms we learned, Alpha-beta pruning. The game idea is simple: The player is to play against an AI. The win factor is that the player should have 4 pieces consecutively in a row or column. The programming language used was  Python 3.1.

What is Alpha-beta pruning?

Read an clear explanation on Wikipedia, our implementation follows it directly. Here is the implementation for my code:

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def alphabeta(problem, alpha, beta, MaxPlayer, limit):
	global starttime
	if (time.clock() - starttime) >= limit or problem[0].isfull(): return problem
	if MaxPlayer: return maxstate(problem[0], alpha, beta, MaxPlayer)
	else: return minstate(problem[0], alpha, beta, MaxPlayer)
 
def maxstate(problem, alpha, beta, MaxPlayer):
	for child in problem.getsuccessors('X'):
		clone = copy.deepcopy(problem)
		clone.move(child[1][0], child[1][1], 'X')
		node = (clone, child[1])
		alpha = max(alpha, alphabeta(node, alpha, beta, not MaxPlayer, limit))
		if beta[0].h <= alpha[0].h: break;
	return alpha
 
def minstate(problem, alpha, beta, MaxPlayer):
	for child in problem.getsuccessors('O'):
		clone = copy.deepcopy(problem)
		clone.move(child[1][0], child[1][1], 'O')
		node = (clone, child[1])
		beta = min(beta, alphabeta(node, alpha, beta, not MaxPlayer, limit))
		if beta[0].h <= alpha[0].h: break;
	return beta

How the AI chooses the best possible move:

I will give a very general explanation. Please read the Wikipedia link for a elaborate explanation.

1.) Given an player’s move, the AI will generate a minimax tree till the search limit time is reached. In other words: Predict player’s next move and then find my best move. Iterate this n-levels deep.

2.) For each move the AI predicts, give it an score. The evaluation function:

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def getsum(self, string):
	sum = 0
	superbad = -5000
	bad1 = -100
	bad2 = -75
	bad3 = -50
	bad4 = -25
	bad5 = -1
	supergood = 5000
	good1 = 100
	good2 = 75
	good3 = 50
	good4 = 25
	good5 = 1
	#opponent
	if 'OOOO' in string: sum += superbad
	if '-OOO-' in string: sum += bad1
	if 'OOO-' in string: sum += bad1
	if '-OOO' in string: sum += bad1
	if 'OO-O' in string: sum += bad2
	if 'O-OO' in string: sum += bad2
	if 'O-OO-' in string: sum += bad2
	if '-OO-O' in string: sum += bad2
	if '-O-OO' in string: sum += bad2
	if 'OO-O-' in string: sum += bad2
	if '-OO-' in string: sum += bad2
	if '-OO' in string: sum += bad3
	if 'OO-' in string: sum += bad3
	if 'O-O-' in string: sum += bad4
	if '-O-O' in string: sum += bad4
	if '-O-O-' in string: sum += bad2
	if 'O-O' in string: sum += bad5
	if 'OO' in string: sum += bad5
	if '-O-' in string: sum += bad5
        .....

3.) The AI will consider all the player’s moves that are against itself and find its worse case.

4.) The AI evaluates itself to find it’s best move to counter the player’s move.

Disadvantage:

Our Design: Too many combinations of player’s moves to evaluate, therefore AI may not make the best move in our consideration.

Strength:

Python’s fast sorting. Alpha-beta pruning which cuts off any worse-off moves from consideration.

Try it out!

An python installation is required. Please make sure you have already installed on your computer, Python 3.1+.

connect4.py [Download]

Download the above file and open it. Enter an time you want the AI to search for an solution. For example, enter 5 for 5 seconds.