Contents

Playing Trickle Down

About The Programmers

Developing Games That Learn


C Help Application
Playing Trickle Down

The defining feature

At the advanced level of play, Trickle Down learns from both its losses and wins. The learning is generalized to many types of game board piece configurations. You can see the learning in action by following this simple procedure:

 Beat Trickle Down at the  Advanced level of play

 Select the  Replay option; replay your moves by following the loud red dot.

When made, the learned move will be clearly identified by the appearance of a message box; in addition the piece placed by the learned move will be denoted with a small white dot at its center.

The game board

Trickle Down is played on a board composed of an 8 x 8 matrix. The tradition four-in-a-row genre game is played on a 6 x 7 matrix. It is widely thought that when playing on the 6 x 7 matrix the first player will always win when play is perfect. It is not clear to the authors of this game that there is any decisive advantage to moving first on the 8 x 8 game board.

Starting a new game

You begin a game by selecting the New item under the Game menu option. In addition you can begin a new game with a single click of the right mouse button. This option can be disabled by selecting the Right button….. item in the Options menu option.

Playing the game

You make a move my maneuvering the mouse pointer over a game board location which contains a small dot and clicking once with the left mouse button. Baxter, the computers persona, will quickly follow with his/her selection.

Winning the game

The  first player who arranges four pieces in a vertical, horizontal or diagonal row wins.

About Trickle Down

The learning algorithms which propel Baxters ability to display learning were developed using the Objective Artificial Intelligence paradigm. The authors developed the learning algorithms in the spirit of adventure and fun. The games designers are always interested in hearing comments from players. You can reach us at:

ldorfman@li.net

If you wish to learn more about Trickle Downs learning algorithms, you can find a description of the Objective Artificial Intelligence paradigm and the learning algorithms in a book titled:

Developing Games That Learn by Len Dorfman and Narendra Ghosh. It is published by Manning / Prentice-Hall.

Prentice-Hall ISBN: 0-13-569617-8

The book is available at bookstores and on the web at:

http://www.browsebooks.com/

The platform-independent source code to the move generation engine includes a detailed description of three learning algorithms is presented in the book. We have done our best to explain the paradigm and learning algorithms with the hope that programmers will be able to use them in a wide variety of game applications.

The OWL source used to create the Win16 and Win32 versions of Trickle Down is presented in a book titled:

Borland C++ 5.0: Migrating From C to C++  by Len Dorfman.

It will be published by Manning / Prentice-Hall and should be available in the summer/fall of 1996.

The tool bar

 Start a new game

 Undo the last two moves in the current game

 Replay a game you have just won to see learning (only Advanced level )

 Open an existing game record file ( not enabled in this version )

 Save a record of the current game ( not enabled in this version )

 Print a games list of moves ( not enabled in this version)

 Info about the game designers and programmers

 Help Trickle Downs help options

About The Programmers

Developing Games That Learn


Menu Item A
About The Programmers

Len Dorfman, Ph.D.

Len is a teacher, writer, programmer and author of many computer science titles. He spends considerable time watching his breath.

Narendra Ghosh

Narendra is currently a student at Harvard University majoring in Computer Science.

Playing Trickle Down

Developing Games That Learn


Menu  Item B
Developing Games That Learn

If you wish to  more about Trickle Downs learning algorithms, you can find a description of the Objective Artificial Intelligence paradigm and the learning algorithms in a book titled:

Developing Games That Learn by Len Dorfman and Narendra Ghosh.

It is published by Manning / Prentice-Hall.

The platform-independent source code to the move generation engine includes a detailed description of three learning algorithms is presented in the book. We have done our best to explain the paradigm and learning algorithms with the hope that programmers will be able to use them in a wide variety of game applications.

The OWL source used to create the Win16 and Win32 versions of Trickle Down is presented in a book titled:

Borland C++ 5.0: Migrating From C to C++ by Len Dorfman.

It is published by Manning / Prentice-Hall and should be available in the summer/fall of 1996.

Playing Trickle Down

About The Programmers