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Archive for the ‘Historical Perspective of AI’ Category

Artificial Intelligence in Modern Military Games @ GameTech 2012

Friday, March 23rd, 2012

I will be MCing the “Artificial Intelligence in Modern Military Games” panel at GameTech 2012 next week (March 29, 2012) in Orlando. I am extremely honored to be joined on this panel by:

Dr. Scott Neal Reilly is currently Principal Scientist and Vice President of the Decision Management Systems Division at Charles River Analytics, an artificial intelligence R&D company in Cambridge, MA. Dr. Neal Reilly’s research focuses on modeling emotional and social behavior in artificial agents and he was Principal Investigator for the US Army’s Culturally Aware Agents for Training Environments (CAATE) program, which focused on developing easy-to-use tools for creating interactive, intelligent, social agents.  Dr. Neal Reilly has a Ph.D. in Computer Science from Carnegie Mellon University, where he developed the Em system to model emotions in broadly capable intelligent agents. Before joining Charles River, Dr. Neal Reilly was Vice President of Production and Lead Character Builder at Zoesis Studios, which developed advanced artificial intelligence techniques for creating animated, artificially intelligent agents.

 

James Korris is CEO and President of Creative Technologies Incorporated (CTI).  CTI, named as one of Military Training Technology’s 2011 Top 100 Simulation and Training companies, is at the forefront of immersive, cognitive simulation development for government and industry.  Recent work includes one of the first DoD augmented virtuality (AV) implementations, the Call For Fire Trainer – AV, along with novel mobile applications for the Fort Sill Fires Center of Excellence.  Korris is currently leading a CTI effort supporting the SAIC Sirius team with a desktop application to mitigate analyst cognitive bias for IARPA.

From its establishment in 1999 until October 2006, Korris served as Creative Director of the U.S. Army-funded Institute for Creative Technologies at the University of Southern California.  In this pioneering “serious gaming” environment, Korris led the development of Full Spectrum Warrior, the first military application developed for the Xbox, along with desktop applications Full Spectrum Command and Full Spectrum Leader.  Korris’ team captured the DoD 2006 Modeling & Simulation award for training with Every Soldier A Sensor Simulation.  In 2007, USJFCOM recognized another Korris-led effort, the Joint Fires & Effects Trainer System as the highest-rated Close Air Support simulation trainer in the world.  In 2008, Korris was appointed to the Naval Research Advisory Committee, advising the Secretary of the Navy on its research portfolio.  Korris came to the defense industry following work in Hollywood studio production, producing and writing.  He is a member of the writers’ branch of the Academy of Television Arts and Sciences, the Writers Guild of America, the Writers Guild of Canada and the Society of Motion Picture and Television Engineers.  His work was recognized in the 2006 Smithsonian Cooper-Hewitt National Design Triennial, Saul Wurman’s eg2006 conference and as a Visionary in Bruce Mau’s Massive Change exhibition.  Korris earned a BA from Yale University and an MBA with distinction at the Harvard Business School.”

 

Dr. Michael van Lent received a PhD at the University of Michigan in 2000. His expertise is in applying cognitive science approaches to military problems. Dr. van Lent is a recognized expert in the development of advanced simulation systems for military training. He has participated in the design and development of many immersive training applications including Full Spectrum Warrior, Full Spectrum Command, the Joint Fires and Effects Trainer System (JFETS), ELECT BiLAT, UrbanSim, Helping our Heroes and the Strategic Social Interaction Modules program.

 

Robert Franceschini is a vice president and Technical Fellow at Science Applications International Corporation (SAIC). He directs the Modeling and Simulation Center of Expertise, an organization that spans SAIC’s modeling and simulation capabilities.    Prior to SAIC, Dr. Franceschini held academic and research positions at the University of Central Florida (UCF) and its Institute for Simulation and Training.  He plays an active role in science, technology, engineering, and mathematics programs in central Florida.  He received both a BS and a Ph.D. in computer science at UCF.

 

I’m looking forward to meeting you in Orlando!

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Bad Game AI / Good Game AI (Part 1)

Thursday, June 9th, 2011

Most game AI is bad AI. Let’s be honest; it’s not just bad, it sucks.

I’ve been writing and playing computer games since the early 1980s and I haven’t seen even a modest improvement in the quality of computer opponents. There are a few notable exceptions – and we’ll get to them shortly – but, the vast majority of commercial games that are released were developed with little thought, or budget, given to AI.

So, since it’s such a short list, let’s start with a few computer games that have good AI:

Computer Chess. Any computer chess program that is available today, including ‘freebie’ online Java applets will kick your ass. Back in the ‘70s I had an ‘electronic chess game’ that played as well as I did (I was about a 1600 level player at the time). The game had various levels of AI; but all that changed was how much time the machine was given to make a move. If you put it on the top level it would take forever contemplating the all the responses to the opening P-K4.

So, why was chess AI pretty good thirty-five years ago and even better now? There are a couple of reasons, the first being that chess can be divided into three ‘phases’: the opening, the middle and the endgame. Chess openings are very well understood and there are number of ‘standard’ texts on the subject such as Batsford Chess Openings Volume 1 and 2. These chess openings are available in various file formats and are easily integrated into a chess engine. So, until the program is ‘out of book’ the most important moves, the opening moves, are expertly played by the program without any AI at all. There are also books for endgame positions. So, really, the only difficult area for chess programs is the middlegame.

1st Chess problem solved by computer

1st Chess problem solved by computer by Dr. Dietrich Prinz with the Manchester Mark 1 in 1951 (White to mate in two. The solution is: R - R6, PxR. P - N7 Mate.)

There are dozens of very good articles, papers and books on evaluating chess positions using heuristic evaluation function. Here’s a pretty good page on the subject, even though it looks like all the picture links are broken: http://www.cs.cornell.edu/boom/2004sp/ProjectArch/Chess/algorithms.html ). And here’s a link to a series on building a chess engine: http://www.gamedev.net/page/resources/_/reference/programming/artificial-intelligence/gaming/c

hess-programming-part-vi-evaluation-functions-r1208 .

Chess was one of the first games to be implemented on computers. The first chess problem solved by a computer see picture) was done by Dr. Dietrich Prinz with the Manchester Mark 1 in 1951 (see picture, right).

Though I could be wrong, I think Dr. Prinz’s program simply employed brute force to solve the problem.

So, why is it comparatively easy to find/write good chess AI? Opening and endgame databases are readily available, evaluation functions for board positions are well understood and (I suspect I’ll get some flak for saying this) it’s a relatively easy game (at least to program, not to master). Also, there are not a lot of pieces, their moves are restricted, the rules of the game are simple and the board size is fixed.

Chris Crawford’s Patton vs. Rommel. Crawford’s Patton vs. Rommel was a wargame that came out in 1987. On the PC (remember this was before Windows) it ran in 640kb (and that included the operating system). The display was 640 x 200 x 2, if I remember correctly (see screen shot).

Chris Crawford's Patton vs. Rommel

Chris Crawford's Patton vs. Rommel (1987)

I haven’t played the game in over 20 years, but I remember being very impressed by the AI, specifically how the program had a ‘feel’ for the tactical situation. A very important part of the game was the ‘road net’. Units moved much faster on roads and it was easy to get your units caught up in traffic jams. When that happened the AI would warn the user. This really shocked me when I first played the game. Chris employed what he called ‘geometric AI’ in Patton vs. Rommel. He goes into more details in his book, “Chris Crawford on Game Design,” (http://www.amazon.com/Chris-Crawford-Game-Design/dp/0131460994).

 

There are plenty of great games out there, but that’s not what this post is about. The question is what games have good AI? I’m going to need to think about this and see if I can add some more titles to the ‘good AI’ list, because I sure have a ton for the ‘bad AI’ list.

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Where’s the Artificial Intelligence we were promised?

Thursday, July 15th, 2010

Way back at the end of the last century we were all led to believe that wonderful ass-kicking AI would be standard issue in every computer game, office application and multibillion dollar DoD wargame. When we were writing these games in the 1980s and ’90s we were still dealing with limitations on available RAM and storage space (games shipped and ran on 3.5″ disks; early hard drives were awfully small). Still, what we did back in the ‘old days’ of computer games was, frankly, at least as good as what is shipping today.

I’ve written a lot of computer games and I’ve played even more. I would be very hard pressed to name a current game that has ‘pretty good’ AI. Based on the reviews I’ve read of most computer games, I’m not alone. AI, even just ‘acceptable’ AI, continues to be the biggest problem facing commercial computer games today.

On the ‘professional’ side large, expensive wargames (and here I can’t get into many details if I ever want to work in the industry again) had no AI whatsoever until fairly recently. For now, I’ll just say the results aren’t commensurate with the truck-loads of money that are being dumped on the problem.

My old friend, Mike Morton, urged me to start writing a blog about AI and this is the result. If there are any complaints about the blog, please write to me and I will send you Mike’s email address. He works for Google and probably has plenty of time to answer email.

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