PUBLIC MARKS with tags games & ai

qasimodo asks: "I was recently reading a preview of Splinter Cell: Pandora Tomorrow, and then I came across this article at GameSpot saying Pandora Tomorrow will feature adaptive AI which 'will adjust itself to players' skill level'. I remember (and is also mentioned in the PT article) Max Payne also featured this, but I never noticed it. I guess that's the best way to know if it works, since it adapts to your gaming skills, but does it really work? Have you noticed it? Do you have proof of it?"

I’ve been steadily plugging away at my list of bugs and enhancements for the upcoming release and finally tackled one area that I had been avoiding: improving the AI of some of the bugs.

The aim of the 2nd annual North American Game-On Conference (GameOn'NA 2006) is to bring together academics, researchers and games people from North America in order to exchange ideas on higher-level concepts that contribute to the field of computer gaming research.

However though AI Planning has much to contribute to both these fields, particularly in producing more convincing Non-Player Characters and autonomous intelligent characters, few AI planning researchers have been involved in this work, and the technology, where applied at all, has often been used in a somewhat ad hoc way. In addition, games company use of AI planning has so far been limited - A*-based motion planning the main exception - with practitioners feeling that the technology is too computationally expensive or risky for integration into computer games.

Much of the work in game AI has focused on the ‘big' problems: path planning, squad planning, goal-directed behavior, etc. The result is characters that are capable of increasingly intelligent behavior. However, acting intelligently and acting aware and sentient is not the same thing. But if we are to create the kind of compelling and emotional characters upon which the next generation of computer games will be based, we must solve the latter problem, namely how to build characters that seem aware and sentient.

Black and White 2 AI I played Black and White 2 for many hours yesterday. The computer player and I were in a stalemate. The computer kept sending armies against me and I kept defeating them. I had built my town with walls around it, and then put archers on top of the walls. I was building up my strength while defending myself, in preparation for a big attack. I felt pretty safe. After around 40 attacks, I realized that they weren't all the same. The computer wasn't using the same attackers each time. It tried the creature, archers, swordsmen, and catapults. It tried combinations of them. Sometimes it would come through my main entrance, and sometimes it would come around the back entrance to the city. The computer player also destroyed major sections of the city using the “earthquake” power, but I recovered from these too. After a while the enemy creature figured out that he should kick my wall in. His archers and swordsmen stayed back, out of range, while the creature came up and destroyed my wall, including the archers on it. After it breached the wall, the army swarmed into my town and killed half my people. I rebuilt my wall and started to recover, but the computer's newly discovered strategy worked well. It tried several variants but kept going back to the same approach: kick down the wall, then swarm the town. This forced me to try some new strategies. Although being on the wall has advantages, it leaves the archers vulnerable when the enemy creature attacks the wall. So I moved them behind the wall. I've also learned to open my gate, wait for the enemy army to get close, then close the gate and set their army on fire. I have no good strategy for the creature knocking down my wall though, and I'm constantly losing townspeople and then rebuilding. After a long stalemate, the computer AI learned how to attack more effectively, and now I'm having trouble keeping my city safe. I'm very impressed by the AI. I'm not sure how it's programmed, but it tried out many different things and learned which ones work the best. From the game AI techniques I've learned (genetic algorithms, neural networks, fuzzy logic, state machines, etc.), the AI in Black and White 2 seems to match most closely with what I know about reinforcement learning. It's a technique that uses online learning (observing results as the game is played) instead of training (from examples constructed ahead of time), allows both exploration (trying new things in order to learn) and exploitation (taking advantage of what you've learned), and associates rewards (like whether the attack was successful) with actions (like kicking down the wall and keeping the army away from my archers). I recommend Sutton and Barto's book if you want to learn more. It's entirely possible though that the game uses something much simpler that just happens to look impressive, but my guess is that it's using reinforcement learning. — Amit — Monday, December 12, 2005 Comments: Post a Comment Links to this post:

AI很高的黑白棋

AI很高的黑白棋

There is an increasing interest in the computer games industry in the development of games with emotionally compelling interactive storylines. Games designers, screen writers and narrative theorists propose contrasting approaches to engineering satisfying stories in which players can participate. This symposium focuses on the application of artificial intelligence techniques, frameworks and theories to the creation of interactive narrative in game worlds. It will address questions such as: how can we engineer believable story characters which can interact with players in an emotionally convincing way; how can we design interactive stories in which the player’s experience is central; how can we scale up prototype interactive narrative architectures to meet the requirements of today’s game engines; and what are the applications of narrative games in other domains such as education or health? Themes running throughout the symposium will be: the extent to which games engines can be used as research tools and appropriate methods for disseminating and sharing prototype systems throughout the community.

NERO is the result of a joint project between the Digital Media Collaboratory (DMC) and the neuroevolution group at the Department of Computer Sciences at the University of Texas at Austin (UTCS). NERO utilizes state-of-the-art academic artificial intelligence research in order to demonstrate its effectiveness in a game environment. The game uses AI to allow simulated robotic agents to cope with changing environments and situations, and form adaptive tactical solutions. The end result is a game that adapts itself to the strategies desired by the player, while still allowing the AI-controlled entities to operate as autonomous agents. NERO introduces a new genre of video game that is only possible with machine learning technology: The player takes the role of trainer and teaches a team of novice soldiers the skills for battle. When sufficient skills have been acquired, players can take their teams to battle mode where their skills are tested against teams trained by other players.