Archive for May 8th, 2007

Artificial Intelligence for Adaptive Computer Games

Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also so-called “humane” gaming applications for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of going beyond scripted interactions, however complex, into the arena of truly interactive systems that are responsive, adaptive, and intelligent. Such systems learn about the player(s) during game play, adapt their own behaviors beyond the pre-programmed set provided by the game author, and interactively develop and provide a richer experience to the player(s).

The long-term goal of our research is to develop artificial intelligence techniques that can have a significant impact in the game industry. We present a list of challenges and research opportunities in developing techniques that can be used by computer game developers. We discuss three Case Based Reasoning (CBR) approaches to achieve adaptability in games: automatic behavior adaptation for believable characters; drama management and user modeling for interactive stories; and strategic behavior planning for real-time strategy games.

Read the paper:

Artificial Intelligence for Adaptive Computer Games

by Ashwin Ram, Santi Ontañón, Manish Mehta

Invited talk at the 20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Special Track on Case-Based Reasoning, Key West, FL, May 2007

Detecting Medical Rule Sentences with Semi-Automatically Derived Patterns: A Pilot Study

We propose a semi-supervised method to extract rule sentences from medical abstracts. Medical rules are sentences that give interesting and non-trivial relationship between medical entities. Mining such medical rules is important since the rules thus extracted can be used as inputs to an expert system or in many more other ways. The technique we suggest is based on paraphrasing a set of seed sentences and populating a pattern dictionary of paraphrases of rules. We match the patterns against the new abstract and rank the sentences.

Read the paper:

Detecting Medical Rule Sentences with Semi-Automatically Derived Patterns: A Pilot Study

by Shreekanth Karvaje, Bharat Ravisekar, Baoli Li, Ernie Garcia, Ashwin Ram

International Symposium on Bioinformatics Research and Applications ( ISBRA-07), Atlanta, GA, May 2007