Archive for February, 2011

25 Feb

Case-Based Reasoning and User-Generated AI for Real-Time Strategy Games

Posted by cognitivecomputing in Game AI, Learning. Tagged: , , , .

Creating AI for complex computer games requires a great deal of technical knowledge as well as engineering effort on the part of game developers. This paper focuses on techniques that enable end-users to create AI for games without requiring technical knowledge by using case-based reasoning techniques.

AI creation for computer games typically involves two steps: a) generating a first version of the AI, and b) debugging and adapting it via experimentation. We will use the domain of real-time strategy games to illustrate how case-based reasoning can address both steps.

Read the paper:

Case-Based Reasoning and User-Generated AI for Real-Time Strategy Games

by Santi Ontañón and Ashwin Ram

In P. Gonzáles-Calero & M. Gomez-Martín (ed.), AI for Games: State of the Practice, 2011.
www.cc.gatech.edu/faculty/ashwin/papers/er-11-02.pdf

24 Feb

Intentional analysis of medical conversations for community engagement

Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: , , .

With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user-generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels.

In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.

Read the paper:

Intentional analysis of medical conversations for community engagement

by Saurav Sahay, Hua Ai, Ashwin Ram

FLAIRS-11 International Conference on Artificial Intelligence
www.cc.gatech.edu/faculty/ashwin/papers/er-11-01.pdf

24 Feb

Learning Opponent Strategies through First Order Induction

Posted by cognitivecomputing in Agents, Game AI, Learning.

In a competitive game it is important to identify the opponent’s strategy as quickly and accurately as possible so that an effective response can be staged. In this vein, this paper summarizes our work in exploring the use of the first order inductive learning (FOIL) algorithm for learning rules which can be used to represent opponent strategies. Specifically, we use these learned rules to perform plan recognition and classify an opponent strategy as one of multiple learned strategies. Our experiments validate this novel approach in a simple real-time strategy game.

Read the paper:

Learning Opponent Strategies through First Order Induction

by Kathryn Genter, Santiago Ontañón, Ashwin Ram

FLAIRS-11 International Conference on Artificial Intelligence
www.cc.gatech.edu/faculty/ashwin/papers/er-11-03.pdf