Archive for June 8th, 2007

8 Jun

Emotionally Driven Natural Language Generation for Personality Rich Characters in Interactive Games

Posted by cognitivecomputing in Agents, Game AI, Language. Tagged: , , , , .

Natural Language Generation for personality rich characters represents one of the important directions for believable agents research. The typical approach to interactive NLG is to hand-author textual responses to different situations. In this paper we address NLG for interactive games. Specifically, we present a novel template-based system that provides two distinct advantages over existing systems. First, our system not only works for dialogue, but enables a character’s personality and emotional state to influence the feel of the utterance. Second, our templates are resuable across characters, thus decreasing the burden on the game author. We briefly describe our system and present results of a preliminary evaluation study.

Read the paper:

Emotionally Driven Natural Language Generation for Personality Rich Characters in Interactive Games

by Christina Strong, Kinshuk Mishra, Manish Mehta, Alistair Jones, Ashwin Ram

Third Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), Stanford, CA, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-09.pdf

8 Jun

Evaluating Player Modeling for a Drama Manager Based Interactive Fiction

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

A growing research community is working towards employing drama management components in story-based games that guide the story towards specific narrative arcs depending on a particular player’s playing patterns. Intuitively, player modeling should be a key component for Drama Manager (DM) based approaches to succeed with human players.

In this paper, we report a particular implementation of the DM component connected to an interactive story game, Anchorhead, while specifically focusing on the player modeling component. We analyze results from our evaluation study and show that similarity in the trace of DM decisions in previous games can be used to predict interestingness of game events for the current player. Results from our current analysis indicate that the average time spent in performing player actions provides a strong distinction between players with varying degrees of gaming experience, thereby helping the DM to adapt its strategy based on this information.

Read the paper:

Evaluating Player Modeling for a Drama Manager Based Interactive Fiction

by Manu Sharma, Manish Mehta, Santi Ontañón, Ashwin Ram

Third Conference on Artificial Intelligence for Interactive Digital Entertainment (AIIDE-07), Workshop on Player Satisfaction, Stanford, CA, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-08.pdf