Posts Tagged ‘games’

9 May

Towards Player Preference Modeling for Drama Management in Interactive Stories

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

There is a growing interest in producing story based game experiences that do not follow fixed scripts pre-defined by the author, but change the experience based on actions performed by the player during his interaction. In order to achieve this objective, previous approaches have employed a drama management component that produces a narratively pleasing arc based on an author specified aesthetic value of a story, ignoring a player’s personal preference for that story path. Furthermore, previous approaches have used a simulated player model to assess their approach, ignoring real human players interacting with the story-based game.

This paper presents an approach that uses a case-based player preference modeling component that predicts an interestingness value for a particular plot point within the story. These interestingness values are based on real human players’ interactions with the story. We also present a drama manager that uses a search process (based on the expectimax algorithm) and combines the author specified aesthetic values with the player model.

Read the paper:

Towards Player Preference Modeling for Drama Management in Interactive Stories

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

20th International FLAIRS Conference on Artificial Intelligence (FLAIRS-07), Key West, FL, May 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-03.pdf

8 May

Artificial Intelligence for Adaptive Computer Games

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

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
www.cc.gatech.edu/faculty/ashwin/papers/er-07-04.pdf

11 Jan

Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL

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

The goal of transfer learning is to use the knowledge acquired in a set of source tasks to improve performance in a related but previously unseen target task. In this paper, we present a multi-layered architecture named CAse-Based Reinforcement Learner (CARL). It uses a novel combination of Case-Based Reasoning (CBR) and Reinforcement Learning (RL) to achieve transfer while playing against the Game AI across a variety of scenarios in MadRTS, a commercial Real-Time Strategy game. Our experiments demonstrate that CARL not only performs well on individual tasks but also exhibits significant performance gains when allowed to transfer knowledge from previous tasks.

Read the paper:

Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL

by Manu Sharma, Michael Holmes, Juan Santamaria, Arya Irani, Charles Isbell, Ashwin Ram

International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-01.pdf

8 Jan

Towards Runtime Behavior Adaptation for Embodied Characters

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

Typically, autonomous believable agents are implemented using static, hand-authored reactive behaviors or scripts. This hand-authoring allows designers to craft expressive behavior for characters, but can lead to excessive authorial burden, as well as result in characters that are brittle to changing world dynamics.

In this paper we present an approach for the runtime adaptation of reactive behaviors for autonomous believable characters. Extending transformational planning, our system allows autonomous characters to monitor and reason about their behavior execution, and to use this reasoning to dynamically rewrite their behaviors. In our evaluation, we transplant two characters in a sample tag game from the original world they were written for into a different one, resulting in behavior that violates the author intended personality. The reasoning layer successfully adapts the character’s behaviors so as to bring its long-term behavior back into agreement with its personality.

Towards Runtime Behavior Adaptation for Embodied Characters

by Peng Zang, Manish Mehta, Michael Mateas, Ashwin Ram

International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, January 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-02.pdf

25 Jul

Case-Based Planning to Learn

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

Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework.

Read the paper:

Case-Based Planning to Learn

by Bill Murdock, Gordon Shippey, Ashwin Ram

2nd International Conference on Case-Based Reasoning (ICCBR-97), Providence, RI, July 1997
www.cc.gatech.edu/faculty/ashwin/papers/er-97-04.pdf
Newer Entries »