Posts Tagged ‘interactive drama’

21 Jul

Authoring Behaviors for Games using Learning from Demonstration

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

Behavior authoring for computer games involves writing behaviors in a programming language. This method is cumbersome and requires a lot of programming effort to author the behavior sets. Further, this approach restricts the behavior set authoring to people who are experts in programming.

This paper describes our approach to design a system that allows a user to demonstrate behaviors to the system, which the system uses to learn behavior sets for a game domain. With learning from demonstration, we aim at removing the requirement that the user has to be an expert in programming, and only require him to be an expert in the game. The approach has been integrated in a easy-to-use visual interface and instantiated for two domains, a real-time strategy game and an interactive drama.

Read the paper:

Authoring Behaviors for Games using Learning from Demonstration

by Manish Mehta, Santiago Ontañón, Tom Amundsen, Ashwin Ram

ICCBR-09 Workshop on Case-Based Reasoning for Computer Games, Seattle, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-07.pdf

22 May

Emotional Memory and Adaptive Personalities

Posted by cognitivecomputing in Agents, Learning, Robotics. Tagged: , , , , .

Believable agents designed for long-term interaction with human users need to adapt to them in a way which appears emotionally plausible while maintaining a consistent personality. For short-term interactions in restricted environments, scripting and state machine techniques can create agents with emotion and personality, but these methods are labor intensive, hard to extend, and brittle in new environments. Fortunately, research in memory, emotion and personality in humans and animals points to a solution to this problem. Emotions focus an animal’s attention on things it needs to care about, and strong emotions trigger enhanced formation of memory, enabling the animal to adapt its emotional response to the objects and situations in its environment. In humans this process becomes reflective: emotional stress or frustration can trigger re-evaluating past behavior with respect to personal standards, which in turn can lead to setting new strategies or goals.

To aid the authoring of adaptive agents, we present an artificial intelligence model inspired by these psychological results in which an emotion model triggers case-based emotional preference learning and behavioral adaptation guided by personality models. Our tests of this model on robot pets and embodied characters show that emotional adaptation can extend the range and increase the behavioral sophistication of an agent without the need for authoring additional hand-crafted behaviors.

Read the paper:

Emotional Memory and Adaptive Personalities

by Anthony Francis, Manish Mehta, Ashwin Ram

Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, IGI Global, 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-08-10.pdf

11 May

Creating Behavior Authoring Environments for Everyday Users

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

The design of interactive experiences is increasingly important in our society. Examples include interactive media, computer games, and interactive portals. There is increasing interest in modes of interaction with virtual characters, as they represent a natural way for humans to interact. Creating such characters is a complex task, requiring both creative skills (to design personalities, emotions, gestures, behaviors) and programming skills (to code these in a scripting or programming language). There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments that can support the cognitive needs of everyday users and help them at every step to easily carry out this creative task.

Our research focuses on behavior authoring environments that not only make it easy for novices/everyday users to create characters but also provide them scaffolding in designing these interactive experiences. In this paper we present results from a user study with a paper prototype of an authoring environment that is aimed to allow everyday users to create virtual characters. The study aims at determining whether typical computer users are able to create character personalities in specific scenarios and think about characters’ mental states, and if so, then what kinds of user interfaces would be suitable for this authoring environment.

Read the paper:

Creating Behavior Authoring Environments for Everyday Users

by Manish Mehta, Christina Lacey, Iulian Radu, Abhishek Jain, Ashwin Ram

International Conference on Computer Games, Multimedia, and Allied Technologies (CGAT-09), Singapore, May 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-01.pdf

26 Nov

Developing a Drama Management Architecture for Interactive Fiction Games

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

A growing research community is working towards employing drama management components in interactive story-based games. These components gently guide the story towards a narrative arc that improves the player’s experience. In this paper we present our Drama Management architecture for real-time interactive story games that has been connected to a real graphical interactive story based on the Anchorhead game. We also report on the natural language understanding system that has been incorporated in the system and report on a user study with an implementation of our DM architecture.

Developing a Drama Management Architecture for Interactive Fiction Games

by Santi Ontañón, Abhishek Jain, Manish Mehta, Ashwin Ram

1st Joint International Conference on Interactive Digital Storytelling (ICIDS-08), Erfurt, Germany, November 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-11.pdf

3 Apr

Case-Based Reasoning for Game AI

Posted by cognitivecomputing in 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 applications, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are also what the so called “humane” gaming applications—interactive tools for medical training, educational games, and games that reflect social consciousness or advocate for a cause. Game AI is the effort of taking computer games 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).

In this talk, I discuss a range of CBR approaches for Game AI. I discuss differences and similarities between character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager or game director, for example). I explain why the AI must reason at multiple levels, including reactive, tactical, strategic, rhetorical, and meta, and propose a CBR architecture that lets us design and coordinate real-time AIs operating asynchronously at all these levels. I conclude with a brief discussion on the very idea of Game AI: is it feasible? realistic? and would we call it “intelligence” if we could implement all this stuff?

View the talk:

Google Tech Talk: Case-Based Reasoning for Game AI

by Ashwin Ram

Google Tech Talk, Mountain View, CA, April 2008
www.youtube.com/watch?v=s9G7DRTuB5s

10 Nov

Driving Interactive Drama Research through Building Complete Systems

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

Interactive drama presents one of the most challenging applications of autonomous characters, requiring characters to simultaneously engage in moment-by-moment personality-rich physical behavior, exhibit conversational competencies, and participate in a dynamically developing story arc. One way to advance the field and continue to make exciting progress is to develop building blocks needed for creating these interactive experiences that are situated in a complete system. Our research goals presented in this paper are driven by this perspective of developing a complete interactive drama architecture. Specifically, we discuss the different research challenges that we are interested in pursuing at the different building blocks required to build a complete interactive drama. We also discuss the interactive drama domain we are developing and present our initial steps in handling the research challenges.

Read the paper:

Driving Interactive Drama Research through Building Complete Systems

by Manish Mehta, Santi Ontañón, Ashwin Ram

AAAI-07 Spring Symposium on Intelligent Narrative Technologies, Arlington, VA, November 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-07-14.pdf

9 Nov

Drama Management Evaluation for Interactive Fiction Games

Posted by cognitivecomputing in Game AI. Tagged: , .

A growing research community is working towards employing drama management components in interactive story-based games. These components gently guide the story towards a narrative arc that improves the player’s experience. However, the success of drama management approaches has not been evaluated using human players in a real game implementation. In this paper, we evaluate our drama management approach deployed in our own implementation of an interactive fiction game Anchorhead. Our approach uses player feedback as a basis for guiding the personalization of the interaction. The results indicate that our Drama Manager (DM) aids in providing a better overall experience for the players while guiding them through their interaction. Based on this work, we suggest that the strategies employed by the DM should take into account the player’s previous playing experience with the current game as well as his general game-playing experience.

Read the paper:

Drama Management Evaluation for Interactive Fiction Games

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

AAAI-07 Spring Symposium on Intelligent Narrative Technologies, Arlington, VA, November 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-15.pdf

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

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
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