The role of emotion in believable agents
Communications of the ACM
Character-Based Interactive Storytelling
IEEE Intelligent Systems
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Thespian: using multi-agent fitting to craft interactive drama
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Narrative generation: balancing plot and character
Narrative generation: balancing plot and character
Reinforcement learning for declarative optimization-based drama management
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
AI characters and directors for interactive computer games
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Open-world planning for story generation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Evaluating directorial control in a character-centric interactive narrative framework
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Narrative planning: balancing plot and character
Journal of Artificial Intelligence Research
Controlling narrative time in interactive storytelling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Narrative planning: compilations to classical planning
Journal of Artificial Intelligence Research
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In Interactive Storytelling (IS) the prevailing approach for the automatic generation of plausible narratives that meet global author goals is intentional planning. However, existing approaches suffer from limited expressiveness and poor scalability. We address this by replacing single intentional planners with multiple agents representing the characters of a narrative, which can reason about the relevance of narrative actions given their individual intents. These are then combined using a state-based forward search procedure that results in a significantly smaller search space. Unlike other multiagent approaches, these agents calculate all reasonable plans in a state. This allows a search of a wide range of narrative possibilities prior to execution as in planner-based approaches, rather than agents making early plan commitments in a simulation. We demonstrate that this not only produces the same forms of narrative as single intentional planners but can be extended to generate narratives that are beyond their scope. We also present a search heuristic that exploits the agents' relevant actions to further reduce the size of the explored search space. Experimental results demonstrate system performance that makes it suitable for use in real-time applications such as IS.