Artificial Intelligence - Special volume on planning and scheduling
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Managing interaction between users and agents in a multi-agent storytelling environment
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
An Intent-Driven Planner for Multi-Agent Story Generation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
Case-based plan recognition in computer games
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
U-director: a decision-theoretic narrative planning architecture for storytelling environments
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Narrative Generation for Suspense: Modeling and Evaluation
ICIDS '08 Proceedings of the 1st Joint International Conference on Interactive Digital Storytelling: Interactive Storytelling
ICIDS '08 Proceedings of the 1st Joint International Conference on Interactive Digital Storytelling: Interactive Storytelling
Simulating socially intelligent agents in semantic virtual environments
The Knowledge Engineering Review
Probabilistic goal recognition in interactive narrative environments
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
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In interactive plan-based narrative environments, user's actions must be monitored to ensure that conditions necessary for the execution of narrative plans are not compromised. In the Mimesis system, management of user actions is performed on a reactionary basis by a process called mediation. In this paper, we describe an extension to this approach, proactive mediation, which calculates responses to user input in an anticipatory manner. A proactive mediation module accepts as input a plan describing the actions being performed by the user (generated by a plan recognition system) and identifies portions of that plan that jeopardize the causal structure of the overall narrative. Once these portions are identified, proactive mediation generates modifications to the narrative plan structure that avoid the unwanted interaction between user and story. This extension to the original mediation algorithm provides more responses to a user's actions and generates responses that are tailored to the user's actions.