Symbolic Plan Recognition in Interactive Narrative Environments

AAAI Conferences

Interactive narratives suffer from the narrative paradox: the tension that exists between providing a coherent narrative experience and allowing a player free reign over what she can manipulate in the environment. Knowing what actions a player in such an environment intends to carry out would help in managing the narrative paradox, since it would allow us to anticipate potential threats to the intended narrative experience and potentially mediate or eliminate them. The process of observing player actions and attempting to come up with an explanation for those actions (i.e. the plan that the player is trying to carry out) is the problem of plan recognition. We adopt the framing of narratives as plans and leverage recent advances that cast plan recognition as planning to develop a symbolic plan recognition system as a proof-of-concept model of a player's reasoning in an interactive narrative environment. In this paper we outline the system architecture, report on performance metrics that demonstrate adequate performance for non-trivial domains, and discuss the implications of treating players as plan recognizers.


Toward Narrative Schema-Based Goal Recognition Models for Interactive Narrative Environments

AAAI Conferences

Computational models for goal recognition hold great promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form, plan recognition, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.


Preface

AAAI Conferences

Narrative is a pervasive aspect of all human societies. Human beings make sense of the world by constructing stories and listening to the stories of others. In addition, stories as a form of entertainment play a central role in our social and leisure lives. As a result, story and narrative have become a key interest for Artificial Intelligence researchers. The role of narrative as a primary mechanism for organizing human experience has been broadly recognized.


Learning Director Agent Strategies: An Inductive Framework for Modeling Director Agents

AAAI Conferences

Interactive narrative environments offer significant potential for creating engaging narrative experiences that are tailored to individual users. Increasingly, applications in education, training, and entertainment are leveraging narrative to create rich interactive experiences in virtual storyworlds. A key challenge posed by these environments is devising accurate models of director agents’ strategies that determine the most appropriate director action to perform for crafting customized story experiences. A promising approach is developing an empirically informed model of director agents’ decision-making strategies. In this paper, we propose a framework for learning models of director agent decision-making strategies by observing human-human interactions in an interactive narrative-centered learning environment. The results are encouraging and suggest that creating empirically driven models of director agent decision-making is a promising approach to interactive narrative.


Improving Goal Recognition in Interactive Narratives with Models of Narrative Discovery Events

AAAI Conferences

Computational models of goal recognition hold considerable promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form plan recognition, has been the subject of extensive investigation in the AI community. However, there have been relatively few empirical investigations of goal recognition models in the intelligent narrative technologies community to date, and little is known about how computational models of interactive narrative can inform goal recognition. In this paper, we investigate a novel goal recognition model based on Markov Logic Networks (MLNs) that leverages narrative discovery events to enrich its representation of narrative state. An empirical evaluation shows that the enriched model outperforms a prior state-of-the-art MLN model in terms of accuracy, convergence rate, and the point of convergence.