Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and to use them for guiding search, in the hope of speeding up the planning process. We go beyond the previous approaches by considering ordering constraints not only over the (top-level) goals, but also over the sub-goals that will necessarily arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We extend Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks. We show how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant runtime performance improvements when used as a control loop around state-of-the-art sub-optimal planning systems, as exemplified by FF and LPG.

Abstract. AI planning has featured in a number of Interactive Storytelling prototypes: since narratives can be naturally modelled as a sequence of actions it has been possible to exploit state of the art planners in the task of narrative generation. However the characteristics of a "good" plan, such as optimality, aren't necessarily the same as those of a "good" narrative, where errors and convoluted sequences may offer more reader interest, so some narrative structuring is required. In our work we have looked at injecting narrative control into plan generation through the use of PDDL3.0 state trajectory constraints which enable us to express narrative control information within the planning representation. As part of this we have developed an approach to planning with such trajectory constraints. The approach decomposes the problem into a set of smaller subproblems using the temporal orderings described by the constraints and then solves these subproblems incrementally. In this paper we outline our method and present results that illustrate the potential of the approach.

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Previous Interactive Storytelling systems have been designed to allow active user intervention in an unfolding story, using established multi-modal interactive techniques to influence narrative development. In this paper we instead explore the use of a form of passive interaction where users' affective responses, measured by physiological proxies, drive a process of narrative adaptation. We introduce a system that implements a passive interaction loop as part of narrative generation, monitoring users' physiological responses to an on-going narrative visualization and using these to adapt the subsequent development of character relationships, narrative focus and pacing. Idiomatic cinematographic techniques applied to the visualization utilize existing theories of establishing characteristic emotional tone and viewer expectations to foster additional user response. Experimental results support the applicability of filmic emotional theories in a non-film visual realization, demonstrating significant appropriate user physiological response to narrative events and "emotional cues". The subsequent narrative adaptation provides a variation of viewing experience with no loss of narrative comprehension.

This paper discusses the potential of Brain-Computer Interfaces based on neurofeedback methods to support emotional control and pursue the goal of emotional control as a mechanism for human augmentation in specific contexts. We illustrate this discussion through two proof-ofconcept, fully-implemented experiments: one controlling disposition towards virtual characters using pre-frontal alpha asymmetry, and the other aimed at controlling arousal through activity of the amygdala. In the first instance, these systems are intended to explore augmentation technologies that would be incorporated into various media-based systems rather than permanently affect user behaviour.

Rights'ACM Allow an authors' version of their own ACMcopyrighted work on their personal server or on servers belonging to their employers ' ABSTRACTIn the paper we present a prototype of video-based storytelling that is able to generate multiple story variants from a baseline video. The video content for the system is generated by an adaptation of forefront video summarisation techniques that decompose the video into a number of Logical Story Units (LSU) representing sequences of contiguous and interconnected shots sharing a common semantic thread. Alternative storylines are generated using AI Planning techniques and these are used to direct the combination of elementary LSU for output. We report early results from experiments with the prototype in which the reordering of video shots on the basis of their high-level semantics produces trailers giving the illusion of different storylines.

This paper describes our experience in capturing, using a formal specification language, a model of the knowledge-intensive domain of oceanic air traffic control. This model is intended to form part of the requirements specification for a decision support system for air traffic controllers. We give an overview of the methods we used in analysing the scope of the domain, choosing an appropriate formalism, developing a domain model, and validating the model in various ways. Central to the method was the development of a formal requirements engineering environment which provided automated tools for model validation and maintenance

AI Planning has been shown to be a useful approach for the generation of narrative in interactive entertainment systems and games. However, the creation of the underlying narrative domain models is challenging: the well documented AI planning modelling bottleneck is further compounded by the need for authors, who tend to be non-technical, to create content. We seek to support authors in this task by allowing natural language (NL) plot synopses to be used as a starting point from which planning domain models can be automatically acquired. We present a solution which analyses input NL text summaries, and builds structured representations from which a pddl model is output (fully automated or author in-the-loop). We introduce a novel sieve-based approach to pronoun resolution that demonstrates consistently high performance across domains. In the paper we focus on authoring of narrative planning models for use in interactive entertainment systems and games. We show that our approach exhibits comprehensive detection of both actions and objects in the system-extracted domain models, in combination with significant improvement in the accuracy of pronoun resolution due to the use of contextual object information. Our results and an expert user assessment show that our approach enables a reduction in authoring effort required to generate baseline narrative domain models from which variants can be built.