On the extraction, ordering, and usage of landmarks in planning

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 use them for guiding search, in the hope to speed up the planning process. We go beyond the previous approaches by dening ordering constraints not only over the (top level) goals, but also over the sub-goals that will arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We show how such landmarks can be found, how their inherent ordering constraints can be approximated, and how this information can be used to decompose a given planning task into severa smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant performance improvements in both heuristic forward search and GRAPHPLAN-style planning

Plan-based narrative generation with coordinated subplots

Despite recent progress in plan-based narrative generation, one major limitation is that systems tend to produce a single plotline whose progression entirely determines the narrative experience. However, for certain narrative genres such as serial dramas and soaps, multiple interleaved subplots are expected by the audience, as this tends to be the norm in real-world, human-authored narratives. Current narrative generation techniques have overlooked this important requirement, something which could improve the perceived quality of generated stories. To this end, we have developed a flexible plan-based approach to multiplot narrative generation, that successfully generates narratives conforming to different subplot profiles, in terms of the number of subplots interleaved and the relative time spent on each presentation. We have identified specific challenges such as: distribution of virtual characters across subplots; length of each subplot presentation; and transitioning between subplots. In this paper, we overview this approach and describe its operation in a prototype Interactive Storytelling (IS) System set in the serial drama genre. Results of experiments with the system demonstrate its usability. Furthermore, results of a user study highlight the potential of the approach, with clear user preference for presentations that feature interleaved multiple subplots

Exploring passive user interaction for adaptive narratives

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

Best-Fit Action-Cost Domain Model Acquisition and its application to authorship in interactive narrative

Domain model acquisition is the problem of learning the structure of a state-transition system from some input data, typically example transition sequences. Recent work has shown that it is possible to learn action costs of PDDL models, given the overall costs of individual plans. In this work we have explored the extension of these ideas to narrative planning where cost can represent a variety of features (e.g. tension or relationship strength) and where exact solutions don’t exist. Hence in this paper we generalise earlier results to show that when an exact solution does not exist, a best-fit costing can be generated. This approach is of particular interest in the context of plan-based narrative generation where the input cost functions are based on subjective input. In order to demonstrate the effectiveness of the approach, we have learnt models of narratives using subjective measures of narrative tension. These were obtained with narratives (presented as video in this case) that were encoded as action traces, and then scored for subjective narrative tension by viewers. This provided a collection of input plan traces for our domain model acquisition system to learn a best-fit model. Using this learnt model we demonstrate how it can be used to generate new narratives that fit different target levels of tension