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

Do pharmacists contribute to patients’ management of symptoms suggestive of cancer : a qualitative study

Funding This work was supported by the Sir Hugh Linstead Fellowship Award from Pharmacy Research UK. Acknowledgements The authors would like to acknowledge the support of the consultant physicians Marianne Nicolson, Russell Petty and Les Samuel Aberdeen Royal Infirmary with patient recruitment.Peer reviewedPostprin

Framer: Planning Models from Natural Language Action Descriptions

In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants' NL input. We demonstrate that our approach is effective at learning models on these tasks

Using a discrete choice experiment to develop a decision aid tool to inform the management of persistent pain in pharmacy : – a protocol for a randomised feasibility study

Acknowledgments The authors thank participants from previous stages that helped design the decision aid tool and study procedures for this feasibility study. They also thank Amanda Cardy and the NHS Research Scotland Primary Care (NRS Primary Care) network for their help recruiting healthcare professionals and patients in this and previous stages of the project, and members of the Patient Advisory Group whose input throughout has helped guide the study. The authors thanks internal reviewers whose comments helped improve this Protocol. The authors also thank Dr Gin Nie Chua for her work in the grant awarding and literature review process. Funding This work was funded by Pharmacy Research UK (Grant PRUK-2016-PG57-1). Additional funding has been granted by the University of Aberdeen. The Health Economics Research Unit is supported by the Scottish Government Health and Social Care Directorate. The funders had no input to the study design, collection, management, analysis or interpretation of the data and will have no input to the writing of the report or decision to submit for publication.Peer reviewedPublisher PD

Spin coefficients for four-dimensional neutral metrics, and null geometry

Notation for spin coefficients for metrics of neutral signature in four dimensions is introduced. The utility and interpretation of spin coefficients is explored through themes in null geometry familiar from (complex) general relativity. Four-dimensional Walker geometry is exploited to provide examples and the generalization of the real neutral version of Pleba\~nski's (1975) second heavenly equation to certain Walker geometries given in Law and Matsushita [16] is extended further.Comment: 50 pages; minor typos corrected in v