Everything-as-a-Thing for Abstracting the Internet-of-Things

Copyright © 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved This paper discusses Everything-as-a-Thing (*aaT) as a novel way for abstracting the Internet-of-Things (IoT) applications. Compared to other forms of abstraction like Everything-as-a-Service (*aaS) and Everything-as-a-Resource (*aaR), *aaT puts emphasis on living things, on top of non-living things, that populate these applications. On the one hand, living things take over roles that are defined in terms of rights and duties. On the other hand, non-living things offer capabilities that are defined in terms of functional and non-functional properties. Interactions that occur between living and non-living things are specified as stories that define who does what, when, and where. For illustration purposes, *aaT is put into action using a healthcare case study

A Character Model with Moral Emotions: Preliminary Evaluation

In literary and drama criticism, emotions, and moral emotions in particular, have been pointed out as one of characterizing features of stories. In this paper, we propose to model story characters as value-based emotional agents, who appraise their own and others\u27 actions based on their desires and values, and feel the appropriate moral emotions in response to narrative situations that challenge their goals and values. In order to validate the appropriateness of the agent model for narrative characters, we ran an experiment with human participants aimed at comparing their expectations about characters\u27 emotions with the predictions of the value-based model of emotional agent. The results of the experiment show that the participants\u27 expectations meet the predictions of the model

Narrative balance management in an Intelligent biosafety training application for improving user performance

The use of three-dimensional virtual environments in training applications supports the simulation of complex scenarios and realistic object behaviour. While these environments have the potential to provide an advanced training experience to students, it is difficult to design and manage a training session in real time due to the number of parameters to pay attention to: timing of events, difficulty, user’s actions and their consequences or eventualities are some examples. For that purpose, we have extended our virtual Bio-safety Laboratory application used for training biohazard procedures with a Narrative Manager. The Narrative Manager controls the simulation deciding which events will take place in the simulation, and when, by controlling the narrative balance of the session. Our hypothesis is that the Narrative Manager allows us to increase the number of tasks for the user to solve and, due to balancing difficulty and intensity, it keeps the user interested in training. When evaluating our system we observed that the Narrative Manager effectively introduces more tasks for the user to solve, and despite that, is accepted by the users as more interesting and not harder than an identical system without a Narrative Manager. Also, a knowledge test demonstrated better results in users’ interest and learning output in the narrative condition

Does conflict improve story dialogue? : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Psychology at Massey University, Manawatū, New Zealand

While theorising on what makes a good story goes back over 1000 years, empirical research is more recent, and limited in extent. One almost universally held theory is that conflict improves stories. However, conflict is a broad and poorly conceptualised variable, and there is a dearth of empirical research into its effects on stories. Here we show that one specific form of conflict – conflictual dialogue – does not measurably improve ratings of story quality or how entertaining a story is. We used specially created stories, manipulated to create different levels of conflictual dialogue, in a repeated measures experiment. After the passage of dialogue, participants rated story quality and how entertaining they found the story. While the conflict manipulation was successful, is produced no significant difference in the rating of either quality or entertainment. However, the study may have been under-powered to find a small effect size. Despite the power issue, this study raises questions concerning whether conflict has a meaningful positive effect on the audience appreciation of conflictual dialogue, and may have wider implications for understanding the effects of conflict on stories

Platform for decoupling experience managers and environments

Experience Management employs Artificial Intelligence technologies to enhance people's interactive application experiences by dynamically modifying the environment during the experience. In game-related research, there is a prevailing trend where each experience manager is tightly integrated with the specific environment it can manipulate. This integration poses a challenge in comparing different managers within a single environment or a single manager across multiple environments. In this dissertation, I propose a solution to address this issue by introducing EM-Glue, an intermediary software platform that decouples experience managers from the environments they can modify. Prior to presenting the solution, I provide a comprehensive problem description and conduct a literature review to explore the current state of the field. Subsequently, I outline the platform's structural design, including a communication protocol facilitating interaction between managers and environments, as well as the regular communication process. Additionally, I develop a use case to evaluate the effectiveness of the proposed solution. This involves employing an environment and two experience managers: the Camelot Wrapper, a software I constructed to extend the interactive visualization engine Camelot and connect it to the platform, PaSSAGE, an existing experience manager adapted for use with the platform, and a random experience manager. The evaluation results demonstrate the platform's ability to decouple experience managers from environments, enabling future work to compare experience managers across multiple environments

A Plan-Based Model of Conflict for Narrative Reasoning and Generation

Narrative is one of the fundamental cognitive tools that we use to understand the world around us. When interacting with other humans we rely on a shared knowledge of narrative structure, but in order to enable this kind of communication with digital artifacts we must first formalize these narrative conventions. Narratology, computer science, and cognitive science have formed a symbiotic relationship around this endeavor to create computational models of narrative. These models provide us a deeper understanding of story structure and will enable us to create a fundamentally new kind of interactive narrative experience in which the author, the audience, and the machine all participate in the story composition process. This document presents a computational model of narrative conflict, its empirical evaluation, and its deployment in an interactive narrative experience. Narratologists have described conflict in terms of the difficulties that an intelligent agent encounters while executing a plan to achieve a gol.. This definition is inherently plan-based, and has been integrated into an existing model of narrative based on the data structures and algorithms of artificial intelligence planning|the process of constructing a sequence of actions to achieve a goal. The conflict Partial Order Causal Link (or CPOCL) model of narrative represents the events of a story along with their causal structure and temporal constraints. It extends previous models by representing non-executed actions which describe how an agent intended to complete its plans even if those plans failed, thus enabling an explicit representation of thwarted plans and conflict. The model also includes seven dimensions which can distinguish one conflict from another and provide authors with greater control over story generation: participants, topic, duration, balance, directness, intensity, and resolution. One valuable aspect of plan-based models is that they can be generated and modified automatically. Two story creation methods are discussed: the plan-space CPOCL algorithm that works directly with the rich CPOCL knowledge representation and the state-space Glaive algorithm which is significantly faster. Glaive achieves its speedup by incorporating research from fast forward-chaining state-space heuristic search planning and by using the constraints that a valid narrative plan must obey to calculate a more accurate heuristic. Glaive is fast enough to solve certain non-trivial narrative planning problems in real time. This computational model of narrative conflict has been evaluated in a series of empirical experiments. The first validates the three discrete dimensions of conflict: participants, topic, and duration. It demonstrates that a human audience recognizes thwarted plans in static text stories in the same places that the CPOCL model defines them to exist. The second experiment validates the four continuous dimensions|balance, directness, intensity, and resolution|by showing that a human audience ranks static text stories in the same order defined by the formulas for those dimensions. The final experiment is an evaluation of an interactive narrative video game called The Best Laid Plans, which uses Glaive to generate a story at run time from atomic actions and without recourse to pre-scripted behaviors or story fragments. In this game, the player first acts out a plan to achieve a goal and then Glaive coordinates all the non-player characters in the game to thwart the player's plan. The game is evaluated relative to two other versions: a control in which the other characters do nothing and a scripted version in which the other characters are controlled by programs written by a human author. Players recognize intentionality and conflict in the stories Glaive produces more so than in the control and comparably to the human scripted version. In summary, this document describes how a narratological definition of conflict as thwarted plans has been operationalized in plan data structures and incorporated into a narrative planning algorithm. The knowledge representation is rich enough that a human audience recognizes thwarted plans where the model defines them to exist. The algorithm is fast enough to be used in a real time interactive context for certain non-trivial story domains. This work represents one small advancement toward understanding human storytelling and leveraging that understanding in interactive systems