Reaching Cognitive Consensus with Improvisational Agents

A common approach to interactive narrative involves imbuing the computer with all of the potential story pre- authored story experiences (e.g. as beats, plot points, planning operators, etc.). This has resulted in an accepted paradigm where stories are not created by or with the user; rather, the user is given piecemeal access to the story from the gatekeeper of story knowledge: the computer (e.g. as an AI drama manager). This article describes a formal process that provides for the equal co-creation of story-rich experiences, where neither the user nor computer is in a privileged position in an interactive narrative. It describes a new formal approach that acts as a first step for the realtime co-creation of narrative in games that rely on the negotiated shared mental model between a human actor and an AI improv agent

Integration of agent-based modelling of social-spatial processes in architectural parametric design

A representation framework for modelling the social-spatial processes of inhabitation is proposed to extend the scope of parametric architectural design process. We introduce an agent-based modelling framework with a computational model of social-spatial dynamics at its core. Architectural parametric design is performed as a process of modelling the temporal characteristics of spatial changes required for members of a social group to reach social spatial comfort. We have developed a prototype agent-based modelling system using the Rhino-Grasshopper platform. The system employs a human behaviour model adapted from the PECS (Physical, Emotional, Cognitive, Social) reference model first proposed by Schmidt and Urban. The agent-based model and its application was evaluated by comparative modelling of two real Vietnamese dwellings: a traditional vernacular house in Hue and a contemporary house in Ho Chi Minh City. The evaluation shows that the system returns differentiated temporal characteristics of spatial modifications of the two dwellings as expected

Innovative integrated architecture for educational games: Challenges and merits

Interactive Narrative in game environments acts as the main catalyst to provide a motivating learning experience. In previous work, we have described how the use of a dual narrative generation technique could help to resolve the conflict between allowing high player student agency and also the track of the learning process. In this paper, we define a novel architecture that assists the dual narrative generation technique to be employed effectively in an adaptive educational game environment. The architecture composes components that individually have shown effectiveness in educational games environments. These components are graph structured narrative, dynamically generated narrative, evolving agents and a student model. An adaptive educational game, AEINS, has been developed to investigate the synergy of the architecture components. AEINS aims to foster character education at 8-12 year old children through the use of various interactive moral dilemmas that attempt the different student\u27s cognitive levels. AEINS was evaluated through a study involved 20 participants who interacted with AEINS on an individual basis

A Creative Exploration of the Use of Intelligent Agents in Spatial Narrative Structures

This thesis is an interdisciplinary study of authoring tools for creating spatial narrative structures– exposing the relationship between artists, the tools they use, and the experiences they create. It is a research-creation enterprise resulting in the creation of a new authoring tool. A prototype collaborative tool for authoring spatial narratives used at the Land|Slide: Possible Futures public art exhibit in Markham, Ontario 2013 is described. Using narrative analysis of biographical information a cultural context for authoring and experiencing spatial narrative structures is discussed. The biographical information of artists using digital technologies is posited as a context framing for usability design heuristics. The intersection of intelligent agents and spatial narrative structures provide a future scenario by which to assess the suitability of the approach outlined in this study

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System Roya Aminikia Learning Management Systems (LMSs) are digital frameworks that provide curriculum, training materials, and corresponding assessments to guarantee an effective learning process. Although these systems are capable of distributing the learning content, they do not support dynamic learning processes and do not have the capability to communicate with human learners who are required to interact in a dynamic environment during the learning process. To create this process and support the interaction feature, LMSs are equipped with Intelligent Tutoring Systems (ITSs). The main objective of an ITS is to facilitate students’ movement towards their learning goals through virtual tutoring. When equipped with ITSs, LMSs operate as dynamic systems to provide students with access to a tutor who is available anytime during the learning session. The crucial issues we address in this thesis are how to set up a dynamic LMS, and how to design the logical structure behind an ITS. Artificial intelligence, multi-agent technology and machine learning provide powerful theories and foundations that we leverage to tackle these issues. We designed and implemented the new concept of Pedagogical Agent (PA) as the main part of our ITS. This agent uses an evaluation procedure to compare each particular student, in terms of performance, with their peers to develop a worthwhile guidance. The agent captures global knowledge of students’ feature measurements during students’ guiding process. Therefore, the PA retains an updated status, called image, of each specific student at any moment. The agent uses this image for the purpose of diagnosing students’ skills to implement a specific correct instruction. To develop the infrastructure of the agent decision making algorithm, we laid out a protocol (decision tree) to select the best individual direction. The significant capability of the agent is the ability to update its functionality by looking at a student’s image at run time. We also applied two supervised machine learning methods to improve the decision making protocol performance in order to maximize the effect of the collaborating mechanism between students and the ITS. Through these methods, we made the necessary modifications to the decision making structure to promote students’ performance by offering prompts during the learning sessions. The conducted experiments showed that the proposed system is able to efficiently classify students into learners with high versus low performance. Deployment of such a model enabled the PA to use different decision trees while interacting with students of different learning skills. The performance of the system has been shown by ROC curves and details regarding combination of different attributes used in the two machine learning algorithms are discussed, along with the correlation of key attributes that contribute to the accuracy and performance of the decision maker components

Believable Agents and Intelligent Story Adaptation for Interactive Storytelling

Abstract. Interactive Narrative is an approach to interactive entertainment that enables the player to make decisions that directly affect the direction and/or outcome of the narrative experience being delivered by the computer system. Interactive narrative requires two seemingly conflicting requirements: coherent narrative and user agency. We present an interactive narrative system that uses a combination of narrative control and autonomous believable character agents to augment a story world simulation in which the user has a high degree of agency with narrative plot control. A drama manager called the Automated Story Director gives plot-based guidance to believable agents. The believable agents are endowed with the autonomy necessary to carry out directives in the most believable fashion possible. Agents also handle interaction with the user. When the user performs actions that change the world in such a way that the Automated Story Director can no longer drive the intended narrative forward, it is able to adapt the plot to incorporate the user’s changes and still achieve dramatic goals.

Designing Games To Be Retold

Master'sMASTER OF ART