Collaborative hybrid agent provision of learner needs using ontology based semantic technology

© Springer International Publishing AG 2017. This paper describes the use of Intelligent Agents and Ontologies to implement knowledge navigation and learner choice when interacting with complex information locations. The paper is in two parts: the first looks at how Agent Based Semantic Technology can be used to give users a more personalised experience as an individual. The paper then looks to generalise this technology to allow users to work with agents in hybrid group scenarios. In the context of University Learners, the paper outlines how we employ an Ontology of Student Characteristics to personalise information retrieval specifically suited to an individual’s needs. Choice is not a simple “show me your hand and make me a match” but a deliberative artificial intelligence (AI) that uses an ontologically informed agent society to consider the weighted solution paths before choosing the appropriate best. The aim is to enrich the student experience and significantly re-route the student’s journey. The paper uses knowledge-level interoperation of agents to personalise the learning space of students and deliver to them the information and knowledge to suite them best. The aim is to personalise their learning in the presentation/format that is most appropriate for their needs. The paper then generalises this Semantic Technology Framework using shared vocabulary libraries that enable individuals to work in groups with other agents, which might be other people or actually be AIs. The task they undertake is a formal assessment but the interaction mode is one of informal collaboration. Pedagogically this addresses issues of ensuring fairness between students since we can ensure each has the same experience (as provided by the same set of Agents) as each other and an individual mark may be gained. This is achieved by forming a hybrid group of learner and AI Software Agents. Different agent architectures are discussed and a worked example presented. The work here thus aims at fulfilling the student’s needs both in the context of matching their needs but also in allowing them to work in an Agent Based Synthetic Group. This in turn opens us new areas of potential collaborative technology

Friendly lords implementing an artificial intelligence social architecture in Mount& Blade II: Bannerlord

Despite living in a golden age for video games where there is an increase in the development and popularity of new technologies, such as Virtual Reality and Augmented Reality, and constant improvements being made on immersion, either through rich storytelling, high graphical fidelity or even gameplay itself, one area that is generally lacking on innovation is the social interaction of Non-Playable Characters. The credibility of these virtual characters (usually called NPCs) requires that they have human characteristics, such as emotions and the ability to think and make decisions depending on their own will. One of the most important characteristics is the ability to socialize and interact with each other. The main objective of this work was to implement a Social Architecture of Artificial Intel ligence in the game Mount&Blade II: Bannerlord to enhance the credibility of NPCs, making them socially more active and interesting, and improving the User Experience. In the game in which this study takes place (respectively Mount&Blade II: Bannerlord), although there are plenty of NPCs in the world, these are extremely limited, predictable and rarely demonstrate social behaviors. It also addresses some socio-emotional architectures such as CiF and FAtiMA. While FA tiMA is more focused on the generation of emotions, and how those emotions may affect the behaviour of characters, CiF has an explicit representation of social relations between NPCs and how the can influence behaviour. Due to this, the system architecture used as the basis for our model is the CiF architecture, also used before in some popular games like Skyrim and Conan Exiles. It is a system designed to be able to generate social behavior for the social agents, in this case the virtual characters. Instead of looking static and appearing to be clones of each other, the NPCs will appear more natural, making them more interesting and believable from the point of view of social interaction. One of the major differences between the CiF model and the developed model, named Comme il Faut - Bannerlord (CiF-BL), was, respectively, an addition of a centralized component, which will manage about which NPCs will be able to engage in interactions, how many Social Interactions will be able to take place at the same time, when these are started and finished From CiF-BL’s perspective, the player is just another character, and just as NPCs will want to interact with each other, they will also want to interact with the player. To do this work, it was necessary to adapt the game’s existing dialog system to make CiF BL’s changes possible. Adding the options that allow the player to interact socially with an NPC and vice-versa. It was also necessary to adapt and implement another game system, responsible for signaling and informing the most relevant locations and characters. This adaptation was responsible for making it possible to visualize the interactions between the different NPCs. Thus, during the time that the user is present in the virtual world, the virtual characters will no longer have a totally irrelevant and figurative role in the game, and will have a greater social participation. With the goal of appearing more believable, natural, and, to look more like "living beings". The developed model was validated and evaluated through user tests. Unfortunately, due to COVID-19 restrictions that occurred during the execution of this thesis, it was not possible to conduct a controlled in-person evaluation (which would be the ideal way to evaluate our work). Instead, a mod was produced and released online to the Bannerlords player and modding community. The only difference between the mod and the base version of the game was the CIF-BL model used to control the NPCs. The mod was called "Friendly Lords" and released in 18th of August 2021, both in NexusMods and in ModDB. The mod was announced on social networks, such as Reddit, in groups that were related to the game. Participants were voluntarily invited to complete an anonymous questionnaire regarding their gaming experience, and the answers were collected. From the analysis of these same answers, very favorable results were obtained for the objectives we defined. However, overall, the feedback from players and the community has been very positive about the mod and its modifications to the game. Some examples are that it looks promising, that they want active development, and that are grateful to help bring new life to the game. People give suggestions and constructive feedback to implement in the mod and help get a better experience. Meanwhile, some players have shown more interest and offered to collaborate directly in the development, either for more dialogue options between the NPCs, or to translate the mod respectively into Turkish which is the native language of the studio currently developing the game. In short, using the social architecture implemented in the game, the Credibility of NPCs and the Social Presence improved by more than 30%, having successfully achieved the goals. This document describes in more detail the process of researching, implementing and testing the model. To improve this work and the Artificial Intelligence social architecture, it would be necessary to at least add more personality traits and a greater number of different social interactions, for the NPCs to have a greater diversity in terms of their social behavior.Apesar de vivermos numa era dourada para os videojogos onde há um aumento do desen volvimento e da popularidade de novas tecnologias, como a Realidade Virtual e a Realidade Aumentada, e constantes melhorias a serem feitas na imersão, seja através de ricas narrativas, fidelidade gráfica elevada ou até mesmo gameplay em si, uma área que geralmente não é alvo de inovação é a interação social entre as Personagens Não Jogáveis. A credibilidade dessas personagens virtuais (normalmente chamadas de NPCs) exige que estas tenham características humanas, como emoções e a capacidade de pensar e tomar decisões dependendo da sua própria vontade. Uma das características mais importante é a capacidade de socializar e interagir uns com os outros. O objectivo principal deste trabalho é implementar uma Arquitectura Social de Inteligência Artificial no jogo Mount&Blade II: Bannerlord para incrementar a Credibilidade dos NPCs, tornando-os socialmente mais ativos e interessantes, e melhorar a Experiência do Utilizador. No jogo em que se realiza este estudo (respetivamente Mount&Blade II: Bannerlord), embora haja uma abundância de NPCs no mundo, estes são extremamente limitados, previsíveis e raramente demonstram comportamentos sociais. Este trabalho aborda diferentes arquitecturas socio-emocionais, como Comme il Faut (CiF) e FAtiMA. Enquanto FAtiMA está mais centrado na geração de emoções, e como essas emoções podem afectar o comportamento das personagens, CiF tem uma representação explícita das relações sociais entre os NPCs e de que forma o comportamento pode influenciar. Devido a isto, a arquitetura do sistema usada como base para o nosso modelo é a arquitetura CiF, também já usada antes em alguns jogos populares como Skyrim e Conan Exiles. É um sistema concebido para ser capaz de gerar comportamentos sociais para os agentes sociais, neste caso, os personagens virtuais. Em vez de parecerem estáticos e aparentarem serem clones uns dos outros, os NPCs vão parecer mais naturais, tornando-os mais interessantes e credíveis do ponto de vista da interação social. Uma das maiores diferenças entre o modelo CiF e o modelo final desenvolvido, nomeado de Comme il Faut - Bannerlord (CiF-BL), foi, respectivamente, uma adição de um componente centralizado, que irá fazer a gestão acerca de quais os NPCs que estarão aptos para se envolverem em interações, quantas Interações Sociais serão capazes de se realizar ao mesmo tempo, quando estas são iniciadas e terminadas. Da perspectiva do CiF-BL, o jogador é apenas uma outra personagem e, assim como os NPCs vão querer interagir uns com os outros, eles também vão querer interagir com o jogador. Para realizar este trabalho, foi necessário adaptar o atual sistema de diálogo já existente do jogo para possibilitar as alterações do CiF-BL. Adicionando assim as opções que permitem ao jogador interagir socialmente com um NPC e vice-versa. Foi necessária também uma adaptação e a implementação de um outro sistema do jogo, responsável por assinalar e informar as localizações e os personagens mais relevantes. Esta adaptação foi responsável para tornar possível a visualização das interações entre os diferentes NPCs. Assim, durante o tempo em que o utilizador estiver presente no mundo virtual, as personagens virtuais irão deixar de ter um papel totalmente irrelevante e figurativo no jogo, e, terão uma maior participação social. Com o objectivo de parecerem mais credíveis, naturais, e, de se assemelharem mais com “seres vivos”. Para a validação e a avaliação do modelo, este foi sujeito a testes de utilizador. Infelizmente, devido às restrições COVID-19 que ocorreram durante a execução desta tese, não foi possível realizar uma avaliação controlada presencialmente (que seria a forma ideal de avaliar o nosso trabalho). Em vez disso, um mod foi produzido e lançado online para os jogadores do jogo Bannerlord e para a comunidade modding. A única diferença entre o mod e a versão base do jogo foi o modelo CIF-BL usado para controlar e melhorar os NPCs. O mod foi chamado de "Friendly Lords" e lançado em 18 de agosto de 2021, tanto no site NexusMods como no ModDB. O mod foi anunciado nas redes sociais, como o Reddit, em grupos que estavam relacionados com o jogo. Os participantes foram convidados voluntariamente a preencher um questionário anónimo relativamente à sua experiência de jogo, sendo feita a recolha das respostas. A partir da análise dessas mesmas respostas, foram obtidos resultados muito favoráveis para os objectivos que definimos. Entretanto, no geral, o feedback dos jogadores e da comunidade foi muito positivo sobre o mod e sobre as suas modificações no jogo. Alguns exemplos são que parece promissor, que querem um desenvolvimento ativo, e que estão gratos por ajudar a trazer uma nova vida ao jogo. As pessoas dão sugestões e feedback construtivo para implementar no mod e ajudar a obter uma melhor experiência. Entretanto, alguns jogadores mostraram mais interesse e disponibilizaram se para colaborar diretamente no desenvolvimento, quer seja para mais opções de diálogos entre os NPCs, quer seja para a tradução do mod, respectivamente para Turco que é a língua materna do estúdio que atualmente desenvolve o jogo. Resumindo, usando a arquitetura social implementada no jogo, a Credibilidade dos NPCs e a qualidade do comportamento social melhorou mais de 30%, tendo atingido com sucesso os objetivos. Este documento descreve mais em pormenor o processo de pesquisa, de implemen tação e de teste do modelo. Para melhorar este trabalho e a sua arquitetura social de Inteligência Artificial, seria necessário, pelo menos, adicionar mais traços de personalidade e um maior número de diferentes interações sociais, para os NPCs terem uma maior diversidade quanto ao seu comportamento social

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

An Actor-Centric Approach to Facial Animation Control by Neural Networks For Non-Player Characters in Video Games

Game developers increasingly consider the degree to which character animation emulates facial expressions found in cinema. Employing animators and actors to produce cinematic facial animation by mixing motion capture and hand-crafted animation is labor intensive and therefore expensive. Emotion corpora and neural network controllers have shown promise toward developing autonomous animation that does not rely on motion capture. Previous research and practice in disciplines of Computer Science, Psychology and the Performing Arts have provided frameworks on which to build a workflow toward creating an emotion AI system that can animate the facial mesh of a 3d non-player character deploying a combination of related theories and methods. However, past investigations and their resulting production methods largely ignore the emotion generation systems that have evolved in the performing arts for more than a century. We find very little research that embraces the intellectual process of trained actors as complex collaborators from which to understand and model the training of a neural network for character animation. This investigation demonstrates a workflow design that integrates knowledge from the performing arts and the affective branches of the social and biological sciences. Our workflow begins at the stage of developing and annotating a fictional scenario with actors, to producing a video emotion corpus, to designing training and validating a neural network, to analyzing the emotion data annotation of the corpus and neural network, and finally to determining resemblant behavior of its autonomous animation control of a 3d character facial mesh. The resulting workflow includes a method for the development of a neural network architecture whose initial efficacy as a facial emotion expression simulator has been tested and validated as substantially resemblant to the character behavior developed by a human actor

(MU-CTL-01-12) Towards Model Driven Game Engineering in SimSYS: Requirements for the Agile Software Development Process Game

Software Engineering (SE) and Systems Engineering (Sys) are knowledge intensive, specialized, rapidly changing disciplines; their educational infrastructure faces significant challenges including the need to rapidly, widely, and cost effectively introduce new or revised course material; encourage the broad participation of students; address changing student motivations and attitudes; support undergraduate, graduate and lifelong learning; and incorporate the skills needed by industry. Games have a reputation for being fun and engaging; more importantly immersive, requiring deep thinking and complex problem solving. We believe educational games are essential in the next generation of e-learning tools. An extensible, freely available, engaging, problem-based game platform that provides students with an interactive simulated experience closely resembling the activities performed in a (real) industry development project would transform the SE/Sys education infrastructure. Our goal is to extend the state-of-the-art research in SE/Sys education by investigating a game development platform (GDP) from an interdisciplinary perspective (education, game research, and software/systems engineering). A meta-model has been proposed to provide a rigourous foundation that integrates the three disciplines. The GDP is intended to support the semi-automated development of collections of scripted games and their execution, where each game embodies a specific set of learning objectives. The games are scripted using a template based approach. The templates integrate three approaches: use cases; storyboards; and state machines (timed, concurrent, hierarchical state machines). The specification templates capture the structure of the game (Game, Acts, Scenes, Screens, Challenges), storyline, characters (player, non-player, external), graphics, music/sound effects, rules, and so on. The instantiated templates are (manually) transformed into XML game scripts that can be loaded into the SimSYS Game Play Engine. As a game is played, the game play events are logged; they are analyzed to automatically assess a player’s accomplishments and automatically adapt the game play script. Currently, we are manually defining a collection of games. The games are being used to ensure the GDP is flexible and reliable (i.e., the prototype can load and correctly run a variety of game scripts), the ontology is comprehensive, and the templates assist in defining well-organized, modular game scripts. In this report, we present the initial part of an Agile Software Development Process game (Act I, Scenes 1 and 2) that embodies learning objectives related to SE fundamentals (requirements, architecture, testing, process); planning with Gantt charts; working with budgets; and selecting a team for an agile development project. A student player is rewarded in the game by getting hired, scoring points, or getting promoted to lead a project. The game has a variety of settings including a classroom, job fair, and a work environment with meeting rooms, cubicles, and a water cooler station. The main non-player characters include a teacher, boss, and an evil peer. In the future, semi-automated support for creating new game scripts will be explored using a wizard interface. The templates will be formally defined, supporting automated transformation into XML game scripts that can be loaded into the SimSYS Game Engine. We also plan to explore transforming the requirements into a notation that can be imported into a commercial tool that supports Statechart simulation

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or “social talk”) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called “out-of-domain” (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads

Social talk capabilities for dialogue systems

Small talk capabilities are an important but very challenging extension to dialogue systems. Small talk (or social talk) refers to a kind of conversation, which does not focus on the exchange of information, but on the negotiation of social roles and situations. The goal of this thesis is to provide knowledge, processes and structures that can be used by dialogue systems to satisfactorily participate in social conversations. For this purpose the thesis presents research in the areas of natural-language understanding, dialogue management and error handling. Nine new models of social talk based on a data analysis of small talk conversations are described. The functionally-motivated and content-abstract models can be used for small talk conversations on various topics. The basic elements of the models consist of dialogue acts for social talk newly developed on basis of social science theory. The thesis also presents some conversation strategies for the treatment of so-called out-of-domain (OoD) utterances that can be used to avoid errors in the input understanding of dialogue systems. Additionally, the thesis describes a new extension to dialogue management that flexibly manages interwoven dialogue threads. The small talk models as well as the strategies for handling OoD utterances are encoded as computational dialogue threads