Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Serious Games in Cultural Heritage

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

Influence map-based pathfinding algorithms in video games

Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems by intelligent agents, ranging from computer games and applications to robotics. Pathfinding is a particular kind of search, in which the objective is to find a path between two nodes. A node is a point in space where an intelligent agent can travel. Moving agents in physical or virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able to navigate through its surrounding environment without avoiding obstacles, it does not seem intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games. Pathfinding algorithms work well with single agents navigating through an environment. In realtime strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large and dynamic game environments. On the contrary, influence maps are not used in pathfinding. Influence maps are a spatial reasoning technique that helps bots and players to take decisions about the course of the game. Influence map represent game information, e.g., events and faction power distribution, and is ultimately used to provide game agents knowledge to take strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g., capture an enemy location and win the game. Tactical decisions are based on small and precise actions, e.g., where to install a turret, where to hide from the enemy. This dissertation work focuses on a novel path search method, that combines the state-of-theart pathfinding algorithms with influence maps in order to achieve better time performance and less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho mais curto, desde a àrea jogos de computador até à robótica. Pathfinding é um tipo particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar. Agentes móveis em mundos físicos e virtuais são uma componente chave para a simulação de comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz parte das tarefas fundamentais de inteligencia artificial em vídeo jogos. Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados no pathfinding. Influence maps são uma técnica de raciocínio espacial que ajudam agentes inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo. Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding com influence maps, afim de alcançar melhores performances a nível de tempo de pesquisa e consumo de memória, assim como obter caminhos visualmente mais suaves

Machine learning state evaluation in prismata

Strategy games are a unique and interesting testbed for AI protocols due their complex rules and large state and action spaces. Recent work in game AI has shown that strong, robust AI agents can be created by combining existing techniques of deep learning and heuristic search. Heuristic search techniques typically make use of an evaluation function to judge the value of a game state, however these functions have historically been hand-coded by game experts. Recent results have shown that it is possible to use modern deep learning techniques to learn these evaluation functions, bypassing the need for expert knowledge. In this thesis, we explore the implementation of this idea in Prismata, an online strategy game by Lunarch Studios. By generating game trace training data with existing state-of-the-art AI agents, we are able to use a Machine Learning (ML) approach to learn a new evaluation function. We trained several evaluation models with various parameters in order to compare prediction time with prediction accuracy. To evaluate the strength of our learned model, we ran a tournament between AI players which differ only in their state evaluation strategy. The results of this tournament demonstrate that our learned model when combined with the existing Prismata Hierarchical Portfolio Search system, produces a new AI agent which is able to defeat the previously strongest agents. A subset of the research presented in this thesis was the subject of a publication in the Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2019 Strategy Games Workshop [1]

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study. Data and code available at -- http://nlp.cs.washington.edu/triviaqa/Comment: Added references, fixed typos, minor baseline updat

Cognitive apprenticeship : teaching the craft of reading, writing, and mathtematics

Includes bibliographical references (p. 25-27)This research was supported by the National Institute of Education under Contract no. US-NIE-C-400-81-0030 and the Office of Naval Research under Contract No. N00014-85-C-002

Endogenous space in the Net era

Libre Software communities are among the most interesting and advanced socio-economic laboratories on the Net. In terms of directions of Regional Science research, this paper addresses a simple question: “Is the socio-economics of digital nets out of scope for Regional Science, or might the latter expand to a cybergeography of digitally enhanced territories ?” As for most simple questions, answers are neither so obvious nor easy. The authors start drafting one in a positive sense, focussing upon a file rouge running across the paper: endogenous spaces woven by socio-economic processes. The drafted answer declines on an Evolutionary Location Theory formulation, together with two computational modelling views. Keywords: Complex networks, Computational modelling, Economics of Internet, Endogenous spaces, Evolutionary location theory, Free or Libre Software, Path dependence, Positionality.

Drive-Based Utility-Maximizing Computer Game Non-Player Characters

This research examines the emergence of the five-string fiddle in contemporary North American fiddle culture within the past ten years. By interacting with leading artistlevel practitioners, the research documents the evolution and impact of the instrument to date in exploring the possibilities the five-string fiddle presents for musical performance and innovation. North American vernacular music and, in particular, the contemporary fiddle playing landscape, exemplifies virtousic and innovative idiomatic technique and improvisation as central to an overarching musical explosion, evidenced in the music of many high level, multi-stylistic contemporary practitioners. Within contemporary American fiddle performance, it is compelling to observe how many of the most innovative and highly regarded players now perform on five-string fiddles. The research uses a qualitative research methodology, drawing on interviews conducted with seven leading American fiddle players, each of whom has adopted the five-string fiddle in their own musical practice. The participants represent a rich cross section of American fiddle culture. They emerged naturally during the course of the literature review, and in-depth listening research, as particularly relevant sample cases. All participants were identified as leading exponents of the diversities encompassed in American fiddle music, between them sharing extensive professional recording, performance and academic experience, and all playing on five-string instruments. The research is further illuminated through practice, reflecting on my own musical work in illustrating how I have personally adopted the five-string fiddle, drawing influence from the research in demonstrating some wider possibilities of the instrument. This enquiry is important as it addresses the lack of specific research to date regarding the five-string fiddle, despite the significanance it holds for some of American fiddle music\u27s leading exponents, and consequently, for fiddle music itself. Equally significant, is the role of the instrument in facilitating the performance of innovative extended instrumental techniques, in particular, the five-string fiddles association with the rhythmic/percussive \u27chop\u27 bow techniques, now, so conspicuous within contemporary groove-based American string music. ix The findings of this research established the definitive emergence of the five-string fiddle, and subscribe that the five-string has now become a widely accepted part of the mainstream instrumentation in American music. This understanding emerges clearly through the words and practice of the participants. From this perspective, the research identifies the musical reasons that inspire the instruments popularity and elaborates through practice, the musical possibilities it presents to others. behaviour selection systems that have been used successfully in industry. The evaluations show that UDGOAP can outperform these systems in both environments. Another novel contribution of this thesis is smart ambiance. Smart ambiance is an area of space in a virtual world that holds information about the context of that space and uses this information to have non-player characters inside the space select more contextually appropriate actions. Information about the context comes from events that took place inside the smart ambiance, objects inside the smart ambiance, and the location of the smart ambiance. Smart ambiance can be used with any cost based planner. This thesis demonstrates dierent aspects of smart ambiance by causing an industry standard action planner to select more contextually appropriate behaviours than it otherwise would have without the smart ambiance