Optimising Humanness: Designing the best human-like Bot for Unreal Tournament 2004

This paper presents multiple hybridizations of the two best bots on the BotPrize 2014 competition, which sought for the best humanlike bot playing the First Person Shooter game Unreal Tournament 2004. To this aim the participants were evaluated using a Turing test in the game. The work considers MirrorBot (the winner) and NizorBot (the second) codes and combines them in two different approaches, aiming to obtain a bot able to show the best behaviour overall. There is also an evolutionary version on MirrorBot, which has been optimized by means of a Genetic Algorithm. The new and the original bots have been tested in a new, open, and public Turing test whose results show that the evolutionary version of MirrorBot apparently improves the original bot, and also that one of the novel approaches gets a good humanness level.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Computación Efímera: identificando retos para la investigación en videojuegos

La Computación Efímera (Eph-C , por sus siglas en inglés, Ephemerical Computing) es un nuevo paradigma de computación de reciente creación que pretende sacar provecho de la naturaleza pasajera (o sea, asociada a un tiempo de vida limitado) de los recursos computacionales. En este trabajo se introducirá este nuevo paradigma Eph-C de forma general, y se irá poco a poco enfocando específicamente dentro del contexto del proceso de desarrollo de videojuegos, mostrando posibles aplicaciones y beneficios dentro de las principales líneas de investigación asociadas a la creación de los mismos. Se trata de un trabajo preliminar que intenta indagar en las posibilidades de aplicar la computación efímera en la creación de productos en la industria del videojuego. Lo que presentamos aquí debe ser valorado como un trabajo preliminar que intenta a su vez servir de inspiración para otros posibles investigadores o desarrolladores de videojuegos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Imitation learning through games: theory, implementation and evaluation

Despite a history of games-based research, academia has generally regarded commercial games as a distraction from the serious business of AI, rather than as an opportunity to leverage this existing domain to the advancement of our knowledge. Similarly, the computer game industry still relies on techniques that were developed several decades ago, and has shown little interest in adopting more progressive academic approaches. In recent times, however, these attitudes have begun to change; under- and post-graduate games development courses are increasingly common, while the industry itself is slowly but surely beginning to recognise the potential offered by modern machine-learning approaches, though games which actually implement said approaches on more than a token scale remain scarce. One area which has not yet received much attention from either academia or industry is imitation learning, which seeks to expedite the learning process by exploiting data harvested from demonstrations of a given task. While substantial work has been done in developing imitation techniques for humanoid robot movement, there has been very little exploration of the challenges posed by interactive computer games. Given that such games generally encode reasoning and decision-making behaviours which are inherently more complex and potentially more interesting than limb motion data, that they often provide inbuilt facilities for recording human play, that the generation and collection of training samples is therefore far easier than in robotics, and that many games have vast pre-existing libraries of these recorded demonstrations, it is fair to say that computer games represent an extremely fertile domain for imitation learning research. In this thesis, we argue in favour of using modern, commercial computer games to study, model and reproduce humanlike behaviour. We provide an overview of the biological and robotic imitation literature as well as the current status of game AI, highlighting techniques which may be adapted for the purposes of game-based imitation. We then proceed to describe our contributions to the field of imitation learning itself, which encompass three distinct categories: theory, implementation and evaluation. We first describe the development of a fully-featured Java API - the Quake2 Agent Simulation Environment (QASE) - designed to facilitate both research and education in imitation and general machine-learning, using the game Quake 2 as a testbed. We outline our motivation for developing QASE, discussing the shortcomings of existing APIs and the steps which we have taken to circumvent them. We describe QASE’s network layer, which acts as an interface between the local AI routines and the Quake 2 server on which the game environment is maintained, before detailing the API’s agent architecture, which includes an interface to the MatLab programming environment and the ability to parse and analyse full recordings of game sessions. We conclude the chapter with a discussion of QASE’s adoption by numerous universities as both an undergraduate teaching tool and research platform. We then proceed to describe the various imitative mechanisms which we have developed using QASE and its MatLab integration facilities. We first outline a behaviour model based on a well-known psychological model of human planning. Drawing upon previous research, we also identify a set of believability criteria - elements of agent behaviour which are of particular importance in determining the “humanness” of its in-game appearance. We then detail a reinforcement-learning approach to imitating the human player’s navigation of his environment, centred upon his pursuit of items as strategic goals. In the subsequent section, we describe the integration of this strategic system with a Bayesian mechanism for the imitation of tactical and motion-modelling behaviours. Finally, we outline a model for the imitation of reactive combat behaviours; specifically, weapon-selection and aiming. Experiments are presented in each case to demonstrate the imitative mechanisms’ ability to accurately reproduce observed behaviours. Finally, we criticise the lack of any existing methodology to formally gauge the believability of game agents, and observe that the few previous attempts have been extremely ad-hoc and informal. We therefore propose a generalised approach to such testing; the Bot-Oriented Turing Test (BOTT). This takes the form of an anonymous online questionnaire, an accompanying protocol to which examiners should adhere, and the formulation of a believability index which numerically expresses each agent’s humanness as indicated by its observers, weighted by their experience and the accuracy with which the agents were identified. To both validate the survey approach and to determine the efficacy of our imitative models, we present a series of experiments which use the believability test to evaluate our own imitation agents against both human players and traditional artificial bots. We demonstrate that our imitation agents perform substantially better than even a highly-regarded rule-based agent, and indeed approach the believability of actual human players. Some suggestions for future directions in our research, as well as a broader discussion of open questions, conclude this thesis

Assessing the quality of Wikidata referencing

Wikidata is a versatile and broad-based Knowledge Graph (KG) that leverages the power of collaborative contributions via an open wiki, augmented by bot accounts, to curate the content. Wikidata represents over 102 million interlinked data entities, accompanied by over 1.4 billion statements about the items, accessible to the public via a SPARQL endpoint and diverse dump formats. The Wikidata data model enables assigning references to every single statement. While the quality of Wikidata statements has been assessed, the quality of references in this knowledge graph is not well covered in the literature. To cover the gap, we develop and implement a comprehensive referencing quality assessment framework based on Linked Data quality dimensions and criteria. We implement the objective metrics of the assessment framework as the Referencing Quality Scoring System - RQSS. RQSS provides quantified scores by which the referencing quality can be analyzed and compared. Due to the scale of Wikidata, we developed a subsetting approach to creating a comparison platform that systematically samples Wikidata. We have used both well-defined subsets and random samples to evaluate the quality of references in Wikidata using RQSS. Based on RQSS, the overall referencing quality in Wikidata subsets is 0.58 out of 1. Random subsets (representative of Wikidata) have higher overall scores than topical subsets by 0.05, with Gene Wiki having the highest scores amongst topical subsets. Regarding referencing quality dimensions, all subsets have high scores in accuracy, availability, security, and understandability, but have weaker scores in completeness, verifiability, objectivity, and versatility. RQSS scripts can be reused to monitor the referencing quality over time. The evaluation shows that RQSS is practical and provides valuable information, which can be used by Wikidata contributors and WikiProject owners to identify the referencing quality gaps. Although RQSS is developed based on the Wikidata RDF model, its referencing quality assessment framework can be generalized to any RDF KG.James Watt Scholarship fundin

Pathfinding in Computer Games

One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. This report will highlight pathfinding algorithms used presently in games and their shortcomings especially when dealing with real-time pathfinding. With the advances being made in other components, such as physics engines, it is AI that is impeding the next generation of computer games. This report will focus on how machine learning techniques such as Artificial Neural Networks and Genetic Algorithms can be used to enhance an agents ability to handle pathfinding in real-time

Statistical natural language processing methods for intelligent process automation

Nowadays, digitization is transforming the way businesses work. Recently, Artificial Intelligence (AI) techniques became an essential part of the automation of business processes: In addition to cost advantages, these techniques offer fast processing times and higher customer satisfaction rates, thus ultimately increasing sales. One of the intelligent approaches for accelerating digital transformation in companies is the Robotic Process Automation (RPA). An RPA-system is a software tool that robotizes routine and time-consuming responsibilities such as email assessment, various calculations, or creation of documents and reports (Mohanty and Vyas, 2018). Its main objective is to organize a smart workflow and therethrough to assist employees by offering them more scope for cognitively demanding and engaging work. Intelligent Process Automation (IPA) offers all these advantages as well; however, it goes beyond the RPA by adding AI components such as Machine- and Deep Learning techniques to conventional automation solutions. Previously, IPA approaches were primarily employed within the computer vision domain. However, in recent times, Natural Language Processing (NLP) became one of the potential applications for IPA as well due to its ability to understand and interpret human language. Usually, NLP methods are used to analyze large amounts of unstructured textual data and to respond to various inquiries. However, one of the central applications of NLP within the IPA domain – are conversational interfaces (e.g., chatbots, virtual agents) that are used to enable human-to-machine communication. Nowadays, conversational agents gain enormous demand due to their ability to support a large number of users simultaneously while communicating in a natural language. The implementation of a conversational agent comprises multiple stages and involves diverse types of NLP sub-tasks, starting with natural language understanding (e.g., intent recognition, named entity extraction) and going towards dialogue management (i.e., determining the next possible bots action) and response generation. Typical dialogue system for IPA purposes undertakes straightforward customer support requests (e.g., FAQs), allowing human workers to focus on more complicated inquiries. In this thesis, we are addressing two potential Intelligent Process Automation (IPA) applications and employing statistical Natural Language Processing (NLP) methods for their implementation. The first block of this thesis (Chapter 2 – Chapter 4) deals with the development of a conversational agent for IPA purposes within the e-learning domain. As already mentioned, chatbots are one of the central applications for the IPA domain since they can effectively perform time-consuming tasks while communicating in a natural language. Within this thesis, we realized the IPA conversational bot that takes care of routine and time-consuming tasks regularly performed by human tutors of an online mathematical course. This bot is deployed in a real-world setting within the OMB+ mathematical platform. Conducting experiments for this part, we observed two possibilities to build the conversational agent in industrial settings – first, with purely rule-based methods, considering the missing training data and individual aspects of the target domain (i.e., e-learning). Second, we re-implemented two of the main system components (i.e., Natural Language Understanding (NLU) and Dialogue Manager (DM) units) using the current state-of-the-art deep-learning architecture (i.e., Bidirectional Encoder Representations from Transformers (BERT)) and investigated their performance and potential use as a part of a hybrid model (i.e., containing both rule-based and machine learning methods). The second part of the thesis (Chapter 5 – Chapter 6) considers an IPA subproblem within the predictive analytics domain and addresses the task of scientific trend forecasting. Predictive analytics forecasts future outcomes based on historical and current data. Therefore, using the benefits of advanced analytics models, an organization can, for instance, reliably determine trends and emerging topics and then manipulate it while making significant business decisions (i.e., investments). In this work, we dealt with the trend detection task – specifically, we addressed the lack of publicly available benchmarks for evaluating trend detection algorithms. We assembled the benchmark for the detection of both scientific trends and downtrends (i.e., topics that become less frequent overtime). To the best of our knowledge, the task of downtrend detection has not been addressed before. The resulting benchmark is based on a collection of more than one million documents, which is among the largest that has been used for trend detection before, and therefore, offers a realistic setting for the development of trend detection algorithms.Robotergesteuerte Prozessautomatisierung (RPA) ist eine Art von Software-Bots, die manuelle menschliche Tätigkeiten wie die Eingabe von Daten in das System, die Anmeldung in Benutzerkonten oder die Ausführung einfacher, aber sich wiederholender Arbeitsabläufe nachahmt (Mohanty and Vyas, 2018). Einer der Hauptvorteile und gleichzeitig Nachteil der RPA-bots ist jedoch deren Fähigkeit, die gestellte Aufgabe punktgenau zu erfüllen. Einerseits ist ein solches System in der Lage, die Aufgabe akkurat, sorgfältig und schnell auszuführen. Andererseits ist es sehr anfällig für Veränderungen in definierten Szenarien. Da der RPA-Bot für eine bestimmte Aufgabe konzipiert ist, ist es oft nicht möglich, ihn an andere Domänen oder sogar für einfache Änderungen in einem Arbeitsablauf anzupassen (Mohanty and Vyas, 2018). Diese Unfähigkeit, sich an veränderte Bedingungen anzupassen, führte zu einem weiteren Verbesserungsbereich für RPAbots – den Intelligenten Prozessautomatisierungssystemen (IPA). IPA-Bots kombinieren RPA mit Künstlicher Intelligenz (AI) und können komplexe und kognitiv anspruchsvollere Aufgaben erfüllen, die u.A. Schlussfolgerungen und natürliches Sprachverständnis erfordern. Diese Systeme übernehmen zeitaufwändige und routinemäßige Aufgaben, ermöglichen somit einen intelligenten Arbeitsablauf und befreien Fachkräfte für die Durchführung komplizierterer Aufgaben. Bisher wurden die IPA-Techniken hauptsächlich im Bereich der Bildverarbeitung eingesetzt. In der letzten Zeit wurde die natürliche Sprachverarbeitung (NLP) jedoch auch zu einem der potenziellen Anwendungen für IPA, und zwar aufgrund von der Fähigkeit, die menschliche Sprache zu interpretieren. NLP-Methoden werden eingesetzt, um große Mengen an Textdaten zu analysieren und auf verschiedene Anfragen zu reagieren. Auch wenn die verfügbaren Daten unstrukturiert sind oder kein vordefiniertes Format haben (z.B. E-Mails), oder wenn die in einem variablen Format vorliegen (z.B. Rechnungen, juristische Dokumente), dann werden ebenfalls die NLP Techniken angewendet, um die relevanten Informationen zu extrahieren, die dann zur Lösung verschiedener Probleme verwendet werden können. NLP im Rahmen von IPA beschränkt sich jedoch nicht auf die Extraktion relevanter Daten aus Textdokumenten. Eine der zentralen Anwendungen von IPA sind Konversationsagenten, die zur Interaktion zwischen Mensch und Maschine eingesetzt werden. Konversationsagenten erfahren enorme Nachfrage, da sie in der Lage sind, eine große Anzahl von Benutzern gleichzeitig zu unterstützen, und dabei in einer natürlichen Sprache kommunizieren. Die Implementierung eines Chatsystems umfasst verschiedene Arten von NLP-Teilaufgaben, beginnend mit dem Verständnis der natürlichen Sprache (z.B. Absichtserkennung, Extraktion von Entitäten) über das Dialogmanagement (z.B. Festlegung der nächstmöglichen Bot-Aktion) bis hin zur Response-Generierung. Ein typisches Dialogsystem für IPA-Zwecke übernimmt in der Regel unkomplizierte Kundendienstanfragen (z.B. Beantwortung von FAQs), so dass sich die Mitarbeiter auf komplexere Anfragen konzentrieren können. Diese Dissertation umfasst zwei Bereiche, die durch das breitere Thema vereint sind, nämlich die Intelligente Prozessautomatisierung (IPA) unter Verwendung statistischer Methoden der natürlichen Sprachverarbeitung (NLP). Der erste Block dieser Arbeit (Kapitel 2 – Kapitel 4) befasst sich mit der Impementierung eines Konversationsagenten für IPA-Zwecke innerhalb der E-Learning-Domäne. Wie bereits erwähnt, sind Chatbots eine der zentralen Anwendungen für die IPA-Domäne, da sie zeitaufwändige Aufgaben in einer natürlichen Sprache effektiv ausführen können. Der IPA-Kommunikationsbot, der in dieser Arbeit realisiert wurde, kümmert sich ebenfalls um routinemäßige und zeitaufwändige Aufgaben, die sonst von Tutoren in einem Online-Mathematikkurs in deutscher Sprache durchgeführt werden. Dieser Bot ist in der täglichen Anwendung innerhalb der mathematischen Plattform OMB+ eingesetzt. Bei der Durchführung von Experimenten beobachteten wir zwei Möglichkeiten, den Konversationsagenten im industriellen Umfeld zu entwickeln – zunächst mit rein regelbasierten Methoden, unter Bedingungen der fehlenden Trainingsdaten und besonderer Aspekte der Zieldomäne (d.h. E-Learning). Zweitens haben wir zwei der Hauptsystemkomponenten (Sprachverständnismodul, Dialog-Manager) mit dem derzeit fortschrittlichsten Deep Learning Algorithmus reimplementiert und die Performanz dieser Komponenten untersucht. Der zweite Teil der Doktorarbeit (Kapitel 5 – Kapitel 6) betrachtet ein IPA-Problem innerhalb des Vorhersageanalytik-Bereichs. Vorhersageanalytik zielt darauf ab, Prognosen über zukünftige Ergebnisse auf der Grundlage von historischen und aktuellen Daten zu erstellen. Daher kann ein Unternehmen mit Hilfe der Vorhersagesysteme z.B. die Trends oder neu entstehende Themen zuverlässig bestimmen und diese Informationen dann bei wichtigen Geschäftsentscheidungen (z.B. Investitionen) einsetzen. In diesem Teil der Arbeit beschäftigen wir uns mit dem Teilproblem der Trendprognose – insbesondere mit dem Fehlen öffentlich zugänglicher Benchmarks für die Evaluierung von Trenderkennungsalgorithmen. Wir haben den Benchmark zusammengestellt und veröffentlicht, um sowohl Trends als auch Abwärtstrends zu erkennen. Nach unserem besten Wissen ist die Aufgabe der Abwärtstrenderkennung bisher nicht adressiert worden. Der resultierende Benchmark basiert auf einer Sammlung von mehr als einer Million Dokumente, der zu den größten gehört, die bisher für die Trenderkennung verwendet wurden, und somit einen realistischen Rahmen für die Entwicklung von Trenddetektionsalgorithmen bietet

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH

Evaluating the Effects on Monte Carlo Tree Search of Predicting Co-operative Agent Behaviour

This thesis explores the effects of including an agent-modelling strategy into Monte-Carlo Tree Search. This is to explore how the effects of such modelling might be used to increase the performance of agents in co-operative environments such as games. The research is conducted using two applications. The first is a co-operative 2-player puzzle game in which a perfect model outperforms an agent that makes the assumption the other agent plays randomly. The second application is the partially observable co-operative card game Hanabi, in which the predictor variant is able to outperform both a standard variant of MCTS and a version that assumes a fixed-strategy for the paired agents. This thesis also investigates a technique for learning player strategies off-line based on saved game logs for use in modelling

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)