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

A psychoanalyst artificial intelligence model in a computer game

Projecte realitzat en el marc d'un programa de mobilitat amb la Vienna University of Technology.[ANGLÈS] Implementation of an artificial intelligence model based on the psychoanalytic theory of the ID-Ego-SuperEgo of Sigmund Freud into the computer game Unreal Tournament 2004.[CASTELLÀ] Implementación de un modelo de inteligencia artificial basado en la teoría psicoanalítica del ID-Ego-SuperEgo de Sigmund Freud en el videojuego Unreal Tournament 2004.[CATALÀ] Implementació d'un model d'intel·ligència artificial basat en la teoria psicoanalítica de l'ID-Ego-SuperEgo de Sigmund Freud en el videojoc Unreal Tournament 2004

Pokročilé použitie ACT-R v Pogamuteiti

The requirements for virtual agents are more and more demanding. In order to manage the complex behavior of the agent, it's possible to take advantage of cognitive architectures which arised on the field neuroscience and artificial intelligence. This work examines PoJACTR library which links Pogamut library for developing intelligent agents in Unreal Tournament 2004 and jACT-R library which is Java implementation of one of the leading cognitive architectures ACTR. This work also studies certain agent implementation problems in PoJACTR and proposes a solution for them in form of debugging tools, which were subsequently implemented on an Eclipse IDE platform. In addition, it expands PoJACTR navigation and communication library modules for the game - Capture The Flag. As a validation, two agents (bots) were developed to play game, one in standard Pogamut and one in PoJACTR. When matched against each other in battle, Po- JACTR bot had comparable performance to a Pogamut bot. The results showed that debugging tools facilitated development process of PoJACTR agents.Na virtuálnych agentov sú kladené čoraz náročnejšie požiadavky. Pre riadenie komplexného správania sa agenta je možné využiť kognitívne architektúry, ktoré vznikli na rozhraní neurovied a umelej inteligencie. Táto práca sa zaoberá knižnicou PoJACTR, ktorá prepája knižnicu Pogamut pre vývoj inteligentných agentov v hre UT2004. A knižnicu jACT-R, čo je implementácia jednej z popredných kognitívnych architektúr ACT-R v jazyku Java. Práca študuje vybrané problémy implementácie agentov v PoJACTR, navrhuje ako riešenie ladiace nástroje, ktoré boli následne implementované na platforme Eclipse IDE. Okrem toho rozširuje moduly knižnice PoJACTR o navigačný, komunikačný a modul pre hru Capture The Flag. Pre validáciu sa vyvinuli dvaja agenti (boti) hrajúci zmienenú hru, jeden v štandardnom Pogamute a jeden v PoJACTR. Pri súbojoch tímov mal PoJACTR bot porovnateľný výkon ako Pogamut bot. Výsledky ukázali, že ladiace nástroje uľahčili vývoj PoJACTR agentov.Department of Software and Computer Science EducationKatedra softwaru a výuky informatikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Implementation of the Artificial Recognition System Decision Unit in a Complex Simulation Environment

[ANGLÈS] Adaptation of the decision unit "Artificial Recognition System", that was running in a simple simulator created on purpose to test the ARS decision unit into a more complex environment which is not created on purpose for the ARS. Check of the correct behaviour of the decision unit in the new environment afterwards.[CASTELLÀ] Adaptación de la unidad de decisión "Artificial Recognition System", que hasta ahora solamente funcionaba en un simulador sencillo creado a propósito para el testeo de ARS a un entorno más complejo y no creado a propósito para el ARS. Comprobación posterior del correcto funcionamiento de la unidad de decisión en el entorno nuevo.[CATALÀ] Adaptació de la unitat de decisió "Artificial Recognition System", que fins ara només treballava en un simulador senzill creat a propòsit per a testejar l'ARS a un entorn més complex i no creat a propòsit per a l'ARS. Comprovació posterior del correcte funcionament de la unitat de decisió en l'entorn nou

Pokročilé použitie ACT-R v Pogamuteiti

Na virtuálnych agentov sú kladené čoraz náročnejšie požiadavky. Pre riadenie komplexného správania sa agenta je možné využiť kognitívne architektúry, ktoré vznikli na rozhraní neurovied a umelej inteligencie. Táto práca sa zaoberá knižnicou PoJACTR, ktorá prepája knižnicu Pogamut pre vývoj inteligentných agentov v hre UT2004. A knižnicu jACT-R, čo je implementácia jednej z popredných kognitívnych architektúr ACT-R v jazyku Java. Práca študuje vybrané problémy implementácie agentov v PoJACTR, navrhuje ako riešenie ladiace nástroje, ktoré boli následne implementované na platforme Eclipse IDE. Okrem toho rozširuje moduly knižnice PoJACTR o navigačný, komunikačný a modul pre hru Capture The Flag. Pre validáciu sa vyvinuli dvaja agenti (boti) hrajúci zmienenú hru, jeden v štandardnom Pogamute a jeden v PoJACTR. Pri súbojoch tímov mal PoJACTR bot porovnateľný výkon ako Pogamut bot. Výsledky ukázali, že ladiace nástroje uľahčili vývoj PoJACTR agentov. Powered by TCPDF (www.tcpdf.org)The requirements for virtual agents are more and more demanding. In order to manage the complex behavior of the agent, it's possible to take advantage of cognitive architectures which arised on the field neuroscience and artificial intelligence. This work examines PoJACTR library which links Pogamut library for developing intelligent agents in Unreal Tournament 2004 and jACT-R library which is Java implementation of one of the leading cognitive architectures ACTR. This work also studies certain agent implementation problems in PoJACTR and proposes a solution for them in form of debugging tools, which were subsequently implemented on an Eclipse IDE platform. In addition, it expands PoJACTR navigation and communication library modules for the game - Capture The Flag. As a validation, two agents (bots) were developed to play game, one in standard Pogamut and one in PoJACTR. When matched against each other in battle, PoJACTR bot had comparable performance to a Pogamut bot. The results showed that debugging tools facilitated development process of PoJACTR agents. Powered by TCPDF (www.tcpdf.org)Department of Software and Computer Science EducationKatedra softwaru a výuky informatikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Evolving Agents using NEAT to Achieve Human-Like Play in FPS Games

Artificial agents are commonly used in games to simulate human opponents. This allows players to enjoy games without requiring them to play online or with other players locally. Basic approaches tend to suffer from being unable to adapt strategies and often perform tasks in ways very few human players could ever achieve. This detracts from the immersion or realism of the gameplay. In order to achieve more human-like play more advanced approaches are employed in order to either adapt to the player's ability level or to cause the agent to play more like a human player can or would. Utilizing artificial neural networks evolved using the NEAT methodology, we attempt to produce agents to play a FPS-style game. The goal is to see if the approach produces well-playing agents with potentially human-like behaviors. We provide a large number of sensors and motors to the neural networks of a small population learning through co-evolution. Ultimately we find that the approach has limitations and is generally too slow for practical application, but holds promise for future developments. Many extensions are presented which could improve the results and reduce training times. The agents learned to perform some basic tasks at a very rough level of skill, but were not competitive at even a beginner level

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Proceedings of the 4th Student-STAFF Research Conference 2020 School of Computer Science and Engineering SSRC2020

This volume contains the proceedings of the 4th Student-STAFF Research Conference of the School of Computer Science and Engineering (SSRC2020). This is a traditional, annual forum which brings together, for an one-day intensive programme, established and young researchers from different areas of research, doctoral researchers, postgraduate and undergraduate alumni, and covers both traditional and emerging topics, disseminates achieved results or work in progress. During informal discussions at conference sessions, the attendees share their research findings with an open audience of academics, doctoral, postgraduate and undergraduate students. The SSRCS2020 was held on-line. The specifics of this year's conference was the participation of alumni from the Informatics Institute of Technology (IIT Sri Lanka) and Westminster International University in Tashkent (WIUT, Uzbekistan). The event met great interest - it had more than 200 on-line participants, with one session accommodating the audience of 156! The presenters whether they are established researchers or just at the start of their career, not only share their work but also gain invaluable feedback during the conference sessions. Twenty one abstracts of the Proceedings contributed by the speakers at the SSRC2020 are assembled in order of their presentation at the conference. The abstracts cover a wide spectre of topics including the development of on-line knowledge and learning repositories, data analysis, applications of machine learning in fraud detection, bankruptcy prediction, patients mortality, image synthesis, graph DB, image analysis for medical diagnostics, mobile app developments, user experience design, wide area networking, adaptive agent algorithms, plagiarism detection, process mining techniques for behavioural patterns, data mining for reablement, Cloud Computing, Networking and linguistic profiling