TENDENCY OF PLAYERS IS TRIAL AND ERROR: CASE STUDY OF COGNITIVE CLASSIFICATION IN THE COGNITIVE SKILL GAMES

To assess the cognitive level of player ability is difficult; many instruments are potentially biased, unreliable, and invalid test. Whereas, in serious game is important to know the cognitive level. If the cognitive level can be measured well, the mastery learning can be achieved. Mastery learning is the core of the learning process in serious game. To classify the cognitive level of players, researchers propose a Cognitive Skill Game (CSG). CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ) for optimizing the cognitive skill input classification of the player. Training data in LVQ use data observation from the teacher. Populations of cognitive skill classification in this research are pupils when playing the game. Mostly players cognitive skill game have cognitive skill category are Trial and Error. Some of them have Expert category, and a few included in the group carefully. Thus, the general level of skill of the player is still low. Untuk menilai tingkat kognitif dari kemampuan pemain sangatlah sulit; banyak instrumen yang berpotensi bias, tidak dapat diandalkan, dan merupakan tes yang tidak valid. Padahal, dalam serious game penting untuk mengetahui tingkat kognitif. Jika tingkat kognitif dapat diukur dengan baik, penguasaan pembelajaran dapat dicapai. Penguasaan belajar adalah inti dari proses belajar dalam serious game. Untuk mengklasifikasikan tingkat kognitif pemain, kami mengusulkan Cognitive Skill Game (CSG). CSG meningkatkan konsep kognitif untuk memantau bagaimana pemain berinteraksi dengan permainan. Permainan ini menggunakan Learning Vector Quantization (LVQ) untuk mengoptimalkan input klasifikasi keterampilan kognitif pemain. Data trining dalam observasi LVQ menggunakan data dari guru. Populasi klasifikasi keterampilan kognitif dalam penelitian ini adalah siswa saat memainkan permainan. Sebagian besar pemain CSG berkategori keterampilan kognitif adalah coba-coba. Beberapa dari mereka memiliki kategori Ahli, dan sedikit yang termasuk dalam kelompok hati-hati. Dengan demikian, secara umum kemampuan pemain masih rendah

Constrained Collective Movement in Human-Robot Teams

This research focuses on improving human-robot co-navigation for teams of robots and humans navigating together as a unit while accomplishing a desired task. Frequently, the team’s co-navigation is strongly influenced by a predefined Standard Operating Procedure (SOP), which acts as a high-level guide for where agents should go and what they should do. In this work, I introduce the concept of Constrained Collective Movement (CCM) of a team to describe how members of the team perform inter-team and intra-team navigation to execute a joint task while balancing environmental and application-specific constraints. This work advances robots’ abilities to participate along side humans in applications such as urban search and rescue, firefighters searching for people in a burning building, and military teams performing a building clearing operation. Incorporating robots on such teams could reduce the number of human lives put in danger while increasing the team’s ability to conduct beneficial tasks such as carrying life saving equipment to stranded people. Most previous work on generating more complex collaborative navigation for human- robot teams focuses solely on using model-based methods. These methods usually suffer from the need for hard coding the rules to follow, which can require much time and domain knowledge and can lead to unnatural behavior. This dissertation investigates merging high-level model-based knowledge representation with low-level behavior cloning to achieve CCM of a human-robot team performing collaborative co-navigation. To evaluate the approach, experiments are performed in simulation with the detail-rich game design engine Unity. Experiments show that the designed approach can learn elements of high-level behaviors with accuracies up to 88%. Additionally, the approach is shown to learn low-level robot control behaviors with accuracies up to 89%. To the best of my knowledge, this is the first attempt to blend classical AI methods with state-of-the-art machine learning methods for human-robot team collaborative co-navigation. This not only allows for better human-robot team co-navigation, but also has implications for improving other teamwork based human-robot applications such as joint manufacturing and social assistive robotics

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Towards resilient supply chain networks

In the past decade, events like 9/11 terror attacks, the recent financial crisis and other major crisis has proved that there is strong interaction and interdependency of a supply chain network with its external environments in various channels and thus a need to focus on building resiliency (in short, the ability of the system to recover from damage or disruption) of the entire network system. Although literature has discussed some way of improving resiliency of an individual firm which is a member of the network system, it lacked to capture a holistic view of the supply chain network. Pertaining to this observation, this work proposes to improve resiliency of a supply chain network from a system’s perspective rather concentrate on an individual firm. For this purpose, this thesis proposes a conceptual framework to promote early identification and timely information of the disruptions arising in a supply chain network and timely sharing of this information among all the members of the network. The key principle emphasized in this thesis is that recovery from an inevitable disruption has a better possibility if a member of the supply chain network has an early indication or knowledge of the upcoming disruption. A discrete event dynamic system simulation tool called Petri nets is utilized to realize the proposed conceptual framework. Furthermore, the practical benefits and implications of the proposed model and tool are demonstrated with help of two case studies. This thesis has several contributions to the field of operation management and supply chain. First, a new paradigm for supply chain management to avoid large scale failures such as financial crisis is available to the field, which may be applied by governments or regulatory bodies. Second, a new framework which allows for a quantitative analysis of failures of an entire supply chain network is available to the field, which is easy to be used. Third, a novel application of Petri nets to this new problem in supply chain management is available

Gamification of Cyber Security Awareness : A Systematic Review of Games

The frequency and severity of cyber-attacks have increased over the years with damaging consequences such as financial loss, reputational damage, and loss of sensitive data. Most of these attacks can be attributed to user error. To minimize these errors, cyber security awareness training is conducted to improve user awareness. Cyber security awareness training that is engaging, fun, and motivating is required to ensure that the awareness message gets through to users. Gamification is one such method by which cyber security awareness training can be made fun, engaging, and motivating. This thesis presents the state of the art of games used in cyber security awareness. In this regard, a systematic review of games following PRISMA guidelines was conducted on the relevant papers published between 2010 to 2021. The games were analyzed based on their purpose, cyber security topics taught, target audience, deployment methods, game genres implemented and learning mechanics applied. Analysis of these games revealed that cyber security awareness games are mostly deployed as computer games, targeted at the general public to create awareness in a wide range of cyber security topics. Most of the games implement the role-playing genre and apply demonstration learning mechanics to deliver their cyber security awareness message effectively

WRITING FOR EACH OTHER: DYNAMIC QUEST GENERATION USING IN SESSION PLAYER BEHAVIORS IN MMORPG

Role-playing games (RPGs) rely on interesting and varied experiences to maintain player attention. These experiences are often provided through quests, which give players tasks that are used to advance stories or events unfolding in the game. Traditional quests in video games require very specific conditions to be met, and for participating members to advance them by carrying out pre-defined actions. These types of quests are generated with perfect knowledge of the game world and are able to force desired behaviors out of the relevant non-player characters (NPCs). This becomes a major issue in massive multiplayer online (MMO) when other players can often disrupt the conditions needed for quests to unfold in a believable and immersive way, leading to the absence of a genuine multiplayer RPG experience. Our proposed solution is to dynamically create quests from real-time information on the unscripted actions of other NPCs and players in a game. This thesis shows that it is possible to create logical quests without global information knowledge, pre-defined story-trees, or prescribed player and NPC behavior. This allows players to become involved in storylines without having to perform any specific actions. Results are shown through a game scenario created from the Panoptyk Engine, a game engine in early development designed to test AI reasoning with information and the removal of the distinction between NPC and human players. We focus on quests issued by the NPC faction leaders of several in-game groups known as factions. Our generated quests are created logically from the pre-defined personality of each NPC leader, their memory of previous events, and information given to them by in-game sources. Long-spanning conflicts are seen to emerge from factions issuing quests against each other; these conflicts can be represented in a coherent narrative. A user study shows that players felt quests were logical, that players were able to recognize quests were based on events happening in the game, and that players experienced follow-up consequences from their actions in quests

A GRAPH-BASED APPROACH FOR ADAPTIVE SERIOUS GAMES

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, this research presents comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and an average 58% decrease in actions taken to complete the game