Measuring flow as concept for detecting game fun in the Pac-Man game

Affective game engines could support game development by providing specialized emotion sensing (detection), emotion modeling, emotion expression and affective behavior generation explicitly tailored towards games. In this chapter we discuss the rationale for specialized emotional appraisal engines for games, analogous to having specialized physics engines. Such engines provide basic emotion modeling capabilities to generate emotions for Non Player Characters (NPCs), just like the Havok engine provides physics-related special purpose processing. In particular, such engines provide NPCs with an emotional state by simulating the emotional meaning of an event to an NPC in the context of the game’s storyline, the NPC’s personality, and relationships with other NPCs. We discuss why such engines are needed, present an example approach based on cognitive appraisal, and show how this appraisal engine has been integrated in a wide variety of architectures for controlling NPCs. We conclude with a discussion of novel gameplays possible by the more sophisticated emotion modeling enabled by an emotion appraisal engine.

Anagram is a turn-based role-playing game where two players construct words by arranging given letters. A significant aspect of playing a game is the challenge. A good challenge comes from an opponent with a close ability. In a two-player game like Anagram, the second player can be a nonhuman player called Non-Playable Character (NPC). A balanced game is more engaging. Therefore, it is imperative to insert artificial intelligence (AI) into an NPC to make it possess a balance ability. This study investigates the AI algorithm that is the most appropriate to make a balance NPC for Anagram games. We tested three scenarios: Descending AI, Random AI, and AI with k-Nearest Neighbour (k-NN). Descending AI gets an Anagram solution by selecting a word with the highest score from all possible answers. Random AI picks a word randomly from the possible answers, while AI with k-NN chooses a word closest to one of the human players. The results show that Descending AI is the best algorithm to make the strongest NPC, which always gets the highest score, followed by Random AI and AI with k-NN. However, AI with the k-NN algorithm makes the constructed NPC has the highest number of turns at an average of 18, while Descending AI gets 14 turns and Random AI has 15 turns. Looking at the remaining lives at the end of the game, AI with k-NN makes the NPC has 25 lives left, while Descending AI has 59 lives, and Random AI has 48 lives. Less remaining lives suggest that NPC containing AI with the k-NN algorithm matches closer to the human player and therefore is more suitable for Anagram NPC.

Non-playable Character (NPC) is one of the important characters in the game. An autonomous and adaptive NPC can adjust actions with player actions and environmental conditions. To determine the actions of the NPC, the previous researchers used the Neural Network method but there were weaknesses, namely the action produced was not in accordance with the desired so the accuracy was not good. This study overcomes the problem of poor accuracy by using the Logarithmic Learning for Generalized Classifier Neural Network (L-GCNN) method with 6 input parameters, NPC health, distance from players, other NPCs involved, attack power, number of NPCs and NPC levels. While the output is to attack itself, attack in groups and move away. For testing, this study was tested on RPG games. From the results of the experiments conducted, it shows that the L-GCNN method has better accuracy than the 3 methods compared to 7% better than NN and SVM and 8% better than RBFNN because in the L-GCNN method there is an encapsulation process that is data have the same class will. Whereas the L-GCNN training time is 30% longer than the NN method because on L-GCNN one neuron consists of one data where there are fewer NNs in the hidden layer.

This paper firstly seeks an answer to the question of whether Non Player Characters (NPCs) in Computer Games can also be viewed as game agents where reactivity, autonomy, being temporarily continuous and having goal-oriented behavior are taken as the features of being a game agent. Within this scope, we will try to assess whether naming NPCs as agents point to a desire that one day they would fulfill the requirements of being an agent or whether NPCs actually already fulfill these requirements. Secondly, the paper looks into the AI needs of video games. We present the AI methodologies that are either being used and under research for use in Game AI. The same methodologies are also likely to contribute to the increasing levels of autonomy and goal-directed behavior of NPCs and help them become more agent-like.

This paper presents a system that builds on theoretical and experimental insights from linguistic pragmatics, uses novel techniques from computational linguistics and combines them with robust baseline technologies to provide intelligent Non Player Characters (NPCs), which naturally act and talk in a virtual world. Current NPCs still lack the necessary linguistic knowledge and methods to apply them to the numerous conversational application areas in virtual worlds. The system presented in this paper manages two NPCs, a barkeeper and a furniture sales agent, which highly depend on conversational abilities.

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.

Research in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further.

Game ini berjenis role playing game (RPG) dengan tema Game Pemadam Kebakaran. Dalam role skenario, terdapat dua karakter pemain, yakni human player dan komputer player. Karakter human player memiliki dua misi yaitu menyelamatkan hutan yang terbakar dengan cara memadamkan dan mengalahkan karakter komputer player menggunakan senjata air, sedangkan misi komputer player kebalikannya. Khusus untuk komputer player, karakternya bergerak secara mandiri. Karakter komputer player berupa NPC (non-player character) dimana pada permainan ini berwujud karakter monster-api. Perilaku mandiri dari NPC dikendalikan oleh algoritma yang disematkan padanya. Pada artikel ini dijelaskan implementasi metode Algoritma Genetika (GA) sebagai metode yang melatarbelakangi perubahan perilaku pada NPC bersifat acak dipengaruhi berdasarkan kondisi parameter disekitarnya. Parameter tersebut diinisialisasikan sebagai variabel dalam struktur algoritma genetiknya. Metode ini berjalan dengan memaksimalkan nilai parameter yang disimpan dalam gen pada suatu kromosom suatu individu. Pada satu individu kromosom yang dibangkitkan, terdapat empat gen seperti kemampuan menyerang (Attack), kecepatan berpindah (Movement Speed), kecepatan menembak musuh (Attack speed) dan parameter ketahanan (Health point). Perilaku NPC pada karakter monster-api tergambarkan dalam diagram finite state machine (FSM). Pada FSM tampak perilaku dalam bentuk state, event, dan action dimana kondisi dapat berubah sesuai interaksi yang terjadi dan direncanakan dalam godot scene. Hasil ujicoba pada penelitian ini menunjukkan bahwa pembangkitan populasi pada GA sampai dengan generasi ke-5 mendapatkan nilai parameter fitness 134,87. Untuk pembangkitan populasi hingga generasi ke-10, nilai fitness 128,64. Pada setiap pembangkitan generasi akan memperoleh nilai parameter fitness yang optimum ditunjukkan dengan perolehan nilai gen pada kromosom populasi mendapat nilai yang tertinggi.

Game is a form of interactivity where player and game world interact to each other. In a game, one of the elements which can be considered necessary to support the course and the reality of the game is how a NPC (Non-Player Character) in the game moves. A* (A-star) is an algorithm which can be used to perform pathfinding. In this case, A* will be used to find a shortest distance between the NPC and the player character. This research was performed to conduct experiments on the implementation of the A* algorithm in the 3D game. The research will be conducted by implementing an A* algorithm to a game, precisely to the enemy characters that exist in the game. Once the implementation was conducted, an experiment will performed using certain cases as a proving ground for the implementation. As a conclusion, from a hundred experiments, A* algorithm always can find the path for enemy to catch player and 73% of them are optimal paths.

Although barely explored so far, swarm intelligence can arguably have a profound impact on video games; for instance, as a simple yet effective approach for the realistic intelligent behavior of Non-Player Characters (NPCs). In this context, we describe the application of particle swarm optimization to the behavioral design of NPCs in a first-person shooter video game. The feasibility and performance of our method is analyzed through some computer experiments. They show that the proposed approach performs very well and can be successfully used in a fully automatic (i.e., without any human player) and efficient way.

Non-Player Characters (NPCs) are a vital part of computer games. Their behaviour is usually pre determined by the developers before a computer game is released. While these NPCs exist in a virtual environment in which they aim to achieve their objectives, these objectives might involve certain actions which should be both believable and add realism to games. In internet gameplay, these may range from navigation of the environment, attack and defense of an objective or assisting other NPCs or human players to achieve their goals. Various forms of decision making process are now implemented to provide believability and realism to games. These techniques range from reinforcement learning, imitation learning, supervised and unsupervised learning for online and offline games. While these are widely implemented, a new genre of realism is being added to games especially first person shooter games, namely Smart Terrains with virtual destructive environments. Smart Terrain has the potential to improve the realism of games and the behavior of the NPC in games such as first person shooter games where gameplay is fast paced. The potential issue would be if NPCs are dynamic enough to recognize changes in the virtual environment or if the virtual environment provides the required information to assist the NPCs with their objectives or tasks. This paper presents an overview of Smart Terrain in enhancing Online Tactic Agent decision making.

Video games are a means of entertainment for everyone, from children to adults. The genre of games now is also very diverse, ranging from adventure, puzzles to storytelling, and even many folk stories have been made into video games by several developers in Indonesia. Starting from folk tales with horror themes such as kuntilanak, legends such as cucumber mas, to folk tales that rarely sound like golden oranges. The folklore video game of buah jeruk emas is a video game that tells of a king who gets a whisper from the gods to get golden oranges. The king then held a competition to get the golden orange fruit. The player must be able to take the golden orange fruit from the enemy in the form of a Non Playable Character (NPC) who will chase the player. In making NPCs, algorithms are used to help play video games. Therefore, the author wants to apply the A * algorithm in the game of golden oranges so that npc can catch up to players according to the planned system. The main method used is A * and then the addition of the FSM method for other methods. The golden orange fruit is a video game using the A * algorithm and the FSM method after testing it can be concluded that it is enough to make the game run. With the results according to the planned system.

The paper presents an emergency situation simulation game developed at the home institution of the authors as a system of emotional agents on the Jadex platform enhanced by the JBdiEmo emotional engine, which implements the OCC emotional model. It demonstrates usability of Jadex and JBdiEmo for development of such games, explains how the player and non-playable characters are represented by emotional BDI agents, explores limits of 3D visualization support of Jadex and provides some solutions on how to overcome them.

In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as nonplayer characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, “sixth sense” knowledge of human players' whereabouts and deterministic, scripted behaviors are some of the causes of this. We propose, however, that one of the biggest indicators of nonhumanlike behavior in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and “locking on” to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable nonplayer characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.

The maximum cut (MAX-CUT) problem is to find a bipartition of the vertices in a given graph such that the number of edges with ends in different sets reaches the largest. Though, several experimental investigations have shown that evolutionary algorithms (EAs) are efficient for this NP-complete problem, there is little theoretical work about EAs on the problem. In this paper, we theoretically investigate the performance of EAs on the MAX-CUT problem. We find that both the (1+1) EA and the (1+1) EA*, two simple EAs, efficiently achieve approximation solutions of (m/2)+(1/4)s(G) and (m/2)+(1/2)(√{8m+1}-1), where m and s(G) are respectively the number of edges and the number of odd degree vertices in the input graph. We also reveal that for a given integer k the (1+1) EA* finds a cut of size at least k in expected runtime O(nm+1/δ(4k)) and a cut of size at least (m/2)+k in expected runtime O(n(2)m+1/δ((64/3)k(2))), where δ is a constant mutation probability and n is the number of vertices in the input graph. Finally, we show that the (1+1) EA and the (1+1) EA* are better than some local search algorithms in one instance, and we also show that these two simple EAs may not be efficient in another instance.