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      Download the Game Design and Indie Game Marketing Freebook   07/19/17

      GameDev.net and CRC Press have teamed up to bring a free ebook of content curated from top titles published by CRC Press. The freebook, Practices of Game Design & Indie Game Marketing, includes chapters from The Art of Game Design: A Book of Lenses, A Practical Guide to Indie Game Marketing, and An Architectural Approach to Level Design. The GameDev.net FreeBook is relevant to game designers, developers, and those interested in learning more about the challenges in game development. We know game development can be a tough discipline and business, so we picked several chapters from CRC Press titles that we thought would be of interest to you, the GameDev.net audience, in your journey to design, develop, and market your next game. The free ebook is available through CRC Press by clicking here. The Curated Books The Art of Game Design: A Book of Lenses, Second Edition, by Jesse Schell Presents 100+ sets of questions, or different lenses, for viewing a game’s design, encompassing diverse fields such as psychology, architecture, music, film, software engineering, theme park design, mathematics, anthropology, and more. Written by one of the world's top game designers, this book describes the deepest and most fundamental principles of game design, demonstrating how tactics used in board, card, and athletic games also work in video games. It provides practical instruction on creating world-class games that will be played again and again. View it here. A Practical Guide to Indie Game Marketing, by Joel Dreskin Marketing is an essential but too frequently overlooked or minimized component of the release plan for indie games. A Practical Guide to Indie Game Marketing provides you with the tools needed to build visibility and sell your indie games. With special focus on those developers with small budgets and limited staff and resources, this book is packed with tangible recommendations and techniques that you can put to use immediately. As a seasoned professional of the indie game arena, author Joel Dreskin gives you insight into practical, real-world experiences of marketing numerous successful games and also provides stories of the failures. View it here. An Architectural Approach to Level Design This is one of the first books to integrate architectural and spatial design theory with the field of level design. The book presents architectural techniques and theories for level designers to use in their own work. It connects architecture and level design in different ways that address the practical elements of how designers construct space and the experiential elements of how and why humans interact with this space. Throughout the text, readers learn skills for spatial layout, evoking emotion through gamespaces, and creating better levels through architectural theory. View it here. Learn more and download the ebook by clicking here. Did you know? GameDev.net and CRC Press also recently teamed up to bring GDNet+ Members up to a 20% discount on all CRC Press books. Learn more about this and other benefits here.

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Found 9 results

  1. Hello Game Dev! Sorry if I'm beating a dead horse with this thread, but I'm making a fast paced action networked game, and I'm having trouble wrapping my head around some things. Namely, how should the server handle ticking player input packets that arrive at the server at an incorrect simulation steps, as a result of network lag, lag jitter, and inevitably imperfect time synchronization algorithms. I'll start by outlining my understandings, and I would be eternally grateful to anyone who can shed some light on what I'm missing. For the sake of simplicity, I'm assuming the client has synchronized to the server's clock in some respect, and bundles a "suggested simulation step" that the input ought to be executed on by the server. I've read a few threads here, and a lot of the documentation that exists out there. As far as I can tell, there are 2 main methods to rectify inputs arriving at the server at incorrect times: method 1. allow the server to rewind time and re simulate the game for late arriving input packets. This would allow late inputs to still have a chance of being simulated. The server would only allow rewinding to some maximum amount of time, to help curb cheating, and undesirably large jumps in the simulation for other players. method 2. synchronize the client's clock forward of the server's clock by half of round trip time (RTT/2), plus some constant (c) to account for jitter in lag. So in total, (RTT/2) + c. Then, when the input arrives at the server, it arrives on time most of the time. If the input arrives earlier than it needs to, it can be buffered at the server until the appropriate simulation step to execute the input. As far as I can tell, method 1 has the benefit of minimizing any sort of input buffering for players with good pings, but is a very heavy handed solution, and might cause frequent jumps in a given client's simulation. Method 2 seems to be a nice simple solution, would provide nice smooth simulation, but introduces a sizable lag for a given player's input to be seen by other players. I suppose I have 3 main questions on this topic, and I would be a very happy boy if I could get some help on them: 1. Is method 1 a widely used approach? It seems like it introduces a massive amount of work, as a result of the server and clients having to correct their simulation state, all the while introducing potentially jittery movement. 2. I get that method 2 can buffer early input packets, but how does it account for late arriving input packets? I get that the server should acknowledge the late input packet in some way to let the client know it should adjust its simulation time, but would it drop that late arrived input? would it execute it, but at the CURRENT frame? What if there are 2 or more late inputs? 3. Am I missing any other solution? Are these 2 methods the widely used methods for handling state and input on the server? I can imagine a combination solution of method 1 and method 2, is this advisable? Thanks in advance for all of your time! I feel very stuck on this, and will send all the karma your way for any help. In the mean time, I'll keep reading forum posts, to see if this has come up before. *edit* wording.
  2. Hi, community. I am writing a series of articles about Lighting related with real-time computer graphics. The purpose is to get information from a lot of great resources like computer graphics books, blogs, and forums, and try to explain them as easy and clear as I can. I am going to update the list periodically. Lighting Series The first post in the list is the following Lighting Series Part 1 - Light and Radiometry Update (2017.07.05) Lighting Series Part 2 - Radiant Energy and Radiant Power Update (2017.07.16) Lighting Series Part 3 - Radiance All suggestions for improvements, corrections, and new topics, are very welcome because in this way I am going to learn a lot and, and why not, maybe this helps anybody with the same doubts than me. Hope you find this useful!
  3. This article was first published on https://medium.com/@ricardo.valerio/make-it-difficult-not-punishing-7198334573b8 Difficulty plays a big role in keeping players motivated, engaged and, unintuitively, in marketing. Some games have it better designed than others. Some design it even to make it satisfyingly hard, like Dark Souls. But others have it poorly designed, with a very high difficult bar set from the very start, or none to little difficulty altogether, combined with too steep or too shallow difficulty increase. So how do we go about designing good difficulty? The first thing that we need to do is to define difficulty. What is difficulty? Difficulty is the amount of skill required by the player to accomplish a goal or progress through the game experience. It can be as simple as jumping from one platform to another, killing a character, defeat a boss fight — which could be designed respectively to be easy, medium or hard to accomplish. Difficulty goes hand in hand with the challenge presented and the skill of the player related to that challenge and the game. Present a too high challenge for a less skilled player, and it becomes a hard barrier from which players can turn away from; present too little challenge for high skilled players, and it won't be interesting for them. The optimal difficulty is one in which the challenge presented is always slightly greater than the skill of the player when he first encounters it, so defeating the challenge provides a climax, small or big, and satisfaction. This provides for an optimal flow state, where the player knows accomplishing the goal is possible, and investing energy will provide for a satisfying resolution. Mihaly Csikszentmihalyi, a Hungarian psychologist, proposed the following graph that defines flow as the channel where there is an optimal level of skill vs. challenge. Difficulty is the amount of skill required by the player to accomplish a goal or progress through the game. Later on, he proposes a more detailed mental map, matching certain areas of skill and challenge to specific mental states. Image from https://en.wikipedia.org/wiki/Flow_(psychology) As we can see here, a difficult challenge for an average skilled player will at first provoke arousal — the player knows it’s hard right now, but it is within his grasp, and he only needs to become a little more skilled, or perhaps acquire an ability that is key for the challenge, and become skilled at it. But eventually, a player will be in full control of the challenge, as riding a bike. And over time, it will become a somewhat relaxing, or boring, activity. So it’s important that to keep players engaged, more difficult challenges must be presented, or alternatively, different ways of accomplishing goals and avoiding having a single solution for resolving them. So if the player does defeat a challenge, it is always possible to increase their mastery by going back and trying to make it better. And this leads us to motivation. Motivation So what drives someone to perform an activity? We can separate motivations into two categories: intrinsic and extrinsic . When someone is intrinsically motivated, he performs the activity because he likes it, it is satisfying, regardless. An extrinsically motivated person, however, will perform for an external reward — praise, fame, an item, an achievement. Because of this, an extrinsically motivated player needs external factors, but on the other hand, an intrinsically motivated player has his own flame of desire to perform a task. This is important to understand because whenever possible, the primary motivations that should be focused on are the intrinsic ones. This leads us to Self-Determination Theory. The theory states that humans have three fundamental needs, as described in its Wikipedia page: Autonomy, or “to be causal agents of one’s own life and act in harmony with one’s integrated self” Competence, or “seek to control the outcome and experience mastery” Relatedness, or “ the universal want to interact, be connected to, and experience caring for others” Directly connected to skill is competence. It is important for the player to feel he can use his mastery and feel he can control the outcome. Designing good difficulty We know that difficulty will derive from the challenge presented and the player’s skill. Skill is a combination of mental and physical effort and capabilities, which culminate in mastery. And players can vary heavily in skill, depending on their previous experiences, motor skills, cognitive capabilities in the game context, etc. But on the other hand, challenge is easier to define and can be designed to require an increasing amount of mastery. What is a good challenge? The problem mostly lays in defining good challenges. Consider these two boss fights: The boss will randomly kill a player every 5 to 10 seconds without any kind of warning The boss will every 10 seconds place a deadly void zone on the player’s feet, that will explode after 3 seconds and kill anyone on top of it The first one is very punishing, and there is nothing the player can do about it, no matter how much skill he has and earns. But if we look at the second boss, we have many elements that provide a great challenge and promote learning: There are cues The player can react to it There is feedback The player can, therefore, increase his skill in dealing with it Consider riding a bicycle. First times, we are clumsy, lose balance, go slowly. Over time, we focus rather more on the environment than the process of riding the bike itself, because we already are familiar with it, we learned the required techniques for each activity, such as pedalling, balancing, braking, turning, and our skill is high. Same with video games. A good activity should be learnable, to such an extent that it can become mostly automatic, so we can focus on reacting to the environment. A good challenge should present cues, allow a reaction and provide feedback, so when the player fails, he will feel that he could have done better. This will foster learning and increase of skill. If we look at any boss fight in World of Warcraft, we can see they are carefully designed to always provide cues, feedback and allow the player to react. All the mechanics are carefully set in the fight to make it possible to accomplish without it being overly frustrating. The goal is always within reach, and practice takes the player closer to defeating it. Difficult raid bosses have a set of abilities, and sometimes phases in the fight, that as they are defeated get the raiding team steps closer to defeating the challenge. This has a potent effect because as players get closer to their goal, tension rises and builds towards a climax. And after hitting the climax, it provides a great deal of satisfaction — even euphoria and shouting, I have witnessed it first hand with my own guild. A challenge doesn’t need to be solved in the first attempts. But there should be a sense of progress. Even in games as difficult as Super Meat Boy, where a player dies multiple times before clearing a level, players remain engaged. There are clear visual cues of the threats, there is a great deal of feedback, and after death, the player character respawns quickly. With each attempt, the player learns what works better. Perhaps timing, or jumping closer or farther from an edge, etc. When Flappy Bird went viral in 2014, players were raging with the difficulty of the game. A quick search reveals a plethora of videos with raging players. And yet, it went viral, and people were relentlessly trying to get better at it. The fact that cues were presented and the player could react means that there is a chance to become skilled. And it is in fact so hard, that just passing a few pipes can be a reason for bragging and a great sense of one’s growing mastery. Artificial vs Designed difficulty We can add difficulty in two ways: artificial or designed. Designed difficulty is when you design a boss with a certain set of abilities, perhaps adding or removing depending if you are doing a raid with 10 or 25 players, or a hard mode. It is difficulty which requires learning new skills or using them in a certain way, as opposed to just performing better with existing ones. Generally speaking, artificial difficulty is about changing stats. Designed difficulty is about introducing or combining different mechanics, which force the player to learn and master specific skills. Examples of artificial difficulty are increasing health, defences, attacks, number of enemy characters or reduce the time limit if it exists. Examples of designed difficulty are the boss getting a new ability, different kinds of enemies joining the fight or requiring coordination with other players. Artificial difficulty is cheap. It’s easier to tweak than designed difficulty. But at the same time, it might feel cheap for the player. If by going hard mode only changes stats, then the player won't get anything new out of it — unless it challenges his assumptions and forces him to be creative or rethink his strategy. For example, a boss that beats much harder and for longer periods might force the players to rotate their survival abilities carefully; and in team efforts, it might even lead them to coordinate survival abilities. To make a point, imagine a game where you have only one single enemy character that appears with increasing amounts of health and attack. Now imagine one where the enemy characters vary in abilities. The first would become boring faster, and the second one has more chances for using skills differently. As with everything, moderation is key. Use both and combine them to present the best challenge for the player. But it’s not just boss fights While boss fights are in many games a point of climax, and therefore usually are taken more seriously when it comes to difficulty, it doesn’t stop there — in fact, it doesn’t even start there. Setting the difficulty starts right from the beginning of the game. As simple as learning movements, learning to jump, use a skill. The initial game experience of the player is crucial to sparking the desire to play and keep playing. In Super Mario Bros, the player starts with an empty scenario and is left with the only option which is to move. Then he is presented with a cube with an interrogation mark, which will likely trigger curiosity and try to interact with it. Soon after, the first NPC appears, he looks angry and moves towards the player — there are signs he is a threat, and the player needs to react to it. If he touches it, Mario will die — feedback — and the player learned he can’t touch them by walking to them — he has to try something else. The most likely step afterwards will be to try to avoid or jump on top of it, as to smash it, and there will be feedback that the player successfully defeated the challenge presented — his skill level now allows him to deal with these type of NPCs successfully. From the start, the player feels he is learning. Wrapping it up When designing good challenges, it is important that the player is able to learn from it and increase his mastery. In order to do that, we should present cues, visual, audio, vibration, etc, that signal the player something is about to happen. This creates an opportunity to react, and eventually prove one’s mastery. And finally, there should be feedback. With each attempt, the player will become more skilled, defeat the challenge and become satisfied. If the player fails, you want to have him feel there was something better he could have done, and not leave him frustrated and helpless. Avoid having single solutions to defeating a challenge and let play sessions vary — this allows the player to get better, as opposed to a static solution. Make it a difficult challenge but within reach. And avoid making it punishing. Conclusion We went through a definition of difficulty, what drives players, and what is an optimal difficulty level that promotes flow. Allowing the player to learn is fundamental, which ultimately drives progress, and gives a sense of competence and autonomy. And in the end, players will want to seek more challenges. So when it comes to difficulty, make it hard, but not punishing. Stay tuned Stay tuned for follow-ups on this series on game design, where I will explore other aspects that help to deliver a great experience to players and create great games. Follow me on Medium If you liked this article, please upvote or leave review or comment to let me and others know you found this useful, I would be very happy and greatly appreciate it. Thanks!
  4. https://sitavriend.wordpress.com/2017/07/03/how-your-players-thinking-develop-throughout-their-lives/ Developmental psychology is the study of how our cognitive processes change throughout our lives. The field used to focus on children and how their cognitive abilities develop, but nowadays it is understood that we keep developing throughout our lives. This field of psychology might be one of the most interesting for game design. It can help you as a game designer understand players, how they think and what is challenging for them. Developmental psychology started when psychologist began studying children and saw how their cognitive abilities were different from adults. Piaget was one of those psychologists, he proposed a stage theory based on his findings. His stage theory basically means that our cognitive abilities develop according to distinct stages. Children don’t understand object permanence until 1 or 2. Object permanence is the idea that objects still exist when they are hidden from view. That is why it’s so much fun to play peek-a-boo with little babies. They are genuinely surprised when they see you again. Conservation is understood around the age of 6. Children will start to understand that something doesn’t just magically become more because you manipulated it. For example: give a 4-year-old 1 cookie and give yourself 2 cookies. When you ask the child whether you fairly devided the cookies, he’ll say no. Then you take his cookie, break it in half. Now when you ask the child if you fairly devided the cookies he’ll say: “Yes, because we both have 2 cookies now”. Children before the age of 6 can’t make a distinction between appearance and reality yet (De Vries, 1969). Piaget’s experiment for testing if a child understand conservation are a little bit mean but also quite funny (and super cute): https://www.youtube.com/watch?v=gnArvcWaH6I. Children aren’t able to take someone else’s point of view until they are around the age of 6 or 7. Piaget tested this ability in children with the picture story of Sally and Anne you see below. A child who says Sally should look in the box isn’t yet able to take someone else’s perspective. These children have trouble forming empathy as well, they don’t realize yet that other people have feelings too. Now you understand that creating a cooperative game for kids below the age of 6 might not be a good idea. Logical and hypothetical thinking develops around the age of 11 to 12. That is why children below 11 often have a hard time understanding games like chess or checkers. 11 or 12 is also the age children start to understand the idea of reversibility. Before this age, children don’t understand that numbers or object can be changed or returned to their original condition. Children don’t get that when their favorite ball isn’t broken when it is deflated and that it can be filled up again. The ability to think abstractly and hypothetically doesn’t develop until the age of 12 or later. However, it’s good to realize that cognitive development is personal. Some children’s cognitive abilities develop quicker than others. But these ages can be good guidelines when you design a game for kids. As we age we change physically, we get a wrinkle and some grey hair but that’s just the outside. Our brain also changes as well, whether we like it or not. As we age, our reaction time declines and the same happens to our problem solving abilities (Ornstein & Light, 2010; Freedman et all., 2001). Around your 40s or 50s it starts becoming more difficult to learn something new like a new skill. That’s why your granny still doesn’t understand how to use her smart phone even though you’ve explained it a thousand times. You can understand why many older people don’t like to play hardcore games. Beyond your 40s or 50s you have to invest a lot more time to learn how to play the game. This doesn’t even take into account that many elderly people were already older than 40 or 50 when PC’s and game consoles became popular. Your memory declines with age as well. The decline actually already starts in your 20s (Park & Reuter-Lorenz, 2009). But it’s not all downhill from your 20s onwards. As you age, knowledge based on facts increases. You get exposed to facts every day, you will pick information up along the way. That’s probably why your granny likes the daily crossword puzzle from the newspaper and why Wordfeud is still popular with older people. The ‘Tovertafel’ I’d like to talk about a console instead of a game this time: the ‘tovertafel’ (translates to magic table). ‘The tovertafel’ is an interactive projector that was first developed for elderly people with dementia (https://tovertafel.nl/). People can play games together on the ‘tovertafel’. Several games are included that can be played by anyone who suffers from dementia. Players interact with the ‘tovertafel’ by touching the projections on the table. Because of direct manipulation, there is no need for players to learn something new. Players can just touch a projection and see what happens. All the games that come with the ‘tovertafel’ are based on metaphors and knowledge older people remember from when they were younger such as a jigsaw puzzle, a game with flowers and a proverbs game. While some games require a bit of knowledge based on facts others games are more like play, so there is no need for a perfect memory. None of the games require a good reaction time from the player, they can interact with the game whenever they feel like. Tips and suggestions when designing games for older people (40+) Use metaphors, something the players are already familiar with. This can be anything from things they know from their youth to things they are familiar with on a daily basis. Direct manipulation interfaces such as touch screen are always a good idea. Touch screens allow you to design interactions players are used to from every day interaction such as touching or dragging. Don’t forget to keep the player’s reaction time in mind as well. Allow the player to interact with the game at their own speed. Dr. Panda’s bath time Dr. Panda makes games kids can play on a Ipad, Iphone or Android. One of their games is called dr. panda bath time (https://drpanda.com/games/dr-panda-bath-time). It is a semi-educational game that teaches children about hygiene habits while also being fun. Children use direct manipulation to drag the characters and things around. The controls are very similar to what kids are used to on a daily basis. Players cannot make mistakes in the game, they are not punished. The game is ideal for children who don’t yet understand conservation. Children do not need to be familiar with abstract or hypothetical thinking. The game doesn’t have a score system and kids can just try things to see what happens. Tips and suggestions when designing for children Metaphors are not just useful for older people, kids can understand them as well. Just make sure that you use metaphors kids use on a daily basis. For small children, touch interfaces would be the way to go. Direct manipulation doesn’t require the abstract thinking a mouse or keyboard would. It might seem simple to link a character’s movement to something like a mouse, but it already requires more abstract and hypothetical thinking. Since conservation isn’t developed until 6, it’s good practice to keep the mistakes a player can make to a minimum. It would be even better when every interaction a player makes is either right or neutral. Keep your age-range small when designing kids games. Children’s cognitive and mental abilities develop very quickly. References and stuff Paiget’s tasks for kids: https://www.youtube.com/watch?v=gnArvcWaH6I Ornstein, P. A., & Light, L. L. (2010). Memory development across the life span.The handbook of life-span development. Freedman, V. A., Aykan, H., & Martin, L. G. (2001). Aggregate changes in severe cognitive impairment among older Americans: 1993 and 1998.The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 56(2), S100-S111.Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annual review of psychology, 60, 173-196. Müller, U., & Racine, T. P. (2010). The development of representation and concepts.The handbook of life-span development. De Vries, R. (1969). Constancy of generic identity in the years three to six.Monographs of the society for research in child development, 34(3), iii-67. Bialystok, E., & Craik, F. I. (2010). Structure and Process in Life‐Span Cognitive Development.The handbook of life-span development.Kesselring, T., & Müller, U. (2011). The concept of egocentrism in the context of Piaget’s theory. New Ideas in Psychology, 29(3), 327-345. Phillips, J. L. (1975).The origins of intellect: Piaget’s theory (Vol. 1).
  5. What follows below is an email thread from July 1993 on AI in Empire-Based Games between Amit Patel, Markus Stenberg, Kevin (?), and Rob (?). AI In Empire-Based Games Courtesy of Amit Patel http://www-cs-students.stanford.edu/~amitp From: Free at last! To: patel@shell.com Subject: RE: Space Empire Games Date: Fri, 23 Jul 93 16:22:48 EDT Amit - I just wrote up a bit of a description about Second Conflict for another correpondant. I append it here. Its certainly what I consider Space Empire. I'm trying to clone Second Conflict which is what my editorial or side comments on extensions or computer strategies refer to. Thoughts and comments are appreciated. I'm still somewhat overwhelmed by the thought of programming a decent computer opponent. Thanks! Kevin ------------------- Second Conflict appears to be produced by the folks who run the Galactica BBS. It has multi-player capability [possibly for BBS users?] but I've never tried that, playing purely Human-Computer conflicts. I can't really remember the premise, so I'll just talk about the mechanics [which is what I'm trying to clone right now, I'll figure out my own premise later]. You start by selecting the game parameters. Up to 26 star systems (one per alphabet letter) and up to 10 players. Each player gets a beginning star system with 10 planets [each planet produces troops], a random number of Warships, Stealthships and Transports, a certain number of Missiles and Factories, and a random number of system defenses. Two basic scenarios are available with the shareware version of Second Conflict. The first is that each player gets one system. The second is that all systems are divided evenly between all players. The winner is the players who conquers the whole universe or who has the largest number of points when the game ends [game length can be selected]. You can choose to build any of the ships, defenses, or factories. You can send scouts to check out other systems. Each turn every player makes orders (produce X, send ships to system Y, etc) and then all movement and combat orders are reconciled at the end of the turn. Stealthships are more powerful but cost 3 points to build versus 1 point for Warships. Fleets can 'Conquer' (fight until win or die), 'Probe' (attack once then retreat), or 'Raid' (seize transports and/or build points from enemy systems). Items can be wrecked to retrieve 70% of original points. Score points are awarded for ships, star systems, planets, troops belonging to a player at the end of the turn. More points for star systems owned, followed by planets, and then ships/defenses/factories. [So an obvious decision weight factor comes to mind: conquering a system is higher priority then building more ships unless you've got lots of ships]. In an enemy system, one must first destroy the protecting fleet/defenses. Then you must destroy the enemy troops occupying the planets. Every turn you have un-conquered planets, the enemy can destroy your ships, possibly reducing the occupation fleet small enough so the system overthrows your rule. So a typical game for me starts out scouting nearby systems while building up my fleet. I try to find the nearest 'neutral' (non-player-occupied) system that has high defenses (usually an indication of a large number of existing factories; since it costs 5 points per *existing* factory to build a new one, the more already in a system, the better). Or if there are any nearby enemy systems I send raid fleets to get points to build with [the player's home system has no production limit; that is if you have extra points you can build as many of X with those points as you can, whereas other systems can only build as many X as they have factories]. One of the tricks the computer opponent might do is to wreck factories to build stealthships. Since production in the home system is not limited by number of factories, 1 factory can build several hundred stealthships from the points recovered by wrecking the other factories. Then the computer can easily conquer several nearby systems, and use those systems' factories to build. The computer opponent only seems to do this early in the game if there are lots of nearby neutral systems. I haven't decided why the opponent decides to wreck factories later in the game. A weakness of the computer opponent is to send most of the fleet to attack a new system, leaving an old system relatively unprotected. If the computer has a small enough fleet, its possible to occupy the old system with little fear of successful return take-over. There are some other parts of the game, but thats pretty much it in a nutshell. The authors have produced a windows version that has different rules for some of the above (eg its harder to raid). Part of my motivation for making my own version is that I think their Windows interface is a dog, I'd like to learn, and the version I have has some annoying bugs (like with a large game [26 star systems, 5 players] the game will tend to have field overwrite problems, so that all of a sudden one player has got -32000 ships and is completely unconquerable). Some potential additions include having systems that are rich in metals versus good crop planets, taking the time to mine planets, colonization versus conquest, spy satellites, more ship types, trade, diplomacy, etc. But I'd like to get my clone working first and then extend it. From: Free at last! To: patel@shell.com, robert@gtx.com Subject: Medieval SimCity Date: Fri, 23 Jul 93 16:46:43 EDT Amit, someone posted this response, which I think pretty much echos your comments re: realism. >One tip for the Medieval Sim-City game... Ditch realism (or at least some of >it) and invent something that will work well from a game balance perspective >and make the game fun to play. Reality can be a good source of game ideas >sometimes, other times it can be crippling. Its sort of sinking in for me that I do need to concentrate a little more on playability rather than strict realism, although history provides a number of ideas that can be incorporated into a game. You mentioned for example that Civilization has emporers living 4500 years! This just so happens to be something I was having a little trouble figuring out how to handle the transition as the current ruler died, like what happens if the ruler doesn't produce an heir? (or multiple?) But it could just be ignored if necessary. Rob, you wrote: >I really like the idea of a leader and his group getting dumped in the middle of nowhere. The basic premise I was working from was the fact that around 600-700 A.D. large numbers of new villages were founded in portions of Northern Europe that had not been extensively settled. At that time, most of N. Europe was forested. What I hadn't figured out was how to explain how the potential villagers got there without a path or road, but I shrugged that off for the moment. This causes the game to start out to be one of resource management as the village must work to create fields using existing grain resources. Then, as exploration takes place, they will encounter traders and other villages, heathen and bandits, etc. The goal, in my mind, is to start with nothing like this, and develop to a successful large city [possibly the political center of a new country or a bishopric]. Obstacles include the barbarian invasions, trade wars, the black death, and the constant war. >Have you considered hunting & gathering as a potentially >bountiful resource for small populations, as a springboard until farming >begins to yield its returns? Yes, as pigs were typically fed on wild acorns as a major staple, and hunting was a significant contributor of food. I wanted to get the grain plant/yield ratios settled first but maybe I'm trying to take baby steps that are too small. I may be trying to make things too complicated by assigning different activities different costs in terms of grain eaten. I originally thought to require the user to select which activities to use the people on. For example, with 25 starting people they can plow 5 fields in a season if no one does anything else, but they may end up with no food because they eat all the grain. On the other hand, 5 people can build houses, 10 people can hunt, and 15 people can plow, resulting in 3 fields but enough food from hunting, _and_ a place to stay. >If i recall, farming without basic tools is supposed to be hardly worth >the effort (unless you live on a flood plain as rich as the ancient Nile). >Likewise, great skill in farming, even with relatively primitive tools, >can reap rewards. Right. The original plow (pre-700 A.D) was an ox-drawn edition of the original stick plow. Ideal for light Mediterranean soils but poor in the heavier soils of N. Europe. I wanted the village to start out with this plow (and consequently lower grain yields) plus a two-field rotation system. Then, after the development of the shoulder-harness for horses, horse shoes and the heavy mouldboard plow circa 750 AD, the village can acquire this knowledge and increase production. The additional use of a three-field rotation system can also increase production. With increased production the player can then spend more resources for building a church, grain mills, windmills, trade, and developing more of a city. Perhaps I'm trying to be too realistic, making it much less fun? Kevin From: Free at last! To: fingon@nullnet.fi Subject: RE: Space Strategy AI Date: Mon, 26 Jul 93 11:12:55 EDT Markus - >I, too, am creating space strategy game. Only part working 100% now is computer AI, Care to share details? I'm rather lost when it comes to the AI part. Both Amit Patel and Robert Eaglestone have expressed interest or ideas wrt the AI. At this point I've done nothing on the AI (leave the hard part for last . I haven't even thought much on the potential computer operations, much less how the computer makes decisions between them [nor even how the computer gathers the data to make the decisions, but that should be easier]. Is your AI data-driven? What computer operations/decision-points do you have, and how does the computer decide between them? Don't feel that you have to give everything away, any input at all would be helpful at this point. Thanks! Kevin From: Free at last! To: robert@gtx.com, patel@shell.com Cc: routley@4gl.enet.dec.com Subject: Promised infrom from Markus Stenberg on Computer AI Date: Wed, 28 Jul 93 16:57:34 EDT From: US2RMC::"fingon@nullnet.fi" "Markus Stenberg" 27-JUL-1993 15:07:29.00 To: 4gl::routley (Free at last!) CC: Subj: Re: Space Strategy AI > >I, too, am creating space strategy game. Only part working 100% now is > >computer AI, . > Care to share details? I'm rather lost when it comes to the AI part. > Both Amit Patel and Robert Eaglestone have expressed interest or ideas > wrt the AI. I'll write something.. > At this point I've done nothing on the AI (leave the hard part for last . > I haven't even thought much on the potential computer operations, much > less how the computer makes decisions between them [nor even how the > computer gathers the data to make the decisions, but that should be easier]. Data gathering is simple - at least in my model it uses same data as players + some statistical data from preivious turns.. > Is your AI data-driven? What computer operations/decision-points do you > have, and how does the computer decide between them? Don't feel that you > have to give everything away, any input at all would be helpful at this > point. AI I have designed uses mostly data to make decisions - some random chance has been thrown in, too. I think that the AI has to be quite game-specific - at least mine wouldn't work even in VGAPlanets, which is _very_ like my game.. For example about planetary conquest: Computer saves all previous attempts, &c. When ship's turn comes (it handles em quite easily), it checks out 20 nearest not-own planets, and what kind of success it has had before trying to conquer em. Then it orders the ship to go to the planet &c. L8er, if some other ship thinks that the same planet is easiest conquest in terms of range/defense, it merges them to fleet before attacking. If planetary defenses were last time better than the fleet, it just flies to the system & waits until there is great enough force to wipe out the planet. (Planetary defenses cannot attack, as name implies) -- Markus Stenberg / fingon@nullnet.fi / Finland / Europe Public PGP key available on request.
  6. So - the last couple of weeks I have been working on building a framework for some AI. In a game like the one I'm building, this is rather important. I estimate 40% of my time is gonna go into the AI. What I want is a hunting game, where the AI learns from the players behaviour. This is actually what is gonna make the game fun to play. This will require some learning from the creatures that the player hunt and some collective intelligence per species. Since I am not going to spend oceans of Time creating dialogue, tons of cut-scenes and an epic story-line and multiple levels (I can't make something interesting enough to make it worth the time - I need more man-power for that), what I can do, is create some interesting AI and the feeling of being an actual hunter, that has to depend on analysis of the animals and experimentation on where to attack from. SO.. To make it as generic as possible, I mediated everything, using as many interfaces a possible for the system. You can see the general system here in the UML diagram. I customized it for Unity so that it is required to add all the scripts to GameObjects in the game world. This gives a better overview, but requires some setup - not that bothersome. If you add some simple Game Objects and some colors, it could look like this in Unity3D: Now, this system works beautifully. The abstraction of the Animation Controller and Movement Controller assumes some standard stuff that applies for all creatures. For example that they all can move, have eating-, sleeping and drinking animations, and have a PathFinder script attached somewhere in the hierarchy. It's very generic and easy to customize. At some point I'll upload a video of the flocking behavior and general behavior of this creature. For now, I'm gonna concentrate on finishing the Player model, creating a partitioned terrain for everything to exist in. Finally and equally important, I have to design a learning system for all the creatures. This will be integrated into the Brain of all the creatures, but I might separate the collective intelligence between the species. It's taking shape, but I still have a lot of modelling to do, generating terrain and modelling/generating trees and vegetation. Thanks for reading, Alpha-
  7. https://sitavriend.wordpress.com/2017/05/15/reactance-theory/ You can make something more desirable by forbidding it. That something can be anything: an item, an action, an idea. Well this is possible and known as the reactance theory. Reactance is the feeling you get when someone limits your freedom or option. Basically when you’re not allowed to do something or when you are told you have to do something. This feeling results in you: 1. Wanting forbidden option even more. 2. Trying to reclaim your lost option. 3. Experiencing aggressive and angry feelings towards the person (this person may be fictional as well, or and AI) who limited your options or freedom in the first place. (These feelings can be very subtle and barely noticeable but motivate you to do the opposite from what you have been told to do.) The first scientist to talk about the idea reactance was Brehm in a theory of psychological reactance. He was the first to research the reactance theory and explains reactance as a motivational state people experience when their freedom is removed or threatened (1966). But you probably already know the reactance theory as reverse psychology. And that’s what reactance basically comes down to: Getting people to do something by telling them they are not allowed to do that something or the other way around. Unfortunately, it doesn’t always work. Some people are just not as sensitive to experience reactance as others and circumstances matter too. For instance: reactance breaks down when people can rationalize why they shouldn’t do something. If someone told you not to buy the bag you really wanted, you’d probably buy the bag anyway. But if that someone explained that he bought the same bag and it broke after 2 days, you’d probably think twice before buying the bag. Portal 2 applies the idea of reactance brilliantly in their level design when the player enters Aperture’s dungeons. Along the way back up, the player encounters several warning messages as you can see in the picture below: “warning”, “do not enter”. Of course these warnings are not to discourage the player, they are meant to lore the player closer. Reactance helps the storyline feel less linear than it actually is. Player is more attracted to this option and goes on to explore it. It also guides the player through the level more naturally because they want to explore this forbidden option rather than going somewhere else. You probably want to know what’s behind those walls The Stanley parable applied the reactance theory to their gameplay using narrative. The player is encouraged to try all storylines since the end is never the end in the game. In fact, the game is all about discovering new endings and alternative storylines and that means you don’t want listen to the narrator most of the time. The blue door ending is a great example of this: The narrator tells Stanley to walk through the red door when the player approaches a room with a red and blue door. When you ignore the narrator and walk through the blue door, he’ll send the player back and tells Stanley to walk through the red door again. The blue door becomes a more attractive option now, so the player choices the blue door again. The player will be send back to choose the red door again but this time the blue door is moved behind the player and the narrator stresses Stanley he has to walk through the red door. The blue door has never been a more attractive option. Such an attractive blue door! Look at those curves! The reactance theory can easily be applied to your own games. It can help you design interesting levels or create interesting narrative for games that rely on (branching) narration. When you want to implement the idea of reactance into your own game you can make something more desirable by forbidding it or you can make something less attractive by forcing it. This something can be anything: an item, a choice you want the player to make, a path the player should walk, an action you want the player to perform. Be creative! Keep in mind that not everybody is equally sensitive to reactance and that the effect breaks down when the player can rationalize why they shouldn’t do something. Here are some ideas for you. Level design: – Use some art! Show something is dangerous or advise the player not to go there with signs or writing on the walls. Doesn’t have to be art-heavy, just tell them a certain area is closed off and that they are not allowed to enter. Narration games: – Somewhere in the narrative you can tell the player they are not allowed to make a certain choice (remember: don’t explain why). You can also “force” players to make a certain decision like the red door in the Stanley parable. – Empower the player by telling them they aren’t good enough to do something, they will do it. – Tell the player that he/she has to do something a certain way, they will do the opposite. Items: – Tell your player is a forbidden item and they shouldn’t take it. Want to read more (scientific) stuff on the reactance theory? Brehm, J. W. (1966). A theory of psychological reactance. London: Academic Press. Jack W. Brehm (1989) ,”Psychological Reactance: Theory and Applications”, in NA – Advances in Consumer Research Volume 16, eds. Thomas K. Srull, Provo, UT : Association for Consumer Research, Pages: 72-75. https://books.google.nl/books?hl=nl&lr=&id=gd4iAQAAQBAJ&oi=fnd&pg=PT317&dq=reactance+proneness&ots=RSjeInAUj2&sig=xBekeKqXAkdk5JPYckJvlgZkDdQ#v=onepage&q&f=false
  8. https://sitavriend.wordpress.com/2017/05/29/the-stiking-difference-between-liking-and-wanting/ There are two different kinds of pleasures we experience every day, we have anticipatory pleasure or ‘wanting’ and consummatory pleasure or ‘liking’. ‘Wanting’ is pleasure for looking forward to future events. On the other hand we have ‘liking’, this is pleasure for things in the moment. Think of it this way: when you play a game right now and enjoying it, you experience consummatory pleasure (liking). You might experience anticipatory pleasure when you are at your day job or school but can’t wait to be home this evening so you can play your favorite game. It might surprise you, it certainly surprised me, but these two pleasures are very different from each other and even have their own neural system in the brain. This means that according to your brain, liking and wanting aren’t the same thing. The wanting-type pleasure relies of the dopamine system. Dopamine is released each time you’re looking forwards to something you enjoy. The liking-type pleasure relies on your reward-driven system. When you do something you enjoy doing, opiates such as endorphins are released as a reward. These chemicals of the brain make you feel good. While wanting and liking are very different, it’s good to realize that you have to like or enjoy the thing at first before the wanting system for that same thing kicks in. However, you can have liking without wanting and wanting without liking. Think about a party you are dreading to go to. You really don’t ‘want’ to go but you know that you will ‘like’ being there once you get to the party. Addiction is probably the best example of wanting without liking. An addict will ‘want’ his drug but he doesn’t ‘like’ the effect of the drug anymore. So be careful with too much wanting though, this can create addiction (Berridge & Robinson, 1998). I realize it’s an ethical debate whether you as a designer are responsible for a player being addicted to your game. In most cases you simply want people to enjoy your game on a regular basis and a healthy player shouldn’t become seriously addicted (where gaming becomes a problem for their daily lives). While not everyone is equally susceptible for addiction it’s important never to design for it. The difference between ‘liking’ and ‘wanting’ doesn’t seem to be very logical and not much research has been done. It’s only logical that I couldn’t find many games that apply this theory. The closest application of the wanting-system to a game I could find was It takes forever before I can play again! Candy Crush. Candy Crush and other similar mobile games want their players to come back every day. The design of these games is driven by retention and that’s why they often have a lives-system and short levels. The short levels encourage the player to try another level. Once the player fails too many levels and runs out of lives, he or she has to wait before they are restored. Most games with such a lives-system have cycles of about 20 hours. This means that if a player runs out of lives, it takes about 20 hours for all lives to be fully restored. Both the wanting- and liking-systems can be applied to all types of games. However, mobile games can probably benefit most from these different neural-systems. Chances are that you aim for high retention when you design a mobile game. The wanting system is important here, your players should look forward to play your game every day. And of course they should ‘like’ playing your game as well, especially the first time they play. Games with micromanagement can also benefit from the wanting-system, especially if the player has to use a limited resource that replenished over time. Imagine that you have people as a resource and you can use them to build stuff. Of course building stuff isn’t instant, it takes time. There is nothing for the player left to do after a while because all people are building things. The player will than leave the game with the intention to come back when his people are finished building his stuff. The player won’t be annoyed or dislike the game because there is nothing left to do since it’s the nature of the game. Some design ideas for you When designing for retention, it’s good practice to ask yourself why the player should come back to play your game a second time. In my opinion your first answer should always be: “because they liked playing the game”. There is no point in playing a game you didn’t like the first time. The other answers are up to you to think about. Designing your game to be ‘liked’ is much more difficult that designing your game to be ‘wanted’. whether you like something or not is very personal. Some people can’t get enough of shooters while others (like myself) aren’t a big fan. But there are a couple of things that can help the player like your game. Completing or finishing something feels good. When your game is level-based, it helps to keep the first couple of levels short. You can increase the time spend in a level slowly as the player progresses. Finishing each level leaves the player wanting more: “just one more level, then I’ll stop”. Designing your game to be ‘wanted’ is a lot easier. Design your game in such a way that player has some unfinished business when he or she finished the first session. Think about a good cliff-hanger at the end of an episode: it leaves you wanting more. It’s the reason you and your friends are dying to see the new game of thrones season. You can design cliff-hangers for your game as well. The only difference is that you might have to “force” the player out of your game somehow. Add a resource-system to your game that is time-based but is depletes when you are playing. It can be a lives-system like in candy crush or a resource such as money or people in a micro-management game. There is no reason to stay in the game once the player runs out of the resource. Balance the resource in such a way that the player runs out of it when he or she is enjoying your game the most. It’s always important to make sure your player ends the game on a high note. It leaves them wanting more and have them looking forwards to the next session. If you want, you can send the player a reminder when the resource is replenished. But there is no need for daily rewards, this kills the player’s intrinsic motivation (I will talk more about intrinsic motivation the next time) and they won’t like to play your game anymore. References and further research Berridge, K. & Kringelbach, M. (2008). Affective neuroscience of pleasure: Rewards in humans and animals. Psychopharmacology, 199(3), 457-480. Litman, J (2005). Curiosity and the pleasure of learning: Wanting and liking new information. Cognition & Emotion, 19(6), 793-814. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2756052/ http://lsa.umich.edu/psych/research&labs/berridge/research/affectiveneuroscience.html https://www.sciencedaily.com/releases/2007/03/070302115232.htm https://www.researchgate.net/publication/245823962_Curiosity_and_the_pleasures_of_learning_Wanting_and_liking_new_information https://www.marketingsociety.com/the-gym/liking-vs-wanting#6ZiiMdJXqRtJvGSX.97
  9. Why is it that games tend to rarely vary their form? You start with a set of activities-- running, jumping, shooting-- and while they may deepen and expand, they almost never change significantly through the entirety of the experience. By (very flawed) contrast, consider how much more malleable as a medium books and movies can be: They can switch genres, alter perspective and even change subject matter entirely. A movie like Good Morning, Vietnam, for example, starts out as a comedy but ranges into romance, drama, suspenseful action and even tragedy (Hancock, From Dusk Til Dawn, Vanilla Sky, District 9 and Click are movies that could also fit this example). What is it about games that so limit their form? Is it the maturity of the medium? The strict genre expectations of the audience? Or is it possible that one of the medium's greatest strengths-- interactivity and the process of engaging with it, which is basically learning-- is simultaneously a weakness of sorts? I lean toward this. Maybe the process of learning and mastering mechanics sets a kind of upper limit to what a game can depict and the sum of what experiences it can convey. Card games and board games seem to share this limit-- chess does not morph into poker, for instance, and it would be hard to see it do so (I wouldn't count playing both at the same time). Or maybe the whole question is flawed and the comparison to books and movies essentially apples to oranges. You might argue the bulk of movies & books fall into well defined genre categories, for instance, varying similar plotlines maybe much like a shooter or platform jumper or racing game varies levels. But I can't shake the idea of how most games can't really even switch genres let alone their overall form. When you start most, you know you'll be doing basically at the end what you started at the beginning, just maybe with different permutations and contexts. Imagine the howls of going from a shooter to a match-3 game, or from an RTS to a racing game, even if it was a smooth transition. Games that have attempted this, like Spore (or the little known Gordon Alliance decades before it), often run afoul of the problem of sacrificing depth for breadth or suffering from mechanics that just don't cohere well together. What do you think?