Jump to content

NikiOo

Members
  • Content Count

    254
  • Joined

  • Last visited

  • Days Won

    7

Reputation Activity

  1. Awesome
    NikiOo got a reaction from csWaldo in How do I generate ideas for unique layouts in CSGO Wingman maps?   
    Well, it's actually quite simple.

    Ideally, you want to minimize the filler areas and maximize areas of type alpha and omega. You do this by learning how to think in 3 dimensions and how to generate interesting areas, while working within the constraints of nature and human architecture. Here is the formula for what makes a good area:

    Goodness of narrative comes down to story-telling, which is a very expansive topic in and of itself so I will focus on goodness of color and goodness of shape. So we'll use a simplified formula for goodness of area, shown below:

    Formula (1) is the lego approach to area engineering, where you deal with large presets, since you're not yet experienced enough to work on area microarchitecture. Now I will show you the wizard approach. You won't read about this in LD text-books.
    Let H be the set of architectural components used by humans and let N denote the set of architectural components used by nature (God / evolution). Then we'll use Z to denote the available language of architectural expression. Z is the proper set of the union of H and N. Naturally, you cannot use the entire language of expression at your disposal so let's say the language you're going to use is some subset of Z, which we shall call L.

    Now in order to learn how to make a good area, you need a way to calculate the goodness quantifier of formula (5). Without that you won't have the much needed feedback-response cycle and you will not improve. So we need to measure koppaI from an already built level. To do this, you take all elements of L and distribute them in a 3-d vector space, based on their position of use in the level. If you want, you can use two separate vector spaces - one for the color subcomponent of every component, and one for its shape. But beware, the natural distribution of the reasonable color spectrum for most components is pretty tight.
    Next thing you do, you color code the scattered points based on the spatial locality of the components in the real world. In other words, components that appear together are assigned similar colors. Then you want to reduce the amount of overall entropy in the color distribution. One way to do this, without sacrificing variety is to have clusters of low entropy (which appear as splatters of uniform color) in distinct areas of your map, while the connecting areas should be kept low entropy (without many micro-clusters) so they can compensate for the contrasting transition between the larger areas.

    For reference, in the image above, a high entropy area would look something like (c), while the rest are relatively low entropy.
    Some easy ways to reduce color entropy in your level are:
    using a very limited color palette using painterly textures using photo textures taken from the same real world location Some easy ways to reduce shape entropy in your level are:
    using references not mixing architectural styles from different historical periods use large pieces for cover, remove small pieces that only add clutter reducing signs of human life (it is very difficult to prevent signs of human life from skyrocketing your entropy meter) hierarchical procedure (starting from a silhouette blockout and gradually filling in details) Keep in mind, entropy can also be too low. This can happen if you put too much effort into silhouette and visual harmony and don't use any references. You can end up compensating with bad entropy. Bad entropy is when details you add increase visual disharmony very rapidly. Ideally, you want your learning process to lead you to a place where your entropy is similar to the entropy in areas you find visually appealing in real life or virtual environments, and you also want the level of detail to approach the level of detail in those environments.

    In formula (6) I show an example optimization model for a realistic style area. You can use this to derive a model for fantastic or stylized areas. You want entropy and detail to move in parallel with one another. The ideal scenario would look something like this:

    Keep in mind that adding narrative elements such as signs of human life and natural decay can increase entropy quite alot. For further reading on sings of human life look into How buildings learn by Stewart Brand.
    If you get good at adding detail without massively increasing entropy, you will have more confidence to experiment with unique layouts.
    Then there is another formula, called the challenge complexity formula. Player enjoyment from playing a map tends to correlate inversely with frustration. Frustration comes from low correlation between expected challenge and actual challenge.

    One way to model challenge is as a scalar, representing net difficulty (7).

    Another way you can model challenge, shown above, is as a set of micro-games that the player will have to play during the match, determined by the context of each encounter. The phrase "micro-games played in wingman maps on official servers" can be replaced with micro-games anticipated by the user or micro-games user enjoys to play in counter-strike. For this analysis, I assume that enjoyment is maximal on wingman maps on official servers, presuming those maps are examples of prime level design. This is more true for defuse maps than it is for wingman.
    Micro-games constitute things like how you peek certain corners, how you play around with cover and position in certain contexts, how you use your utility, and all sorts of other actions you take to become more unpredictable and to put yourself in a favourable situation. The possible situations you may find yourself in are determined by the architecture of the level.
    You don't want to introduce too many unique ideas into your level, because it will reflect badly on the micro-games ratio coefficient.
    You can just plug those formulas into hammer and watch the magic happen!
  2. LOL
    NikiOo got a reaction from FMPONE in How do I generate ideas for unique layouts in CSGO Wingman maps?   
    Well, it's actually quite simple.

    Ideally, you want to minimize the filler areas and maximize areas of type alpha and omega. You do this by learning how to think in 3 dimensions and how to generate interesting areas, while working within the constraints of nature and human architecture. Here is the formula for what makes a good area:

    Goodness of narrative comes down to story-telling, which is a very expansive topic in and of itself so I will focus on goodness of color and goodness of shape. So we'll use a simplified formula for goodness of area, shown below:

    Formula (1) is the lego approach to area engineering, where you deal with large presets, since you're not yet experienced enough to work on area microarchitecture. Now I will show you the wizard approach. You won't read about this in LD text-books.
    Let H be the set of architectural components used by humans and let N denote the set of architectural components used by nature (God / evolution). Then we'll use Z to denote the available language of architectural expression. Z is the proper set of the union of H and N. Naturally, you cannot use the entire language of expression at your disposal so let's say the language you're going to use is some subset of Z, which we shall call L.

    Now in order to learn how to make a good area, you need a way to calculate the goodness quantifier of formula (5). Without that you won't have the much needed feedback-response cycle and you will not improve. So we need to measure koppaI from an already built level. To do this, you take all elements of L and distribute them in a 3-d vector space, based on their position of use in the level. If you want, you can use two separate vector spaces - one for the color subcomponent of every component, and one for its shape. But beware, the natural distribution of the reasonable color spectrum for most components is pretty tight.
    Next thing you do, you color code the scattered points based on the spatial locality of the components in the real world. In other words, components that appear together are assigned similar colors. Then you want to reduce the amount of overall entropy in the color distribution. One way to do this, without sacrificing variety is to have clusters of low entropy (which appear as splatters of uniform color) in distinct areas of your map, while the connecting areas should be kept low entropy (without many micro-clusters) so they can compensate for the contrasting transition between the larger areas.

    For reference, in the image above, a high entropy area would look something like (c), while the rest are relatively low entropy.
    Some easy ways to reduce color entropy in your level are:
    using a very limited color palette using painterly textures using photo textures taken from the same real world location Some easy ways to reduce shape entropy in your level are:
    using references not mixing architectural styles from different historical periods use large pieces for cover, remove small pieces that only add clutter reducing signs of human life (it is very difficult to prevent signs of human life from skyrocketing your entropy meter) hierarchical procedure (starting from a silhouette blockout and gradually filling in details) Keep in mind, entropy can also be too low. This can happen if you put too much effort into silhouette and visual harmony and don't use any references. You can end up compensating with bad entropy. Bad entropy is when details you add increase visual disharmony very rapidly. Ideally, you want your learning process to lead you to a place where your entropy is similar to the entropy in areas you find visually appealing in real life or virtual environments, and you also want the level of detail to approach the level of detail in those environments.

    In formula (6) I show an example optimization model for a realistic style area. You can use this to derive a model for fantastic or stylized areas. You want entropy and detail to move in parallel with one another. The ideal scenario would look something like this:

    Keep in mind that adding narrative elements such as signs of human life and natural decay can increase entropy quite alot. For further reading on sings of human life look into How buildings learn by Stewart Brand.
    If you get good at adding detail without massively increasing entropy, you will have more confidence to experiment with unique layouts.
    Then there is another formula, called the challenge complexity formula. Player enjoyment from playing a map tends to correlate inversely with frustration. Frustration comes from low correlation between expected challenge and actual challenge.

    One way to model challenge is as a scalar, representing net difficulty (7).

    Another way you can model challenge, shown above, is as a set of micro-games that the player will have to play during the match, determined by the context of each encounter. The phrase "micro-games played in wingman maps on official servers" can be replaced with micro-games anticipated by the user or micro-games user enjoys to play in counter-strike. For this analysis, I assume that enjoyment is maximal on wingman maps on official servers, presuming those maps are examples of prime level design. This is more true for defuse maps than it is for wingman.
    Micro-games constitute things like how you peek certain corners, how you play around with cover and position in certain contexts, how you use your utility, and all sorts of other actions you take to become more unpredictable and to put yourself in a favourable situation. The possible situations you may find yourself in are determined by the architecture of the level.
    You don't want to introduce too many unique ideas into your level, because it will reflect badly on the micro-games ratio coefficient.
    You can just plug those formulas into hammer and watch the magic happen!
  3. Awesome
    NikiOo got a reaction from Mr.Yeah! in How do I generate ideas for unique layouts in CSGO Wingman maps?   
    Well, it's actually quite simple.

    Ideally, you want to minimize the filler areas and maximize areas of type alpha and omega. You do this by learning how to think in 3 dimensions and how to generate interesting areas, while working within the constraints of nature and human architecture. Here is the formula for what makes a good area:

    Goodness of narrative comes down to story-telling, which is a very expansive topic in and of itself so I will focus on goodness of color and goodness of shape. So we'll use a simplified formula for goodness of area, shown below:

    Formula (1) is the lego approach to area engineering, where you deal with large presets, since you're not yet experienced enough to work on area microarchitecture. Now I will show you the wizard approach. You won't read about this in LD text-books.
    Let H be the set of architectural components used by humans and let N denote the set of architectural components used by nature (God / evolution). Then we'll use Z to denote the available language of architectural expression. Z is the proper set of the union of H and N. Naturally, you cannot use the entire language of expression at your disposal so let's say the language you're going to use is some subset of Z, which we shall call L.

    Now in order to learn how to make a good area, you need a way to calculate the goodness quantifier of formula (5). Without that you won't have the much needed feedback-response cycle and you will not improve. So we need to measure koppaI from an already built level. To do this, you take all elements of L and distribute them in a 3-d vector space, based on their position of use in the level. If you want, you can use two separate vector spaces - one for the color subcomponent of every component, and one for its shape. But beware, the natural distribution of the reasonable color spectrum for most components is pretty tight.
    Next thing you do, you color code the scattered points based on the spatial locality of the components in the real world. In other words, components that appear together are assigned similar colors. Then you want to reduce the amount of overall entropy in the color distribution. One way to do this, without sacrificing variety is to have clusters of low entropy (which appear as splatters of uniform color) in distinct areas of your map, while the connecting areas should be kept low entropy (without many micro-clusters) so they can compensate for the contrasting transition between the larger areas.

    For reference, in the image above, a high entropy area would look something like (c), while the rest are relatively low entropy.
    Some easy ways to reduce color entropy in your level are:
    using a very limited color palette using painterly textures using photo textures taken from the same real world location Some easy ways to reduce shape entropy in your level are:
    using references not mixing architectural styles from different historical periods use large pieces for cover, remove small pieces that only add clutter reducing signs of human life (it is very difficult to prevent signs of human life from skyrocketing your entropy meter) hierarchical procedure (starting from a silhouette blockout and gradually filling in details) Keep in mind, entropy can also be too low. This can happen if you put too much effort into silhouette and visual harmony and don't use any references. You can end up compensating with bad entropy. Bad entropy is when details you add increase visual disharmony very rapidly. Ideally, you want your learning process to lead you to a place where your entropy is similar to the entropy in areas you find visually appealing in real life or virtual environments, and you also want the level of detail to approach the level of detail in those environments.

    In formula (6) I show an example optimization model for a realistic style area. You can use this to derive a model for fantastic or stylized areas. You want entropy and detail to move in parallel with one another. The ideal scenario would look something like this:

    Keep in mind that adding narrative elements such as signs of human life and natural decay can increase entropy quite alot. For further reading on sings of human life look into How buildings learn by Stewart Brand.
    If you get good at adding detail without massively increasing entropy, you will have more confidence to experiment with unique layouts.
    Then there is another formula, called the challenge complexity formula. Player enjoyment from playing a map tends to correlate inversely with frustration. Frustration comes from low correlation between expected challenge and actual challenge.

    One way to model challenge is as a scalar, representing net difficulty (7).

    Another way you can model challenge, shown above, is as a set of micro-games that the player will have to play during the match, determined by the context of each encounter. The phrase "micro-games played in wingman maps on official servers" can be replaced with micro-games anticipated by the user or micro-games user enjoys to play in counter-strike. For this analysis, I assume that enjoyment is maximal on wingman maps on official servers, presuming those maps are examples of prime level design. This is more true for defuse maps than it is for wingman.
    Micro-games constitute things like how you peek certain corners, how you play around with cover and position in certain contexts, how you use your utility, and all sorts of other actions you take to become more unpredictable and to put yourself in a favourable situation. The possible situations you may find yourself in are determined by the architecture of the level.
    You don't want to introduce too many unique ideas into your level, because it will reflect badly on the micro-games ratio coefficient.
    You can just plug those formulas into hammer and watch the magic happen!
  4. LOL
    NikiOo got a reaction from Lizard in How do I generate ideas for unique layouts in CSGO Wingman maps?   
    Well, it's actually quite simple.

    Ideally, you want to minimize the filler areas and maximize areas of type alpha and omega. You do this by learning how to think in 3 dimensions and how to generate interesting areas, while working within the constraints of nature and human architecture. Here is the formula for what makes a good area:

    Goodness of narrative comes down to story-telling, which is a very expansive topic in and of itself so I will focus on goodness of color and goodness of shape. So we'll use a simplified formula for goodness of area, shown below:

    Formula (1) is the lego approach to area engineering, where you deal with large presets, since you're not yet experienced enough to work on area microarchitecture. Now I will show you the wizard approach. You won't read about this in LD text-books.
    Let H be the set of architectural components used by humans and let N denote the set of architectural components used by nature (God / evolution). Then we'll use Z to denote the available language of architectural expression. Z is the proper set of the union of H and N. Naturally, you cannot use the entire language of expression at your disposal so let's say the language you're going to use is some subset of Z, which we shall call L.

    Now in order to learn how to make a good area, you need a way to calculate the goodness quantifier of formula (5). Without that you won't have the much needed feedback-response cycle and you will not improve. So we need to measure koppaI from an already built level. To do this, you take all elements of L and distribute them in a 3-d vector space, based on their position of use in the level. If you want, you can use two separate vector spaces - one for the color subcomponent of every component, and one for its shape. But beware, the natural distribution of the reasonable color spectrum for most components is pretty tight.
    Next thing you do, you color code the scattered points based on the spatial locality of the components in the real world. In other words, components that appear together are assigned similar colors. Then you want to reduce the amount of overall entropy in the color distribution. One way to do this, without sacrificing variety is to have clusters of low entropy (which appear as splatters of uniform color) in distinct areas of your map, while the connecting areas should be kept low entropy (without many micro-clusters) so they can compensate for the contrasting transition between the larger areas.

    For reference, in the image above, a high entropy area would look something like (c), while the rest are relatively low entropy.
    Some easy ways to reduce color entropy in your level are:
    using a very limited color palette using painterly textures using photo textures taken from the same real world location Some easy ways to reduce shape entropy in your level are:
    using references not mixing architectural styles from different historical periods use large pieces for cover, remove small pieces that only add clutter reducing signs of human life (it is very difficult to prevent signs of human life from skyrocketing your entropy meter) hierarchical procedure (starting from a silhouette blockout and gradually filling in details) Keep in mind, entropy can also be too low. This can happen if you put too much effort into silhouette and visual harmony and don't use any references. You can end up compensating with bad entropy. Bad entropy is when details you add increase visual disharmony very rapidly. Ideally, you want your learning process to lead you to a place where your entropy is similar to the entropy in areas you find visually appealing in real life or virtual environments, and you also want the level of detail to approach the level of detail in those environments.

    In formula (6) I show an example optimization model for a realistic style area. You can use this to derive a model for fantastic or stylized areas. You want entropy and detail to move in parallel with one another. The ideal scenario would look something like this:

    Keep in mind that adding narrative elements such as signs of human life and natural decay can increase entropy quite alot. For further reading on sings of human life look into How buildings learn by Stewart Brand.
    If you get good at adding detail without massively increasing entropy, you will have more confidence to experiment with unique layouts.
    Then there is another formula, called the challenge complexity formula. Player enjoyment from playing a map tends to correlate inversely with frustration. Frustration comes from low correlation between expected challenge and actual challenge.

    One way to model challenge is as a scalar, representing net difficulty (7).

    Another way you can model challenge, shown above, is as a set of micro-games that the player will have to play during the match, determined by the context of each encounter. The phrase "micro-games played in wingman maps on official servers" can be replaced with micro-games anticipated by the user or micro-games user enjoys to play in counter-strike. For this analysis, I assume that enjoyment is maximal on wingman maps on official servers, presuming those maps are examples of prime level design. This is more true for defuse maps than it is for wingman.
    Micro-games constitute things like how you peek certain corners, how you play around with cover and position in certain contexts, how you use your utility, and all sorts of other actions you take to become more unpredictable and to put yourself in a favourable situation. The possible situations you may find yourself in are determined by the architecture of the level.
    You don't want to introduce too many unique ideas into your level, because it will reflect badly on the micro-games ratio coefficient.
    You can just plug those formulas into hammer and watch the magic happen!
  5. Awesome
    NikiOo got a reaction from Klems in How do I generate ideas for unique layouts in CSGO Wingman maps?   
    Well, it's actually quite simple.

    Ideally, you want to minimize the filler areas and maximize areas of type alpha and omega. You do this by learning how to think in 3 dimensions and how to generate interesting areas, while working within the constraints of nature and human architecture. Here is the formula for what makes a good area:

    Goodness of narrative comes down to story-telling, which is a very expansive topic in and of itself so I will focus on goodness of color and goodness of shape. So we'll use a simplified formula for goodness of area, shown below:

    Formula (1) is the lego approach to area engineering, where you deal with large presets, since you're not yet experienced enough to work on area microarchitecture. Now I will show you the wizard approach. You won't read about this in LD text-books.
    Let H be the set of architectural components used by humans and let N denote the set of architectural components used by nature (God / evolution). Then we'll use Z to denote the available language of architectural expression. Z is the proper set of the union of H and N. Naturally, you cannot use the entire language of expression at your disposal so let's say the language you're going to use is some subset of Z, which we shall call L.

    Now in order to learn how to make a good area, you need a way to calculate the goodness quantifier of formula (5). Without that you won't have the much needed feedback-response cycle and you will not improve. So we need to measure koppaI from an already built level. To do this, you take all elements of L and distribute them in a 3-d vector space, based on their position of use in the level. If you want, you can use two separate vector spaces - one for the color subcomponent of every component, and one for its shape. But beware, the natural distribution of the reasonable color spectrum for most components is pretty tight.
    Next thing you do, you color code the scattered points based on the spatial locality of the components in the real world. In other words, components that appear together are assigned similar colors. Then you want to reduce the amount of overall entropy in the color distribution. One way to do this, without sacrificing variety is to have clusters of low entropy (which appear as splatters of uniform color) in distinct areas of your map, while the connecting areas should be kept low entropy (without many micro-clusters) so they can compensate for the contrasting transition between the larger areas.

    For reference, in the image above, a high entropy area would look something like (c), while the rest are relatively low entropy.
    Some easy ways to reduce color entropy in your level are:
    using a very limited color palette using painterly textures using photo textures taken from the same real world location Some easy ways to reduce shape entropy in your level are:
    using references not mixing architectural styles from different historical periods use large pieces for cover, remove small pieces that only add clutter reducing signs of human life (it is very difficult to prevent signs of human life from skyrocketing your entropy meter) hierarchical procedure (starting from a silhouette blockout and gradually filling in details) Keep in mind, entropy can also be too low. This can happen if you put too much effort into silhouette and visual harmony and don't use any references. You can end up compensating with bad entropy. Bad entropy is when details you add increase visual disharmony very rapidly. Ideally, you want your learning process to lead you to a place where your entropy is similar to the entropy in areas you find visually appealing in real life or virtual environments, and you also want the level of detail to approach the level of detail in those environments.

    In formula (6) I show an example optimization model for a realistic style area. You can use this to derive a model for fantastic or stylized areas. You want entropy and detail to move in parallel with one another. The ideal scenario would look something like this:

    Keep in mind that adding narrative elements such as signs of human life and natural decay can increase entropy quite alot. For further reading on sings of human life look into How buildings learn by Stewart Brand.
    If you get good at adding detail without massively increasing entropy, you will have more confidence to experiment with unique layouts.
    Then there is another formula, called the challenge complexity formula. Player enjoyment from playing a map tends to correlate inversely with frustration. Frustration comes from low correlation between expected challenge and actual challenge.

    One way to model challenge is as a scalar, representing net difficulty (7).

    Another way you can model challenge, shown above, is as a set of micro-games that the player will have to play during the match, determined by the context of each encounter. The phrase "micro-games played in wingman maps on official servers" can be replaced with micro-games anticipated by the user or micro-games user enjoys to play in counter-strike. For this analysis, I assume that enjoyment is maximal on wingman maps on official servers, presuming those maps are examples of prime level design. This is more true for defuse maps than it is for wingman.
    Micro-games constitute things like how you peek certain corners, how you play around with cover and position in certain contexts, how you use your utility, and all sorts of other actions you take to become more unpredictable and to put yourself in a favourable situation. The possible situations you may find yourself in are determined by the architecture of the level.
    You don't want to introduce too many unique ideas into your level, because it will reflect badly on the micro-games ratio coefficient.
    You can just plug those formulas into hammer and watch the magic happen!
  6. Awesome
    NikiOo got a reaction from Radu in How do I generate ideas for unique layouts in CSGO Wingman maps?   
    Well, it's actually quite simple.

    Ideally, you want to minimize the filler areas and maximize areas of type alpha and omega. You do this by learning how to think in 3 dimensions and how to generate interesting areas, while working within the constraints of nature and human architecture. Here is the formula for what makes a good area:

    Goodness of narrative comes down to story-telling, which is a very expansive topic in and of itself so I will focus on goodness of color and goodness of shape. So we'll use a simplified formula for goodness of area, shown below:

    Formula (1) is the lego approach to area engineering, where you deal with large presets, since you're not yet experienced enough to work on area microarchitecture. Now I will show you the wizard approach. You won't read about this in LD text-books.
    Let H be the set of architectural components used by humans and let N denote the set of architectural components used by nature (God / evolution). Then we'll use Z to denote the available language of architectural expression. Z is the proper set of the union of H and N. Naturally, you cannot use the entire language of expression at your disposal so let's say the language you're going to use is some subset of Z, which we shall call L.

    Now in order to learn how to make a good area, you need a way to calculate the goodness quantifier of formula (5). Without that you won't have the much needed feedback-response cycle and you will not improve. So we need to measure koppaI from an already built level. To do this, you take all elements of L and distribute them in a 3-d vector space, based on their position of use in the level. If you want, you can use two separate vector spaces - one for the color subcomponent of every component, and one for its shape. But beware, the natural distribution of the reasonable color spectrum for most components is pretty tight.
    Next thing you do, you color code the scattered points based on the spatial locality of the components in the real world. In other words, components that appear together are assigned similar colors. Then you want to reduce the amount of overall entropy in the color distribution. One way to do this, without sacrificing variety is to have clusters of low entropy (which appear as splatters of uniform color) in distinct areas of your map, while the connecting areas should be kept low entropy (without many micro-clusters) so they can compensate for the contrasting transition between the larger areas.

    For reference, in the image above, a high entropy area would look something like (c), while the rest are relatively low entropy.
    Some easy ways to reduce color entropy in your level are:
    using a very limited color palette using painterly textures using photo textures taken from the same real world location Some easy ways to reduce shape entropy in your level are:
    using references not mixing architectural styles from different historical periods use large pieces for cover, remove small pieces that only add clutter reducing signs of human life (it is very difficult to prevent signs of human life from skyrocketing your entropy meter) hierarchical procedure (starting from a silhouette blockout and gradually filling in details) Keep in mind, entropy can also be too low. This can happen if you put too much effort into silhouette and visual harmony and don't use any references. You can end up compensating with bad entropy. Bad entropy is when details you add increase visual disharmony very rapidly. Ideally, you want your learning process to lead you to a place where your entropy is similar to the entropy in areas you find visually appealing in real life or virtual environments, and you also want the level of detail to approach the level of detail in those environments.

    In formula (6) I show an example optimization model for a realistic style area. You can use this to derive a model for fantastic or stylized areas. You want entropy and detail to move in parallel with one another. The ideal scenario would look something like this:

    Keep in mind that adding narrative elements such as signs of human life and natural decay can increase entropy quite alot. For further reading on sings of human life look into How buildings learn by Stewart Brand.
    If you get good at adding detail without massively increasing entropy, you will have more confidence to experiment with unique layouts.
    Then there is another formula, called the challenge complexity formula. Player enjoyment from playing a map tends to correlate inversely with frustration. Frustration comes from low correlation between expected challenge and actual challenge.

    One way to model challenge is as a scalar, representing net difficulty (7).

    Another way you can model challenge, shown above, is as a set of micro-games that the player will have to play during the match, determined by the context of each encounter. The phrase "micro-games played in wingman maps on official servers" can be replaced with micro-games anticipated by the user or micro-games user enjoys to play in counter-strike. For this analysis, I assume that enjoyment is maximal on wingman maps on official servers, presuming those maps are examples of prime level design. This is more true for defuse maps than it is for wingman.
    Micro-games constitute things like how you peek certain corners, how you play around with cover and position in certain contexts, how you use your utility, and all sorts of other actions you take to become more unpredictable and to put yourself in a favourable situation. The possible situations you may find yourself in are determined by the architecture of the level.
    You don't want to introduce too many unique ideas into your level, because it will reflect badly on the micro-games ratio coefficient.
    You can just plug those formulas into hammer and watch the magic happen!
  7. Like
    NikiOo reacted to BARS in Karrida [Wingman]   
    In the city there is a spectacle, a bullfight! All the residents of the small town are on it now. At this time, the terrorists quietly planted dangerous substances in the city! Do not let the terrorists detonate chem
    https://steamcommunity.com/sharedfiles/filedetails/?id=2411829911

     


  8. Like
    NikiOo got a reaction from Zarsky in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  9. Like
    NikiOo got a reaction from Lizard in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  10. LOL
    NikiOo got a reaction from blackdog in Cyberpunk 2077   
    We finally have a challenger.
  11. Awesome
    NikiOo got a reaction from Serialmapper in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  12. LOL
    NikiOo got a reaction from Zarsky in Cyberpunk 2077   
    We finally have a challenger.
  13. LOL
    NikiOo got a reaction from Beck in Cyberpunk 2077   
    We finally have a challenger.
  14. Like
    NikiOo got a reaction from dux in Cyberpunk 2077   
    We finally have a challenger.
  15. LOL
    NikiOo got a reaction from Minos in Cyberpunk 2077   
    We finally have a challenger.
  16. LOL
    NikiOo got a reaction from Radu in Cyberpunk 2077   
    We finally have a challenger.
  17. Like
    NikiOo reacted to csWaldo in Furnace   
    @esspho has been working on changes from our first Mapcore playtest including a new design for outside A (thanks @Roald for the suggestions!) while I've been working on and off on bombsite B's main attraction.
    Should be ready for another playtest soon!


  18. Like
    NikiOo got a reaction from zombi in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  19. Like
    NikiOo got a reaction from blackdog in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  20. Like
    NikiOo got a reaction from ARPT in ROOF [How to build?]   
    I suggest that you align your geometry to the grid as much as you can, without compromising shape. In the example I show, I have roughly the same slope, but since it aligns with an exact point on the grid every six units. If you then reduce to a smaller grid, it would align every 3 units:

    You can then use the vertex tool to raise the left edge of the wall. Or alternatively, you can make the wall overlap with the roof and use the knife tool to cut it at the same angle, but I wouldn't advice it since making changes afterwards is gonna suck for you.
  21. Like
    NikiOo got a reaction from TelaF in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  22. Like
    NikiOo got a reaction from Squad in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  23. Like
    NikiOo got a reaction from ThunderKeil in The random model thread!   
    Reading this feels odd to me since it's the exact same reaction I had going from blender to 3ds max :D. Imo blender is the most intuitive software to use as far as navigating the viewport goes, especially if you use the (Shift + F) mode to fly around the 3d space. It may just be because I've used it for much longer than any other 3d software. Anyways, it's a matter of personal preference. I find myself switching from one software to another if I don't like how they do a certain thing.
    Here's the first fully textured car I've made (at least one that's good enough for me to remember)

  24. Like
    NikiOo reacted to Zarsky in Cyberpunk 2077   
    Apologies and refunds available.
  25. Awesome
    NikiOo reacted to Vaya in CS_Apollo   
    we got into the new operation. Lots of grind with this project! museum maps? never again.
×
×
  • Create New...