ARENA - Dynamic Run-time Map Generation for Multiplayer Shooters
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This document describes research on procedurally generating maps for asynchronous multiplayer shooters. It discusses motivations like reducing development time and improving longevity. A technique is presented that procedurally generates maps in 5 phases: populating tiles, cleaning up, identifying regions, connecting regions, and positioning strategic points. Maps are evaluated analytically by measuring properties like generation time, collision points, and fairness, and through an online user study. The research aims to quickly generate maps that provide good navigability, pacing, and fairness for asynchronous multiplayer gameplay.
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ARENA - Dynamic Run-time Map Generation for Multiplayer Shooters
- 1. ICEC 2014 ARENA- Dynamic Run-time Map generation for Asynchronous Multiplayer Shooter Bhojan Anand (Presenter), Wong Hong Wei
- 2. Asynchronous Gameplay + Multiplayer Shooter
- 3. David Bot John Bot YOU achieved with Human Imitating Bots
- 4. Building an Asynchronous Multiplayer Shooter with Procedurally Generated Content
- 5. Procedurally generate Multiplayer Maps for Shooters
- 7. 1) Motivation 2) Design & Implementation 3) Evaluation
- 8. 1) Motivation • Procedural Generation of Map “drastically reduce development time” “improved longevity from unlimited maps” • Human Imitating Bots [Extended Ver] “brings Asychronous Gameplay to shooters” “play a multiplayer shooter when friends are unavailable”
- 9. 1) Motivation 2) Design & Implementation a) Procedural Generation of Map b) Human Imitating Bots 3) Evaluation
- 10. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points Join Rule
- 11. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points
- 12. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points
- 13. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points
- 14. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points
- 15. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points
- 16. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points Spawn (red) Spawn (blue)
- 17. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points TeamFlag (red) TeamFlag (blue)
- 18. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points Flag Flag
- 19. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points Collision Point
- 20. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points Cover Cover Cover
- 21. 2a) Procedural Generation of Map • Initialisation PHASE I Populating Game Tiles PHASE II Cleaning Up PHASE III Idenitfying Regions PHASE IV Connecting Regions PHASE V Positioning of Strategic Points Cover Cover Cover Cover Cover
- 22. 2a) Procedural Generation of Map • Evolution Map Fitness = Connectivity Forced Collision Points limited to 1 or 2 Flag Fairness difference in distance to own team flag Overall Flag Fairness difference in distance to all flags + + + In line with the DESIGN GOALS:- - Fast generation - Collision Points - Flow - Navigability, Pacing - Fairness - Asthetics
- 23. 2a) Procedural Generation of Map • Evolution
- 24. 2a) Procedural Generation of Map • Mapping Each tile is mapped to produce
- 25. 1) Motivation 2) Design & Implementation a) Procedural Generation of Map b) Human Imitating Bots 3) Evaluation
- 26. 3) Evaluation • Analytical Evaluation using measurements / analysis of generated map • Subjective Evaluation using online user study - 53 respondents - no knowledge of project nature Based on Design Goals
- 27. Fast Generation Analytical Subjective • Generated 3 maps, in 10.1s, 9.9s, 11.5s • Not very acceptable • Can be improved with • Reached max fitness less populations within 30 populations • Much faster than previous works
- 28. Collision Points Analytical Subjective • Defined well • Prone to meet in middle • Mean 3.60, StdDev 1.25 • Hypothesis that locations are sparsely located can be rejected at 0.05 significance
- 29. Flow – Navigability & Pacing Analytical Subjective • Indoor/outdoor areas • Unique items • Few deadends • Good navigability
- 30. Flow – Navigability & Pacing Analytical Subjective • Indoor/outdoor areas • Unique items • Few deadends • Mostly falls in middle • May be due to difficulty in measuring pacing
- 31. Fairness Analytical Subjective • Time to navigate to flags - 58.88s from red team - 60.21s from blue team • Suggests high fairness at 0.05 significance
- 32. Demography
- 33. Play the game @ http://www.comp.nus.edu.sg/~hongjun/GD.html Thank You! Bhojan Anand (Presenter), Wong Hong Wei
Editor's Notes
- Recently, development in the industry resulted in the asynchronous multiplayer concept. Unlike most multiplayer games which demands players to play together at the same time, asynchronous multiplayer can be played even when other players are not around. In Draw Something, a player draws a picture given a word. The opponent does not need to respond instantly, and can choose to guess the word any time after that. Global Domination is designed to experiment with the possibility of incorporating such an element into a multiplayer shooter. The player plays against an Artificial Intelligence (AI) Bot that imitates the behavior of another player. A common element is seen in Forza Motorstorm in the form of a Drivatar [11].
- We restrict the scope of the game mode to “Capture and Hold” (eg. Killzone)
- Remember to explain rules
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In our implementation, we will make use of SBPCG for map generation due to the abundance of research into the area. SBPCG utilises a generate-and-test approach in the following steps: Candidate contents are created. Each candidate is assigned a grade/fitness using a fitness function. Candidates with lower fitness are discarded and replaced with a new candidate that can be a mutation and/or a combination of the better candidates. Repeat 2 and 3 until a satisfactory fitness is obtained.
- In such functions, features are retrieved from the content which is mapped directly to its fitness. We propose a list of measurable objectives (in decreasing priority) based on what constitutes a good multiplayer map (See Section 4). In the following, team flags refer to 2 flags (out of 4) that are the closest to the team base. The other 2 flags are team flags for the other team. Flag Fairness – The mean distance travelled to team flags are measured for each team by moving from the team base to all flags with the A* algorithm. The negative value of the difference in the distance is used. Forced Collision Points – This can only be either 0 or 1. It is 1 if there exists no more than 2 simple paths (room: vertex, edge: door between rooms) between any one of the team flags and the opponent’s team flags. This can only be applied for the Growth and Subdivision Methods. Overall Flag Fairness – The mean distance travelled to all flags are measured in a way similar to above. The difference in the value is used. The weight for this feature is negative. Pacing – The mean time to travel to all flags are measured for both teams (by computing mean distance/speed). While unsupported by any actual research, judging from our experience, we feel that the optimal pace would be to meet an opponent 12-18s after spawning from the team base. Therefore, the value would be 1 if the mean time falls between 12 and 18s, and decreases as it goes away from the range.
- In such functions, features are retrieved from the content which is mapped directly to its fitness. We propose a list of measurable objectives (in decreasing priority) based on what constitutes a good multiplayer map (See Section 4). In the following, team flags refer to 2 flags (out of 4) that are the closest to the team base. The other 2 flags are team flags for the other team. Flag Fairness – The mean distance travelled to team flags are measured for each team by moving from the team base to all flags with the A* algorithm. The negative value of the difference in the distance is used. Forced Collision Points – This can only be either 0 or 1. It is 1 if there exists no more than 2 simple paths (room: vertex, edge: door between rooms) between any one of the team flags and the opponent’s team flags. This can only be applied for the Growth and Subdivision Methods. Overall Flag Fairness – The mean distance travelled to all flags are measured in a way similar to above. The difference in the value is used. The weight for this feature is negative. Pacing – The mean time to travel to all flags are measured for both teams (by computing mean distance/speed). While unsupported by any actual research, judging from our experience, we feel that the optimal pace would be to meet an opponent 12-18s after spawning from the team base. Therefore, the value would be 1 if the mean time falls between 12 and 18s, and decreases as it goes away from the range.
- In such functions, features are retrieved from the content which is mapped directly to its fitness. We propose a list of measurable objectives (in decreasing priority) based on what constitutes a good multiplayer map (See Section 4). In the following, team flags refer to 2 flags (out of 4) that are the closest to the team base. The other 2 flags are team flags for the other team. Flag Fairness – The mean distance travelled to team flags are measured for each team by moving from the team base to all flags with the A* algorithm. The negative value of the difference in the distance is used. Forced Collision Points – This can only be either 0 or 1. It is 1 if there exists no more than 2 simple paths (room: vertex, edge: door between rooms) between any one of the team flags and the opponent’s team flags. This can only be applied for the Growth and Subdivision Methods. Overall Flag Fairness – The mean distance travelled to all flags are measured in a way similar to above. The difference in the value is used. The weight for this feature is negative. Pacing – The mean time to travel to all flags are measured for both teams (by computing mean distance/speed). While unsupported by any actual research, judging from our experience, we feel that the optimal pace would be to meet an opponent 12-18s after spawning from the team base. Therefore, the value would be 1 if the mean time falls between 12 and 18s, and decreases as it goes away from the range.
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.
- We will be attempting PCG for maps in multiplayer shooters, which are still in its early stages of research. While PCG is sometimes used by multiplayer shooter game designers who would have to alter the generated maps in the final product, our goal is to remove the middleman completely. Optimisation therefore becomes an issue. If we are successful, development time could be drastically reduced, and the result is an unlimited set of maps.