APPARATUS AND METHOD FOR ADJUSTING DIFFICULTY LEVEL OF GAME

• US 20100279762A1
• Filed: 09/02/2009
• Published: 11/04/2010
• Est. Priority Date: 04/30/2009
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: PARK, Chang Joon, SOHN, Kang Min
• Application Number:
  US12/552,884
• Publication Number:
  US 20100279762A1
• Time in Patent Office: Days
• US Class Current: 463/23
• CPC Class Codes: A63F 13/58 by computing conditions of game characters, e.g. stamina, strength, motivation or energy level
• CPC Class Codes: A63F 13/67 adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use
• CPC Class Codes: A63F 13/837 Shooting of targets
• CPC Class Codes: A63F 2300/6018 where the game content is authored by the player, e.g. level editor or by game device at runtime, e.g. level is created from music data on CD
• CPC Class Codes: A63F 2300/6027 using adaptive systems learning from user actions, e.g. for skill level adjustment
• CPC Class Codes: A63F 2300/609 for unlocking hidden game elements, e.g. features, items, levels

Abstract

An apparatus for adjusting difficulty level of a game, includes: an artificial intelligence unit for storing artificial intelligence algorithms; a strategy toolkit for generating metadata for game resources and creating game strategies by applying the artificial intelligence algorithms using the metadata; and a simulation toolkit for calculating relative difficulty levels of the game strategies and combinations of the game strategies and applying to the game one of the game strategies and the combinations of the game strategies based on a user'"'"'s skill level determined during the game.

Claims

1. An apparatus for adjusting difficulty level of a game, comprising:

an artificial intelligence unit for storing artificial intelligence algorithms;

a strategy toolkit for generating metadata for game resources and creating game strategies by executing the artificial intelligence algorithms using the metadata; and

a simulation toolkit for calculating relative difficulty levels of the game strategies and combinations of the game strategies and applying one of the game strategies and the combined game strategies to the game based on a user'"'"'s skill level determined during the game.

2. The apparatus of claim 1, wherein the artificial intelligence algorithms include a decision tree, a genetic algorithm, a planning algorithm, and a neural network.

3. The apparatus of claim 1, wherein the game strategies are created by performing NPC (non player character) positioning.

4. The apparatus of claim 3, wherein the strategy toolkit includes:

a resource loading unit for reading out the game resources;

a metadata generating unit for generating and the metadata to be allocated to the game resources; and

a game strategy creation unit for creating the game strategies through the NPC positioning, which derives positions of the NPCs by executing the artificial intelligence algorithms using the metadata.

5. The apparatus of claim 4, wherein the game resources include at least one of topographic features, buildings, objects and characters in the game.

6. The apparatus of claim 4, wherein the game strategies include at least one of a detour strategy, an occultation strategy and a pincer strategy.

7. The apparatus of claim 1, wherein the simulation toolkit performs a strategy simulation to calculate the relative difficulty levels of the game strategies and to test combinability of the game strategies, and allocates a high difficulty level to a combined game strategy having a relatively high combination effect and a low difficulty level to a combined game strategy having a relatively low combination effect according to the combination effects based on results of the strategy simulation.

8. The apparatus of claim 1, wherein the simulation toolkit determines the user'"'"'s skill level based on an assessment result of relative dominance between PC (player character) and NPC (non-player character) during the game.

9. The apparatus of claim 1, wherein the simulation toolkit includes:

a game strategy combining unit for combining the game strategies provided from the strategy toolkit;

a difficulty level calculating unit for calculating the relative difficulty levels of the game strategies and the combined game strategies;

a dominance assessing unit for assessing relative dominance depending on results of comparisons between PC and NPC with respect to game status information during the game; and

a difficulty level adjusting unit for determining a difficulty level of the game depending on the relative dominance between the PC and the NPC assessed by the dominance assessing unit to apply a strategy of the determined difficulty level among the game strategies and the combined game strategies to the game.

10. The apparatus of claim 9, wherein the game status information includes at least one of score, physical strength, number of remaining bullets, number of wins, number of kills and number of remaining troops.

11. The apparatus of claim 9, wherein the difficulty level adjusting unit increases the difficulty level of the game if the PC is dominant and decreases the difficulty level of the game if the NPC is dominant.

12. A method for adjusting difficulty level of a game, comprising:

reading out game resources to generate metadata for the game resources;

creating game strategies by executing artificial intelligence algorithms;

calculating relative difficulty levels of the game strategies and combinations of the game strategies; and

applying one of the game strategies and the combined game strategies based on a user'"'"'s skill level determined during the game.

13. The method of claim 12, wherein said creating the game strategies creates the game strategies by performing NPC (non player character) positioning.

14. The method of claim 12, wherein the artificial intelligence algorithms include a decision tree, a genetic algorithm, a planning algorithm and a neural network.

15. The method of claim 12, wherein the game resources includes at least one of topographic features, buildings, objects and characters.

16. The method of claim 12, wherein the game strategies include at least one of a detour strategy, an occultation strategy, and a pincer strategy.

17. The method of claim 12, wherein said calculating the relative difficulty levels calculates the relative difficulty and tests combinability of the game strategies by performing a strategy simulation, and allocates a high difficulty level to a combined game strategy having a relatively high combination effect and a low difficulty level to a combined game strategy having a relatively low combination effect according to the combination effects based on results of the strategy simulation.

18. The method of claim 12, wherein said applying one of the game strategies and the combined game strategies includes determining a difficulty level of the game based on an assessment result of relative dominance between PC (player character) and NPC (non-player character) during the game.

19. The method of claim 12, wherein said applying one of the game strategies and the combined game strategies includes:

combining the game strategies;

calculating the relative difficulty levels of the game strategies and the combined game strategies;

assessing relative dominance depending on results of comparisons between PC and NPC with respect to game status information during the game; and

determining a difficulty level of the game depending on the relative dominance between the PC and the NPC to apply a strategy of the determined difficulty level among the game strategies and the combined game strategies to the game.

20. The method of claim 19, wherein said assessing relative dominance includes comparing the game status information including at least one of score, physical strength, number of remaining bullets, number of wins, number of kills, and number of remaining troops, between PC and NPC.