Game Character Ability Adjustment via Group-Individual Tendency Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Simulation type team fighting games lack realism due to simple automatic control patterns, failing to reflect the influence of individual and group behavior tendencies on team performance, which affects user engagement and interest.
Innovation Solution
A program that forms groups of characters with predetermined individual behavior tendencies and ability values, allowing automatic control to adjust character abilities based on the match between group and individual tendencies, enabling more realistic gameplay by reflecting user-set behavior tendencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple automatic control patterns are used in simulation games, then the game is easy to implement and execute, but the realism and user engagement deteriorate
Solution Approach 1:
The automatic control system is segmented into multiple independent thinking patterns (e.g., aggressive pattern, defensive pattern, balanced pattern). Each pattern operates independently and can be selected based on game conditions, allowing the system to maintain simplicity while achieving realistic behavior through pattern selection rather than complex unified control logic.
Solution Approach 2:
The system changes parameters by selecting different thinking patterns with varying characteristics. Each pattern has distinct parameter settings for decision-making (e.g., risk tolerance, aggressiveness thresholds). This allows the game to switch between simple and complex behaviors dynamically, maintaining ease of implementation while achieving realism through parameter variation.
2Reliability
If individual behavior tendencies of characters are considered in automatic control, then the realism of team interactions is improved, but the complexity of the control system increases
Solution Approach 1:
The control system is segmented into modular thinking patterns, each handling specific behavioral aspects (offense, defense, positioning). Individual character behavior tendencies are integrated by selecting and combining relevant pattern segments rather than implementing a single complex unified control system, thus maintaining manageability while achieving realistic interactions.
Solution Approach 2:
The system dynamically adjusts the application of thinking patterns based on individual character behavior tendencies and current game states. Rather than using fixed complex control logic, the system flexibly selects and adapts pattern applications in real-time, achieving realistic dynamic interactions without requiring permanently complex control structures.
3Adaptability or versatility
If multiple thinking patterns are implemented for automatic control, then the adaptability to different game situations is improved, but the device complexity increases
Solution Approach 1:
The adaptable control system is segmented into discrete thinking patterns, each optimized for specific game situations (e.g., comeback pattern for losing teams, dominant pattern for leading teams). This segmentation allows the system to achieve high adaptability through pattern selection rather than through complex conditional logic within a single control system.
Solution Approach 2:
Each thinking pattern serves multiple functions across different game contexts. For example, the aggressive pattern can be applied in both offensive and defensive situations, and the balanced pattern adapts to various score conditions. This multi-functionality reduces the need for separate specialized controls for each situation, maintaining simplicity while achieving versatility.
Data Source
AI summary
A team is organized with players having preset inclination values having an influence on individual behavior tendencies of the players while a whole inclination value having an influence on the behavior tendency of a whole team is set. The difference between the whole inclination value of the team and inclination values of the individual players is calculated. The smaller the difference, the higher the player's ability is changed than the initial value. The larger the difference, the lower it is changed than the initial value. In match simulation processing, a simulation is performed with the abilities after the change.