Dynamic Mystery Generation Framework for Computer Games
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Solution Overview
Problem
Existing computer games lack the ability to efficiently and dynamically generate a variety of problem situations, such as mysteries, which limits user engagement and satisfaction, as they often rely on pre-defined scenarios rather than on-the-fly generation responsive to user interactions.
Innovation Solution
A framework that utilizes a data structure representing candidates, fact patterns, and chains of inference to generate problem situations, allowing for the creation of diverse and customized mysteries by selecting subsets of fact patterns to form sub-graphs representing specific problem scenarios, enabling dynamic generation and modification based on user input and game requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined scenarios are used in computer games, then development is simpler and more controlled, but user engagement and satisfaction are limited due to lack of variety
Solution Approach 1:
The system segments the problem situation generation into modular components: a data structure representing the game world is divided into candidates, fact patterns, and chains of inference. This segmentation allows selective combination of elements to generate diverse problem situations without requiring complete pre-definition of every scenario.
Solution Approach 2:
The system transitions from static pre-defined scenarios to dynamic on-the-fly generation. The framework dynamically selects subsets of fact patterns and constructs problem situations during gameplay based on current game state, providing adaptability while maintaining manageable complexity through systematic selection processes.
2Adaptability or versatility
If on-the-fly generation of problem situations is implemented, then user engagement and satisfaction are enhanced, but computational resources and processing time are consumed
Solution Approach 1:
The system performs preliminary organization of game world data into a structured format with pre-defined candidates, fact patterns, and chains of inference during game setup or idle periods. This preliminary structuring reduces the computational burden during actual problem situation generation, as the system only needs to select and combine pre-organized elements rather than create everything from scratch in real-time.
Solution Approach 2:
The framework applies local quality by selecting specific subsets of fact patterns relevant to the current game context rather than processing all possible patterns. This selective approach generates diverse problem situations while consuming minimal computational resources by focusing only on locally relevant data.
3Adaptability or versatility
If diverse and customized mysteries are generated, then user satisfaction is enhanced, but the complexity of data structure and inference chains increases
Solution Approach 1:
The system employs a universal data structure that can represent multiple types of problem situations (mysteries, puzzles, challenges) using the same framework of candidates, fact patterns, and chains of inference. This multi-functional approach enables customization across different game genres without requiring separate complex data structures for each type.
Solution Approach 2:
The data structure implements nesting by organizing elements hierarchically: the game world contains multiple candidates, each candidate is associated with multiple fact patterns, and these fact patterns form chains of inference. This nested organization manages complexity by containing detailed information within structured layers, allowing diverse customization while maintaining overall system coherence.
Data Source
AI summary
Systems, methods and computer-readable media are disclosed for providing a framework for generating problem situations in a computer game. In one embodiment, the framework includes a data structure representing solution candidates, fact patterns, and a solution designator. The data structure also includes a chain of inference between the solution designator and each candidate. The computer game may generate a problem situation from the data structure and present the problem situation to a user playing the computer game.