Ghost Selection System for Video Game Challenge Matching
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Solution Overview
Problem
Conventional ghost systems in videogames often fail to provide a well-matched challenge, leading to frustration as the recorded best performance ghost either gets left behind or pulls too far ahead, losing visibility and competitiveness.
Innovation Solution
The system selects a plurality of recorded ghost instances based on predetermined threshold distances and velocities to ensure a competitive and visible ghost presence, dynamically replacing or supplementing the initial ghost with a second ghost that is closer and faster or slower to match the player's performance, thereby maintaining a challenging and engaging competition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses the user's best recorded performance as the ghost, then the ghost provides a challenging competitor, but the ghost becomes frustrating when the user cannot replicate the best performance
Solution Approach 1:
The system changes the selection parameter from purely performance-based to a hybrid of performance and temporal proximity. By selecting ghosts based on both performance level and time elapsed since recording, the system maintains challenge quality while reducing frustration through more relevant comparisons.
Solution Approach 2:
The ghost selection system becomes dynamic by continuously adjusting which recorded performances are presented based on real-time player performance. The system adapts the ghost selection criteria during gameplay to maintain optimal challenge level, transitioning from static best-performance-only selection to adaptive multi-criteria selection.
2Loss of information
If the ghost is displayed as a translucent representation of the player's avatar, then the ghost provides visual comparison, but the ghost loses visibility when too far ahead or behind the player
Solution Approach 1:
The system performs preliminary selection of multiple candidate ghosts based on performance criteria before gameplay begins. By pre-selecting a pool of appropriate ghosts and sorting them by performance level, the system ensures that a visible and competitive ghost is always available without needing to adjust during play.
Solution Approach 2:
The system uses feedback from real-time player performance to dynamically adjust ghost selection. When the player's performance changes, the system receives feedback and updates the ghost selection accordingly, ensuring the ghost remains within a visible and competitive range throughout the gameplay.
3Adaptability or versatility
If the system records and displays multiple ghost instances with different performance levels, then the system provides varied challenge levels, but the system complexity increases
Solution Approach 1:
The system segments the ghost selection process into distinct phases: recording phase (capturing multiple performances), selection phase (filtering and sorting candidates), and display phase (presenting appropriate ghosts). This segmentation manages complexity by breaking down the overall system into manageable functional modules.
Solution Approach 2:
The system records and stores more ghost instances than immediately needed, creating a surplus pool of candidate ghosts. By capturing excessive data during the recording phase, the system can selectively present only the necessary number of ghosts during play, simplifying the display logic while maintaining versatility.
Data Source
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AI summary
A method of selecting a plurality of recorded instances of play at a server for interaction with a user at an entertainment device comprises the steps of receiving from an entertainment device average performance data for a player within a virtual environment, selecting a subset of a plurality of recorded instances of play through a virtual environment, and transmitting to the entertainment device data corresponding to the subset of the plurality of recorded instances; wherein the step of selecting a subset of the plurality of recorded instances of play to the virtual environment comprises the steps of calculating a ranking of the player within an ordered list of recorded instances of play based upon the average performance data, and selecting a subset of recorded instances of play according to a predetermined distribution centred upon the calculated ranking.