Time Travel Simulation System with Temporal Event Segmentation
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Solution Overview
Problem
Current computer simulation systems lack the capability to effectively simulate scenarios involving time travel for agents and players, limiting their ability to interact and influence events across different time periods in a realistic and efficient manner.
Innovation Solution
The system allows agents and players to travel through time by providing a time window that spans the farthest reachable past and future, with events applied to agents within this window, enabling time travel, cloning, and conditional actions, and utilizing multiple time rates and AI systems for complex scenario simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the simulation allows time travel for agents and players, then the scenario simulation becomes more realistic and versatile, but the system complexity increases significantly
Solution Approach 1:
The patent divides the time window into discrete time steps and segments the timeline into past, present, and future zones. Events are processed in separate time windows, and agents are instantiated at specific time points. This segmentation allows the system to manage time travel complexity by processing events in manageable temporal chunks rather than handling the entire timeline simultaneously.
Solution Approach 2:
The patent introduces a temporal dimension to the simulation system, allowing agents and players to exist at multiple time points simultaneously. The system creates a multi-dimensional state space where time becomes another coordinate, enabling agents to be instantiated, moved, and manipulated across different temporal layers without disrupting the core simulation logic.
2Adaptability or versatility
If multiple events are applied to agents within the time window, then the simulation becomes more dynamic and realistic, but the computational processing time increases
Solution Approach 1:
The patent pre-processes and queues events before they need to be executed. Events are collected and organized in advance, and the system uses event queues to manage the execution order. This allows the simulation to maintain high scenario dynamics while reducing real-time computational overhead by preparing events beforehand.
Solution Approach 2:
The simulation operates in periodic time steps, processing events in cycles rather than continuously. The system updates the simulation state at discrete intervals, applying events in batches during each time step. This periodic processing reduces computational load compared to continuous event processing while maintaining realistic scenario dynamics.
3Adaptability or versatility
If the time window spans from farthest reachable past to future, then the simulation coverage is maximized, but the data consistency management becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor and adjust the simulation state across time windows. The system uses feedback loops to ensure data consistency by detecting conflicts and resolving them through defined rules. This feedback approach maintains reliability across the extended time window by constantly verifying and correcting data integrity.
Solution Approach 2:
The system prepares and buffers data structures in advance to handle potential consistency issues. By pre-allocating memory for time-traveled agents and pre-processing data validation, the system creates a cushion against data consistency problems. This beforehand preparation allows the system to manage extended time windows without encountering unforeseen data integrity issues.
4Adaptability or versatility
If agents can be cloned and moved across time, then the simulation capability is enhanced, but the memory requirements increase
Solution Approach 1:
The patent uses copying to create agent instances at different time points. Instead of creating entirely new agents, the system copies existing agent data and state to new time slots. This copying approach enhances agent manipulation capability while minimizing memory usage by reusing agent templates and only storing differences between time points.
Solution Approach 2:
The system implements a nested structure where agent data at different time levels are organized hierarchically. Time-traveled agents are nested within the parent agent's data structure, with temporal information stored as metadata. This nesting allows the system to manage multiple agent instances across time without proportionally increasing memory requirements, as nested structures share common data fields.
Data Source
AI summary
A scenario is simulated by providing an agent that is an object in the scenario, providing a time window for the scenario that spans the farthest reachable past time and the farthest reachable future time with respect to a present time for the agent and/or at least one player that interacts with the simulated scenario, providing at least one event that represents a change in at least one attribute of the agent, and updating a state of the scenario by applying the at least one event to the agent within the time window.