AI State Exploration via Disallowed Node Transitions
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Solution Overview
Problem
Current AI technologies are limited by their inability to map and access disallowed states, leading to suboptimal or dangerous outcomes in certain applications, as they strictly adhere to rules without considering alternative scenarios.
Innovation Solution
The development of AI techniques that use a search algorithm to map trade space versus time, allowing access to disallowed states by jumping between nodes to find more favorable solutions, even if they are not initially reachable within the rules of the system, employing principal component analysis and sentiment differences to identify proximate and desirable pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current AI technologies strictly adhere to rules and map only allowed states, then system reliability and rule compliance are improved, but the ability to find optimal solutions and adapt to complex scenarios deteriorates
Solution Approach 1:
The patent applies dynamics by making the AI system's state exploration flexible and adaptive. The search algorithm dynamically adjusts which states to explore based on sentiment analysis and proximity calculations, transitioning between adhering to rules and exploring disallowed states depending on the situation's urgency and the potential benefit of breaking rules.
Solution Approach 2:
The patent changes the parameter of state accessibility by introducing a probability threshold mechanism. States are classified as allowed or disallowed based on probability parameters, but the system can transition to disallowed states when sentiment difference exceeds certain thresholds or when proximity to current state meets specific criteria, effectively changing the accessibility parameter dynamically.
2Adaptability or versatility
If AI systems explore all possible states including disallowed states, then solution optimality and adaptability are improved, but system complexity and computational requirements worsen
Solution Approach 1:
The patent applies partial action by selectively exploring only certain disallowed states rather than all possible states. The search algorithm uses sentiment analysis to identify promising disallowed states and proximity calculations to limit exploration to nearby states, performing partial exploration that balances optimality with computational feasibility.
Solution Approach 2:
The patent introduces sentiment analysis and proximity calculations as intermediary mechanisms that mediate between the current state and potential disallowed states. These intermediaries filter and prioritize which disallowed states to explore, reducing the computational burden of exploring all possible states while still finding optimal solutions when beneficial.
3Adaptability or versatility
If AI technologies map disallowed states and enable access through search algorithms, then the ability to handle limited information scenarios and find better solutions is improved, but the strict rule adherence and system predictability worsen
Solution Approach 1:
The patent applies preliminary action by pre-classifying states as allowed or disallowed based on probability thresholds before the search begins. This preliminary classification maintains rule adherence and predictability for common scenarios, while the search algorithm can selectively access disallowed states when sentiment analysis indicates a strong case for breaking rules, balancing predictability with adaptability.
Data Source
AI summary
Artificial intelligence (AI) techniques that map disallowed states and enable access to those states under certain conditions through a search algorithm are disclosed. In other words, scenario boundaries may be crossed by jumping from one scenario that is less desirable or even has no solution to another scenario that is more desirable.