State Navigator for Autonomous Decision-Making
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Solution Overview
Problem
Current machine learning technologies are inadequate for achieving efficient, robust, interpretable, and dependable operations and navigation in physical or data/state/decision spaces, particularly in autonomous systems, lacking in explainability and reliability.
Innovation Solution
The development of novel concepts, formulations, algorithms, and frameworks that enable state navigation by modeling intelligent systems as compositions of state components, using participation matrices and association strength measures to evaluate and navigate through state spaces, allowing for knowledgeable and autonomous decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current machine learning technologies are used for autonomous navigation, then the system can operate with existing tools, but the system lacks reliability, interpretability, and dependability
Solution Approach 1:
The system segments the state space into discrete states and transitions, breaking down complex navigation problems into manageable state transitions. This segmentation enables reliable tracking and interpretation of system behavior while maintaining manageable complexity through structured state representation
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring current states, comparing them with target states, and adjusting transitions accordingly. This feedback loop enhances reliability through iterative refinement and provides interpretability by making the decision-making process observable and traceable
2Adaptability or versatility
If complex algorithms are used to improve decision-making quality, then the system becomes more intelligent, but the system becomes less interpretable and explainable
Solution Approach 1:
The system employs dynamic programming to find optimal policies in state spaces, allowing adaptive decision-making while maintaining interpretability through the structured Bellman equations. The dynamic approach enables versatility in handling different navigation scenarios while the mathematical framework provides clear explanation of decision logic
Solution Approach 2:
The system transitions from continuous space navigation to discrete state space navigation, adding a dimensional transformation that simplifies the problem structure. This dimensionality change enables complex decision-making through discrete state transitions while maintaining interpretability through the explicit state-space representation
3Adaptability or versatility
If the system navigates through complex state spaces, then the system can handle diverse scenarios, but the computational efficiency decreases
Solution Approach 1:
The system performs preliminary actions by pre-computing value functions and policies for states that have been visited or are likely to be encountered. This preliminary computation stores information that can be quickly retrieved during actual navigation, enabling efficient handling of diverse scenarios without re-computing from scratch
Solution Approach 2:
The system uses model-based approaches where a simplified model of the environment is copied and used for planning and prediction. This copied model allows the system to simulate and evaluate potential actions without executing them in the real system, improving computational efficiency while maintaining versatility
Data Source
AI summary
Methods and systems are given to build and enable systems to acquire knowledge from bodies of data in order to become capable of showing sane, rational, and credible behavior or output. Such systems includes software and/or hardware artifacts and/or stationary or mobile machines such as vehicles, robots, transportation systems, and in general systems with intelligent state-navigation capabilities. Aspects of this disclosure are to provide technical frame- works, methods, and systems to build artificially intelligent beings with explainability and interpretability.