AI Controller Using Episodic Memory for Dynamic Adaptation
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Solution Overview
Problem
Existing artificial intelligence controllers rely on codified decision-making structures that fail to adapt effectively to changing environments and new situations, lacking the ability to remember past experiences and adapt behavior in a human-like manner.
Innovation Solution
A controller employing artificial intelligence based on selection from stored episodic memory, which maintains a historical database of past experiences and uses randomized selection to choose actions, allowing it to remember successful past actions and adapt to new situations by periodically trying different approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If codified decision-making structures (neural networks, rules, or decision trees) are used, then the controller can generate optimal outputs for given inputs, but the controller fails to adapt effectively to changing environments and new situations
Solution Approach 1:
The controller stores episodic memories of past experiences and situations in advance, creating a repository of learned patterns before new situations arise. This preliminary accumulation of experience allows the controller to quickly adapt to new environments by retrieving and applying relevant past experiences, rather than starting from scratch with rigid codified rules.
Solution Approach 2:
The system transitions from static codified decision structures to a dynamic memory-based system that continuously learns and adapts. The episodic memory storage dynamically updates with new experiences, and the retrieval mechanism dynamically selects appropriate past experiences based on current situations, enabling the controller to evolve its behavior over time while maintaining reliability through proven patterns.
2Device complexity
If explicit knowledge bases or rules are used, then the decision-making structure can be codified, but the controller lacks the ability to remember past experiences and adapt behavior in a human-like manner
Solution Approach 1:
The controller copies human-like adaptive behavior by implementing an episodic memory system that mimics human experience storage and retrieval. Instead of relying on explicit codified knowledge bases, the system copies the fundamental mechanism of human learning—storing experiences in memory and retrieving them when needed—thereby achieving human-like adaptability without requiring complex explicit rule sets.
Solution Approach 2:
The patent replaces the mechanical system of explicit codified rules and knowledge bases with a memory-based retrieval system. This substitution eliminates the need for manual coding of decision logic while preserving adaptability, as the system naturally learns and adapts through experience storage and retrieval rather than through pre-programmed mechanical rule structures.
Data Source
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AI summary
A controller and corresponding method for operating a machine maintains a historical database including at least one sequence of parameters relating to the operating environment, corresponding actions taken, and corresponding outcomes of operation of the machine. The controller searches the database for episodes satisfying relevance criteria relative to a current sequence of parameters and then performs a randomized selection between two or more options, at least one of which is derived from similar episodes from the database. At least one control signal is then output to the machine indicating an action to be performed as determined based on the selected option.