Automated Operator Inference for Planning Problems
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Solution Overview
Problem
Current model-based reinforcement learning methods are opaque and difficult to integrate with external knowledge bases, and operators for planning problems are typically manually handcrafted by experts, making them inefficient and limited in data efficiency.
Innovation Solution
A computer-implemented method for inferring operators with preconditions and effects using a set of examples, involving variable lifting, validity label computation, and training a model to output preconditions and effects, enabling the inference of operators even in noisy environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators are manually handcrafted by experts, then the planning problem can be solved with guaranteed correctness, but the process is inefficient and limited in data efficiency
Solution Approach 1:
The patent replaces the manual mechanical process of handcrafting operators by experts with an automated machine learning system. The system uses neural networks to automatically learn operators from state transition examples, substituting human expert labor with computational automation while maintaining reliability through learned preconditions and effects.
Solution Approach 2:
The system enables self-service by allowing the planning domain to automatically generate its own operators from observed state transitions without requiring external expert intervention. The neural network learns directly from data, making the operator creation process autonomous and scalable.
2Productivity
If current model-based reinforcement learning methods are used, then data efficiency is improved, but the models remain opaque and difficult to integrate with external knowledge bases
Solution Approach 1:
The patent segments the operator representation into distinct components: preconditions and effects. This segmentation allows the model to be more interpretable and easier to integrate with external knowledge bases while maintaining the data efficiency of learned representations. Each component can be independently validated and combined with domain knowledge.
Solution Approach 2:
The system changes the parameters of the model by representing operators in terms of learnable preconditions and effects rather than opaque neural network weights. This parameter transformation makes the learned models more interpretable and compatible with traditional planning frameworks and external knowledge bases.
3Adaptability or versatility
If operators are inferred from examples with noise in observed state, then the system becomes more robust to real-world conditions, but the accuracy of operator inference may deteriorate
Solution Approach 1:
The patent converts the harmful effect of noise in observed states into a benefit by training the neural network to be robust to such noise. The system learns to infer accurate operators even when input examples contain observational noise, effectively using the noisy training data to create a more resilient model that generalizes better to real-world conditions.
Data Source
AI summary
A method for inferring an operator including a precondition and an effect of the operator for a planning problem is disclosed. In the method, a set of examples, each of which includes a base state, an action and a next state after performing the action in the base state is prepared. In the method, variable lifting is performed in relation to the set of examples. In the method, a validity label is computed for each example in the set of examples. In the method, a model is trained by using the set of examples with the validity label so that the model is configured to receive an input state and a representation of an input action and output at least validity of the input action for the input state. In the method, the precondition of the operator based on the model and the effect of the operator are outputted.