Gradient Descent-Trained Defensible Blackboard Architecture System: Implementation and Evaluation

Core Concepts

This paper proposes and evaluates a system that expands a gradient descent-trained defensible expert system to be a defensible Blackboard Architecture, adding activation functions and a best path-based optimization algorithm.

Abstract

The paper presents a system that combines the capabilities of expert systems and neural networks. It builds upon previous work on "defensible" systems that use gradient descent to optimize a rule-fact network, preventing the learning of spurious correlations. The key aspects of the proposed system are: It extends the defensible expert system to a Blackboard Architecture, adding the capability to actuate the environment through actions. It incorporates activation functions, similar to those used in neural networks, to regulate the range of possible output values. It proposes a best path-based optimization algorithm to train the system using gradient descent. The paper evaluates the performance of this system through extensive simulations, analyzing the impact of various parameters such as network size, training iterations, training velocity, and trigger thresholds. Key findings include: Increasing the number of rules in the network reduces the error, but with diminishing returns. Training the system can improve performance, but care must be taken to avoid over-training. Selecting the best result from the training process is important. Increasing the training velocity consistently improves performance. Applying trigger thresholds to restrict the range of output values can reduce error, even without application-specific tuning. The runtime of the system does not correlate well with its error level, so runtime cannot be used as a surrogate metric for accuracy. Overall, the results demonstrate the potential of the proposed gradient descent-trained defensible Blackboard Architecture system and provide insights into its performance characteristics.

Stats

The system's average runtime increases at a greater-than-linear rate as the number of rules in the network increases. As the number of facts in the network increases, the average error first increases and then slightly decreases. Increasing the number of rules in the network decreases the average error, with diminishing returns. Increasing the number of actions in the network generally increases the average error.

Quotes

"A variety of forms of artificial intelligence systems have been developed. Two well-known techniques are neural networks and rule-fact expert systems." "Due to the growing prevalence of artificial intelligence systems, concerns have been raised that many techniques – and neural networks, in particular – lack understandability and accountability." "This defensible system was based on an expert system rule-fact network and, thus, lacked actualization capabilities. It also did not include the activation functions used by neural networks."

Key Insights Distilled From

Implementation and Evaluation of a Gradient Descent-Trained Defensible Blackboard Architecture System

by Jordan Milbr... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11714.pdf

Deeper Inquiries

How could the system be extended to handle dynamic changes in the network structure during runtime, such as the addition or removal of facts and rules?

To enable the system to handle dynamic changes in the network structure during runtime, such as adding or removing facts and rules, several approaches can be considered: Dynamic Network Modification Functions: Implement functions within the system that allow for the addition or removal of facts and rules during runtime. These functions should ensure that the network remains consistent and functional after any modifications. Network Versioning: Implement a versioning system for the network structure, where changes are tracked and reversible. This allows for reverting to previous versions if new modifications lead to undesired outcomes. Rule and Fact Dependency Management: Develop a mechanism to manage dependencies between rules and facts. When adding or removing a rule or fact, ensure that dependent elements are appropriately adjusted to maintain network integrity. Real-time Training: Implement a mechanism for real-time training of the network to adapt to changes. This involves continuously updating the network weights and configurations based on incoming data and structural modifications. Automated Validation: Include automated validation checks to ensure that any dynamic changes do not compromise the overall functionality and performance of the system. This can involve running validation tests after each modification. User Interface for Network Editing: Provide a user interface that allows users to interactively add, remove, or modify facts and rules. This interface should ensure that changes are made in a controlled manner. By incorporating these strategies, the system can effectively handle dynamic changes in the network structure during runtime while maintaining stability and performance.

How could the system be adapted to handle uncertainty in the effects of actions, rather than assuming random impacts on the network?

To address uncertainty in the effects of actions within the system, instead of relying on random impacts, the following techniques could be implemented: Probabilistic Action Modeling: Develop probabilistic models for the effects of actions based on historical data or domain knowledge. These models can estimate the likelihood of different outcomes for each action. Bayesian Inference: Utilize Bayesian inference to update the system's beliefs about the effects of actions as new data becomes available. This allows for a more data-driven and adaptive approach to handling uncertainty. Reinforcement Learning: Implement reinforcement learning techniques to learn the effects of actions through trial and error. The system can adjust action strategies based on the observed outcomes and rewards. Sensitivity Analysis: Conduct sensitivity analysis to evaluate the impact of uncertain action effects on the network's performance. This analysis can help identify critical actions and their potential variability. Expert System Integration: Integrate expert knowledge into the system to provide insights into the potential effects of actions. Expert rules can guide decision-making in uncertain scenarios. Feedback Mechanisms: Establish feedback mechanisms that allow the system to learn from the actual outcomes of actions and adjust its predictions and strategies accordingly. By incorporating these techniques, the system can better handle uncertainty in the effects of actions, moving beyond random impacts to more informed and adaptive decision-making processes.

How could the system be adapted to automatically determine the optimal number of training iterations and training velocity for a given network configuration and application?

To automate the determination of the optimal number of training iterations and training velocity for a given network configuration and application, the following approaches can be considered: Hyperparameter Optimization: Implement automated hyperparameter optimization techniques, such as grid search, random search, or Bayesian optimization, to search for the best combination of training iterations and velocity. Cross-Validation: Use cross-validation methods to evaluate different combinations of training parameters and select the one that yields the best performance on validation data. Automated Machine Learning (AutoML): Leverage AutoML tools and frameworks that can automatically search for the optimal hyperparameters, including training iterations and velocity, based on predefined performance metrics. Performance Metrics: Define specific performance metrics for the network that capture its effectiveness and efficiency. Use these metrics to guide the optimization process and determine the optimal training parameters. Adaptive Learning Rates: Implement adaptive learning rate algorithms that adjust the training velocity based on the network's performance during training. This allows for dynamic optimization of the training process. Early Stopping: Integrate early stopping mechanisms that monitor the network's performance during training and halt the process when performance no longer improves, preventing overfitting. By incorporating these strategies, the system can automatically determine the optimal number of training iterations and training velocity for a given network configuration and application, leading to improved efficiency and performance.