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Hybrid Systems

This paper examines the integration of rule-based systems and neural networks, showcasing two notable hybrid techniques: Fu's system and the KBANN system. It discusses the characteristics of knowledge-based systems (KBS) and neural networks (NN), their hybrid approaches, and comparisons in handling erroneous rules and maintaining semantics. Fu’s system accepts rule correctness, while KBANN initializes with knowledge and adjusts weights for consistency. The findings highlight their unique strengths and weaknesses, offering insights into the future of hybrid systems in AI.

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Hybrid Systems

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  1. Hybrid Systems Two examples of the Combination of Rule-Based Systems and neural nets By Pieter Buzing

  2. Plan • Introduction: • Knowledge Based System vs Neural Net • Basic hybrid technique • Fu’s system • KBANN system • Comparison (rule improvement, semantics) • Conclusions

  3. Introduction • Knowledge Based System • Neural Network • Characteristics KBS & NN • Basic hybrid technique

  4. Knowledge Based System • Rule base and fact base • Facts   Conclusions • Certainty Factors [-1, 1] • IF smart AND ambitious rich • Given: CF(smart)=0.8 CF(ambitious)=0.5 • Conclude: CF(rich)=0.7*min(0.8, 0.5)=0.35 INFERENCE CF=0.7

  5. Neural Network • Nodes and connections • layers: input, hidden and output nodes • Aim: right weight vector for each connection • Trained with examples: minimize error

  6. Characteristics

  7. Basic hybrid technique Initialize the neural network with domain knowledge. So architecture and initial weight are now founded! Use the following mapping:

  8. Fu’s system (1989) • Proposed by: Li-Min Fu, Winsconsin • Objective: let NN deal with incorrect KB • Construction: conceptual network with CFs AND-nodes to maintain meaning • Training: backpropagation and hill-climbing because AND-function not differentiable • Error handling: identifies wrong rules • Semantics: rules always ‘visible’ in network

  9. KBANN system (1994) • Proposed by: Towell&Shavlik, Winsconsin • Objective: use KB to initialize a NN • Construction: conceptnode extra nodes and connections added • Training: backpropagation • Error handling: weight adjustment • Semantics: too many connections to make sense out of it

  10. Comparison (1) Coping with erroneous rules Fu considers rule incorrect when weight change exceeds a threshold KBANN deals with it implicitly, it alters the weight of a inconsistent rule Fu can identify malicious rules when 12% of rules is corrupted KBANN: outperforms standard NN with 10% big or 30% small changes

  11. Comparison (2) Maintainability of semantics Fu: every unit keeps its meaning KBANN: (random) units are added Fu: conjunction units hold their original semantic basis KBANN: all nodes are connected, so every node is a big ‘conjunction’ Fu’s weights are CFs. KBANN?

  12. Conclusions • Coping with erroneous rules • Fu can be used to verify rules: identify inconsistent ones. • KBANN handles it convincingly • Maintainability of semantics • Fu succeeds in comprehensibility goal • KBANN loses its semantics: mere starting base • Mind you: different goals, periods, domain

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