Neurules: Improving the Performance of Symbolic Rules
نویسندگان
چکیده
In this paper, we present a method for improving the performance of classical symbolic rules. This is achieved by introducing a type of hybrid rules, called neurules, which integrate neurocomputing into the symbolic framework of production rules. Neurules are produced by converting existing symbolic rules. Each neurule is considered as an adaline unit, where weights are considered as significance factors. Each significance factor represents the significance of the associated condition in drawing the conclusion. A rule is fired when the corresponding adaline output becomes active. This significantly reduces the size of the rule base and, due to a number of heuristics used in the inference process, increases efficiency of the inferences.
منابع مشابه
Integrating Hybrid Rule-Based with Case-Based Reasoning
In this paper, we present an approach integrating neurule-based and case-based reasoning. Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist representation (adaline unit). Each neurule is represented as an adaline unit. One way that the neurules can be produced is from symbolic rules by merging the symbolic rules having the same conclusion. In thi...
متن کاملEfficiently Merging Symbolic Rules into Integrated Rules
Neurules are a type of neuro-symbolic rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Neurules exhibit characteristics such as modularity, naturalness and ability to perform interactive and integrated inferences. One way of producing a neurule base is through conversion of an existing symbolic rule base yielding an equivalent but more compa...
متن کاملIntegrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems
In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other han...
متن کاملMulti-inference with Multi-neurules
Neurules are a type of hybrid rules combining a symbolic and a connectionist representation. There are two disadvantages of neurules. The first is that the created neurule bases usually contain multiple representations of the same piece of knowledge. Also, the inference mechanism is rather connectionism oriented than symbolism oriented, thus reducing naturalness. To remedy these deficiencies, w...
متن کاملUpdating a Hybrid Rule Base with Changes to its Symbolic Source Knowledge
Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. One way that neurules (target knowledge) can be produced is by converting symbolic rules (source knowledge). However, source knowledge may change, so that updating corresponding target knowledge is necessary. Changes concern insertion of new and removal of old symbol...
متن کامل