A Kind of Resolution Based on NN
Resolution principle is a single rule of inference for a test of unsatisfiability. It is based on conjunctive normal form (in shortly CNF), also called as clause set (atomic formulae or their negations is called a literal, disjunction of literal is called a clause, conjunction of clause is consider as a clause set). Many modified resolution methods have been raised, such as semantic resolution, ordered linear resolution, binary resolution, locking restriction resolution, etc. Many of them are based on this ideal: beginning with a clause, then use continually result of resolution, at last, get a conclusion of inference. If the conclusion is an empty clause, then the clause set is unsatisfiable; if the conclusion is not an empty clause, then the clause set is satisfiable.Neural network, simply noted NN, has the strong points are their learning capabilities and their distributed structure that allows for highly parallel software or hardware implementations. In this paper, we will consider the following ideal: one hand, we discuss the classical resolution, and point out that a DNF equal a neural network (a DNF is transformed from q CNF by using distributive law of ∨ to ∧). On the other hand, once a clause set S be transformed a neural network, we can construct learning algorithm of the NN, and implement resolution by using the NN.
Zheng Pei Haiming Li Yang Xu
Intelligence Control Development Center Southwest Jiaotong University Chengdu, Sichuan, P.R.China ,610031
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
979-984
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）