A.V.Gavrilov, Novosibirsk State Technical University,
630092 Novosibirsk 92, K.Marx av., 20,
The actual problem of AI development is combination of traditional logic proceeses in expert systems (ES) and associative adaptive processes in artificial neural networks (ANN). Two kinds of such combination are described in this paper.
First of ones is based on constructing of expert system from two parts : the logic expert system ( LES ) and the program model of ANN, as in brain. This kind of this “two-semi-sphere” architecture is implemented in the expert shell .
Second one is based on the implementation of any properties of neurons in usual method of knowledge representation, such as methods of rules, frames, semantic nets. In particular, the rules are choosed for the implementation in expert system for professional orientation - choosing of profession and diagnostic of abilities for defined profession.
Architecture of the expert shell based on the artificial neural network.
LES deals with logic processing and user interface.
This subsystem may influence on ANN in consider of results of analize of it outputs.Artificial Neural Network are de-signed for set of associative links between facts or hypo-thesis.
To choose of architecture principles of LES we must have in mind that the different complex methods of organizing Expert Systems ( such as the representation of fuzzy knowledge ) are the consequance of absence of such device as Artificial Neural Network. So it seems we may utily the simplest principles in the architecture of LES. The main demand to this architecture is the orientation to parallel asynchronouse deduction with possibility to remove obtained conclusions.
The following architecture principles of LES may be formulated:
· the mechanizm of black board for connection between sources and receivers of knowledges;
· the knowledges transmitted across the black board are facts and hypothesis formed as triplet “object-attribute-value” or the part of this triplet ( for example, “object” or “atribute-value”);
· the sources and receivers of facts and hypothesis are rules with representation of indetermination;
· one of sources and receivers of facts is Artificial Neural Network;
· to send of punishment signal to ANN for actuaating of adaptation ( changing of state) of it the special fact is used;
· the punishment signal may be the result of succesful applying of any rule or result of analyzing of facts generated by ANN;
· to transfer facts to inputs of ANN the dictionary and coding/decoding are used.
Any rule of LES is started by Manager of the Black Board ( MBB ) after appearance ( from another rule or ANN ) of corresponding fact or hypothesis.
In this Expert System the architecture of Artificial Neural Network proposed in [2,3] is used. This architecture differ from most known ones by view on neural element as the simple perceptron learning to recognize the simple image - binary input code, but not simple threshold element.The Neural Element (NE) is described by threshold, key, input links with another NE and input link with another NE, from which one gets the punishment signal.
The neural network works in descrete time, i.e. for time t the outputs of all elements are determined by inputs in t-1.
Two software platforms are used for an implementation of this Expert Shell: PDC-Prolog in MS DOS and Borland C++ in MS Windows.
Architecture of the expert system for professional orientation.
This expert system solves following tasks:
· the testing of existing psichological haracterictics,
· the choosing of more suitable profession,
· the testing of ability to deal with any profession,
· the explanation of reasons for refusal from any profession,
· the choosing of any analog profession.
To include the parts of any psichological tests in know-ledge base the nontradional kind of intepretation of the rules was applied in this ES. This form of the interpretation is based on the threshold and the addition with weghts for making of conclusion. The rule in this ES is described by the following syntacsis:
<Condition i> <CNF>
<Conclusion j> <CNF>
Here CNF in condition has semantic depending on the kind of interpretation of rule. In case of usual logical interpretation CNF is the threshold of the confidence factor and condition is true if the confidence factor of corresponding fact greater then it.
In case of addition interpretation CNF is the weght of the condition and the conclusion is formed always with CNF calcu-lated from confidencies of conditions. The combination of rules with 2-nd and 1-st kinds of inter-pretation is modelling the neuron with distributed functions between some rules.
1) Gavrilov A.V., Novickaja J,V. The Expert Shell based on the Artificial Neural Network. - NITS’94, Penza, 1994, pp. 43-45.
2) Gavrilov A.V. An Architecture of Neurocomputer for Image Recognition& - Latvian Signal Processing International Conference (LSPIC’90).Proceedings.- Riga,1990,pp. 306-308.
3) Gavrilov A.V. An Architecture of Neurocomputer for Image Recognition. - Neural Network World, 1991,N.1, pp.59-60.