%0 Conference Paper %1 MarinovAlexievDjonev1994-BCPN %A Marinov, Georgi %A Alexiev, Vladimir %A Djonev, Yavor %B Artifical Intelligence: Methodology, Systems, and Applications (AIMSA'94) %C Sofia, Bulgaria %D 1994 %E Jorrand, P. %E Sgurev, V. %I World Scientific Publishing %K constraint_propagation expert_system inference knowledge-based_system %P 109-118 %R 10.5555/212090.212113 %T Boolean Constraint Propagation Networks %U http://rawgit2.com/VladimirAlexiev/my/master/pubs/MarinovAlexievDjonev1994-BCPN.pdf %X This paper describes a particular inference mechanism which has been successfully used for the implementation of an expert system and a generic shell supporting consulting-type expert systems. The main features of Boolean Constraint Propagation Networks (BCPN) are: the inference flows in all directions, unlike inference modes of forward or backward chaining systems; all possible consequences of a fact are derived as soon as the user enters the fact, therefore the system is very interactive; if the user withdraws an assertion then all propositions depending on it are retracted; the inference architecture is simple and uniform. After a general description of BCPN we give an account of the problems encountered and the approaches we used to solve them. Some possible extensions of the mechanism and its applicability to various areas are also discussed. The current version of BCPN is written in C++ and took about one man-year to develop. %@ 981-02-1853-2

@inproceedings{MarinovAlexievDjonev1994-BCPN, abstract = {This paper describes a particular inference mechanism which has been successfully used for the implementation of an expert system and a generic shell supporting consulting-type expert systems. The main features of Boolean Constraint Propagation Networks (BCPN) are: the inference flows in all directions, unlike inference modes of forward or backward chaining systems; all possible consequences of a fact are derived as soon as the user enters the fact, therefore the system is very interactive; if the user withdraws an assertion then all propositions depending on it are retracted; the inference architecture is simple and uniform. After a general description of BCPN we give an account of the problems encountered and the approaches we used to solve them. Some possible extensions of the mechanism and its applicability to various areas are also discussed. The current version of BCPN is written in C++ and took about one man-year to develop.}, added-at = {2021-08-25T16:07:36.000+0200}, address = {Sofia, Bulgaria}, author = {Marinov, Georgi and Alexiev, Vladimir and Djonev, Yavor}, biburl = {https://www.bibsonomy.org/bibtex/28c3b267de79067590bf56f1b887769ac/valexiev}, booktitle = {Artifical Intelligence: Methodology, Systems, and Applications (AIMSA'94)}, doi = {10.5555/212090.212113}, editor = {Jorrand, P. and Sgurev, V.}, interhash = {2a82a126113d0cdcbb0974912bec317b}, intrahash = {8c3b267de79067590bf56f1b887769ac}, isbn = {981-02-1853-2}, keywords = {constraint_propagation expert_system inference knowledge-based_system}, month = sep, pages = {109-118}, publisher = {World Scientific Publishing}, timestamp = {2021-08-25T16:07:36.000+0200}, title = {{Boolean Constraint Propagation Networks}}, url = {http://rawgit2.com/VladimirAlexiev/my/master/pubs/MarinovAlexievDjonev1994-BCPN.pdf}, year = 1994 }