Name
Abstract | ||
|---|---|---|
Some issues in executing rule-based systems on parallel processor systems are addressed here. A MIMD shared memory multiprocessor model is first considered for running rule-based expert systems. Rule-based expert systems are modelled by state space and AND/OR graphs. The interdependences among rules are analyzed to guide rule-base partitioning and assignment as well as parameter allocation to memory banks. Also, methods for eliminating the dependences and for avoiding indeterminacy are proposed. A novel architecture is also proposed for the parallel execution of expert systems. This architecture has a regular mesh structure. It assembles a neural network and is thus named the generalized neural network. Execution and task decomposition of expert systems on this architecture are also discussed in this paper. |
Year | DOI | Venue |
|---|---|---|
1989 | 10.1016/0950-7051(89)90005-1 | Knowledge-Based Systems |
Keywords | Field | DocType |
parallel processing,rule-based expert system,rule based partitioning assignment,parameter allocation,input/output dependence,indeterminacy,interactive application,real-time application,generalized neural network,competition | Memory bank,Architecture,Computer science,Expert system,Parallel processing,Rule based expert system,Artificial intelligence,Artificial neural network,State space,Machine learning,Distributed computing,MIMD | Journal |
Volume | Issue | ISSN |
2 | 1 | 0950-7051 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Li | 1 | 0 | 0.34 |
| Luyuan Fang | 2 | 48 | 9.89 |
| William H. Wilson | 3 | 32 | 9.25 |