Truth maintenance systems and their application for verifying Expert System Knowledge Bases

1992 ◽  
Vol 6 (1) ◽  
pp. 67-110 ◽  
Author(s):  
Neli P. Zlatareva
Keyword(s):  
Expert System ◽  
Knowledge Bases ◽  
Maintenance Systems ◽  
Truth Maintenance ◽  
Truth Maintenance Systems ◽  
System Knowledge

scholarly journals A Hierarchy of Tractable Subsets for Computing Stable Models

1996 ◽  
Vol 5 ◽  
pp. 27-52 ◽  
Author(s):  
R. Ben-Eliyahu
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Default Logic ◽  
Autoepistemic Logic ◽  
Computational Problem ◽  
Maintenance Systems ◽  
Truth Maintenance ◽  
Truth Maintenance Systems ◽  
Stable Models

Finding the stable models of a knowledge base is a significant computational problem in artificial intelligence. This task is at the computational heart of truth maintenance systems, autoepistemic logic, and default logic. Unfortunately, it is NP-hard. In this paper we present a hierarchy of classes of knowledge bases, Omega_1,Omega_2,..., with the following properties: first, Omega_1 is the class of all stratified knowledge bases; second, if a knowledge base Pi is in Omega_k, then Pi has at most k stable models, and all of them may be found in time O(lnk), where l is the length of the knowledge base and n the number of atoms in Pi; third, for an arbitrary knowledge base Pi, we can find the minimum k such that Pi belongs to Omega_k in time polynomial in the size of Pi; and, last, where K is the class of all knowledge bases, it is the case that union{i=1 to infty} Omega_i = K, that is, every knowledge base belongs to some class in the hierarchy.

MODIFIED BACKJUMPING ALGORITHMS FOR SOLVING CONSTRAINT SATISFACTION PROBLEMS

1999 ◽  
Vol 13 (01) ◽  
pp. 133-147
Author(s):  
U. CHOWDHURY ◽  
D. K. GUPTA
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Search Technique ◽  
Dead End ◽  
Consistent Manner ◽  
Maintenance Systems ◽  
Truth Maintenance ◽  
Truth Maintenance Systems ◽  
Is Failure ◽  
Selection Of

The backtracking algorithm is a prominent search technique in AI, particularly due to its use in Constraint Satisfaction Problems (CSPs), Truth Maintenance Systems (TMS), and PROLOG. In the context of CSPs, Dechter5 and Gashnig10 proposed two variants of the backtracking algorithm known as backjumping algorithms. One is graph-based and the other is failure-based backjumping algorithm. These algorithms attempt to backjump to the source of failure in case of a dead-end situation. This improves the backtracking performance. However, these algorithms are not consistent in the selection of the variable to backjump. In this paper, the modifications of both types of backjumping algorithms are proposed. These algorithms adopt a technique to select the variable to backjump in a consistent manner. This further increases the search efficiency in them. The merits of these modified algorithms are investigated theoretically. Experimental results on the zebra problem and random problems show that the modified algorithms give better results on most occasions.

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