Assumption-Based Truth Maintenance Systems

2001 ◽  
pp. 183-195
Author(s):  
Weiru Liu
Keyword(s):  
Maintenance Systems ◽  
Truth Maintenance ◽  
Truth Maintenance Systems

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.

scholarly journals Explanation in Constraint Satisfaction: A Survey

2021 ◽  
Author(s):  
Sharmi Dev Gupta ◽  
Begum Genc ◽  
Barry O'Sullivan
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Boolean Satisfiability ◽  
Machine Learning Methods ◽  
General Field ◽  
History Of ◽  
Maintenance Systems ◽  
Truth Maintenance ◽  
Truth Maintenance Systems ◽  
Explanation Generation

Much of the focus on explanation in the field of artificial intelligence has focused on machine learning methods and, in particular, concepts produced by advanced methods such as neural networks and deep learning. However, there has been a long history of explanation generation in the general field of constraint satisfaction, one of the AI's most ubiquitous subfields. In this paper we survey the major seminal papers on the explanation and constraints, as well as some more recent works. The survey sets out to unify many disparate lines of work in areas such as model-based diagnosis, constraint programming, Boolean satisfiability, truth maintenance systems, quantified logics, and related areas.

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