Use this URL to cite or link to this record in EThOS:  http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238211 
Title:  Parallel algorithms for free and associativecommutative unification  
Author:  Hains, Gaétan 
ISNI:
0000 0001 3524 1734


Awarding Body:  University of Oxford  
Current Institution:  University of Oxford  
Date of Award:  1989  
Availability of Full Text: 


Abstract:  
A survey of algorithms for free unification is given, followed by an overview of the computability and complexity of unification problems. Secondorder unification is known to be undecidable, and a proof is given that the firstorder problem is also undecidable under an arbitrary set of axioms. A new systolic algorithm is introduced for term minimisation or term compaction. This is a generalpurpose tool for systems using structure sharing. Apart from time and space savings, its use allows subterms to be tested for equality in constant time. The use of compact terms greatly simplifies free term matching and gives rise to a lineartime algorithm with lower processing overheads than the PatersonWegman unification algorithm. A sublineartime solution to the same problem is also given, assuming preloaded data. No existing algorithm for free unification has a sublineartime implementation and this is related to the notion of a sparse Pcomplete problem. The complexity of restricted associativecommutative term matching is analysed. Contrary to an earlier conjecture the problem is NPcomplete if variables occur at most twice but their number is unrestricted. Parallel methods are suggested as efficient solutions for the  tractable  linear and 1variable versions of the problem. Results presented here should be useful in the implementation of fast symbolic ma nipulation systems.


Supervisor:  McColl, Bill  Sponsor:  Not available  
Qualification Name:  Thesis (Ph.D.)  Qualification Level:  Doctoral  
EThOS ID:  uk.bl.ethos.238211  DOI:  Not available  
Keywords:  Computer science (mathematics) ; automated deduction ; unification ; complexity ; algorithms ; parallel processing  
Share: 