Abductive logic programming: Difference between revisions

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{{No footnotes|date=February 2015}}{{Programming paradigms}}

'''Abductive logic programming''' ('''ALP''') is a high-level [[knowledge representation|knowledge-representation]] framework that can be used to solve problems declaratively, based on [[abductive reasoning]]. It extends normal [[logic programming]] by allowing some predicates to be incompletely defined, declared as abducible predicates. Problem solving is effected by deriving hypotheses on these abducible predicates (abductive hypotheses) as solutions of problems to be solved. These problems can be either observations that need to be explained (as in classical abduction) or goals to be achieved (as in normal [[logic programming]]). It can be used to solve problems in diagnosis, [[planning]], natural language and [[machine learning]]. It has also been used to interpret [[negation as failure]] as a form of abductive reasoning.

==Syntax==

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* P is a logic program of exactly the same form as in logic programming

* A is a set of predicate names, called the abducible predicates

* IC is a set of [[first-order logic|first-order classical formulae]].

Normally, the logic program P does not contain any clauses whose head (or conclusion) refers to an abducible predicate. (This restriction can be made without loss of generality.) Also in practice, many times, the [[integrity constraints]] in IC are often restricted to the form of denials, i.e. clauses of the form:

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* {{cite book |first1=D. |last1=Poole |first2=R. |last2=Goebel |first3=R. |last3=Aleliunas |chapter=Theorist: a logical reasoning system for defaults and diagnosis |editor1-first=Nick |editor1-last=Cercone |editor2-first=Gordon |editor2-last=McCalla |title=The Knowledge Frontier: Essays in the Representation of Knowledge |chapter-url=https://books.google.com/books?id=WRy1XVarSd4C&pg=PA331 |year=1987 |publisher=Springer |isbn=978-0-387-96557-4 |pages=331–352}}

* {{cite book |first1=A.C. |last1=Kakas |first2=P. |last2=Mancarella |chapter=Generalised Stable Models: A Semantics for Abduction |editor-first=L.C. |editor-last=Aiello |title=ECAI 90: proceedings of the 9th European Conference on Artificial Intelligence |publisher=Pitman |year=1990 |isbn=978-0273088226 |pages=385–391 }}

* {{cite journal |first1=L. |last1=Console |first2=D.T. |last2=Dupre |first3=P. |last3=Torasso |title=On the Relationship between Abduction and Deduction |journal=[[Journal of Logic and Computation]] |volume=1 |issue=5 |pages=661–690 |year=1991 |doi=10.1093/logcom/1.5.661 |citeseerx=10.1.1.31.9982 }}

* {{cite journal |first1=A.C. |last1=Kakas |first2=R.A. |last2=Kowalski |author2link = Robert Kowalski|first3=F. |last3=Toni |title=Abductive Logic Programming |journal=Journal of Logic and Computation |volume=2 |issue=6 |pages=719–770 |year=1993 |doi=10.1093/logcom/2.6.719 |citeseerx=10.1.1.37.3655 }}

* {{cite journal |first1=Marc |last1=Denecker |first2=Danny |last2=De Schreye |title=SLDNFA: An Abductive Procedure for Abductive Logic Programs |journal=[[Journal of Logic Programming]] |volume=34 |issue=2 |pages=111–167 |date=February 1998 |doi=10.1016/S0743-1066(97)00074-5 |citeseerx = 10.1.1.21.6503 }}

* {{cite journal |first1=M. |last1=Denecker |first2=A.C. |last2=Kakas |title=Special issue: abductive logic programming |journal=Journal of Logic Programming |volume=44 |issue=1–3 |pages=1–4 |date=July 2000 |doi=10.1016/S0743-1066(99)00078-3 |doi-access=free }}

* {{cite book |first1=M. |last1=Denecker |first2=A.C. |last2=Kakas |chapter=Abduction in Logic Programming |editor1-first=A.C. |editor1-last=Kakas |editor2-first=F. |editor2-last=Sadri |title=Computational Logic: Logic Programming and Beyond: Essays in Honour of Robert A. Kowalski |chapter-url=https://books.google.com/books?id=15umWyDVsRMC&pg=PA402 |year=2002 |publisher=Springer |isbn=978-3-540-43959-2 |pages=402–437 |volume=2407 |series=Lecture Notes in Computer Science}}

* {{cite journal |first=D. |last=Poole |title=Probabilistic Horn abduction and Bayesian networks |journal=[[Artificial Intelligence (journal)|Artificial Intelligence]]|volume=64 |issue=1 |pages=81–129 |year=1993 |doi=10.1016/0004-3702(93)90061-F |url=https://www.cs.ubc.ca/~poole/papers/pha-bn.pdf }}

* {{cite journal|first1=F. |last1=Esposito |first2=S. |last2=Ferilli |first3=T.M.A. |last3=Basile |first4=N. |last4=Di Mauro |title=Inference of abduction theories for handling incompleteness in first-order learning |journal=Knowl.Knowledge Inf.and Syst.Information Systems |volume=11 |issue=2 |pages=217–242 |date=February 2007 |doi=10.1007/s10115-006-0019-5 |s2cid=10699982 |url=http://www.di.uniba.it/~ndm/publications/files/esposito07kais.pdf |url-status=dead |archive-url=https://web.archive.org/web/20110717210259/http://www.di.uniba.it/~ndm/publications/files/esposito07kais.pdf |archive-date=2011-07-17 }}

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