scholarly journals The CIFF Proof Procedure for Abductive Logic Programming with Constraints

2004 ◽  
pp. 31-43 ◽  
Author(s):  
U. Endriss ◽  
P. Mancarella ◽  
F. Sadri ◽  
G. Terreni ◽  
F. Toni
Keyword(s):  
Logic Programming ◽  
Abductive Logic ◽  
Abductive Logic Programming ◽  
Proof Procedure

scholarly journals The CIFF proof procedure for abductive logic programming with constraints: Theory, implementation and experiments

2009 ◽  
Vol 9 (6) ◽  
pp. 691-750 ◽  
Author(s):  
PAOLO MANCARELLA ◽  
GIACOMO TERRENI ◽  
FARIBA SADRI ◽  
FRANCESCA TONI ◽  
ULLE ENDRISS
Keyword(s):  
Logic Programming ◽  
State Of The Art ◽  
Constraint Logic Programming ◽  
Constraint Solver ◽  
Abductive Logic ◽  
Abductive Logic Programming ◽  
Proof Procedure ◽  
Answer Set

AbstractWe present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state-of-the-art abductive systems and answer set solvers and showing how to use it to program some applications.

scholarly journals The IFF proof procedure for abductive logic programming

1997 ◽  
Vol 33 (2) ◽  
pp. 151-165 ◽  
Author(s):  
Tze Ho Fung ◽  
Robert Kowalski
Keyword(s):  
Logic Programming ◽  
Abductive Logic ◽  
Abductive Logic Programming ◽  
Proof Procedure

scholarly journals Nonground Abductive Logic Programming with Probabilistic Integrity Constraints

2021 ◽  
pp. 1-18
Author(s):  
ELENA BELLODI ◽  
MARCO GAVANELLI ◽  
RICCARDO ZESE ◽  
EVELINA LAMMA ◽  
FABRIZIO RIGUZZI
Keyword(s):  
Logic Programming ◽  
Integrity Constraints ◽  
Uncertain Information ◽  
Abductive Logic ◽  
Abductive Logic Programming ◽  
Proof Procedure ◽  
Application Fields ◽  
Suitable Framework ◽  
Probabilistic Logical Models ◽  
Distribution Semantics

Abstract Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are a suitable framework to handle uncertain information, and in the last decade many probabilistic logical languages have been proposed, as well as inference and learning systems for them. In the realm of Abductive Logic Programming (ALP), a variety of proof procedures have been defined as well. In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. In particular, we consider an ALP program enriched with integrity constraints à la IFF, possibly annotated with a probability value. We first present the overall abductive language and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness.

Dischargeable Obligations in the 𝒮CIFF Framework

2020 ◽  
Vol 176 (3-4) ◽  
pp. 321-348
Author(s):  
Marco Alberti ◽  
Marco Gavanelli ◽  
Evelina Lamma ◽  
Fabrizio Riguzzi ◽  
Ken Satoh ◽  
...  
Keyword(s):  
Logic Programming ◽  
Operational Semantics ◽  
Civil Code ◽  
Normative Reasoning ◽  
Abductive Logic ◽  
Abductive Logic Programming ◽  
New Knowledge ◽  
Proof Procedure ◽  
Suitable Framework

Abductive Logic Programming (ALP) has been proven very effective for formalizing societies of agents, commitments and norms, in particular by mapping the most common deontic operators (obligation, prohibition, permission) to abductive expectations. In our previous works, we have shown that ALP is a suitable framework for representing norms. Normative reasoning and query answering were accommodated by the same abductive proof procedure, named 𝒮CIFF. In this work, we introduce a defeasible flavour in this framework, in order to possibly discharge obligations in some scenarios. Abductive expectations can also be qualified as dischargeable, in the new, extended syntax. Both declarative and operational semantics are improved accordingly, and proof of soundness is given under syntax allowedness conditions Moreover, the dischargement itself might be proved invalid, or incoherent with the rules, due to new knowledge provided later on. In such a case, a discharged expectation might be reinstated and hold again after some evidence is given. We extend the notion of dischargement to take into consideration also the reinstatement of expectations. The expressiveness and power of the extended framework, named 𝒮CIFF𝒟, is shown by modeling and reasoning upon a fragment of the Japanese Civil Code. In particular, we consider a case study concerning manifestations of intention and their rescission (Section II of the Japanese Civil Code).

Security Protocols Verification in Abductive Logic Programming: A Case Study

2006 ◽  
pp. 106-124 ◽  
Author(s):  
Marco Alberti ◽  
Federico Chesani ◽  
Marco Gavanelli ◽  
Evelina Lamma ◽  
Paola Mello ◽  
...  
Keyword(s):  
Logic Programming ◽  
Security Protocols ◽  
Abductive Logic ◽  
Abductive Logic Programming

Accountable Protocols in Abductive Logic Programming

2018 ◽  
Vol 18 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Marco Gavanelli ◽  
Marco Alberti ◽  
Evelina Lamma
Keyword(s):  
Logic Programming ◽  
Abductive Logic ◽  
Abductive Logic Programming

Legal Reasoning Using Abductive Logic Programming

1997 ◽  
Vol 1 (2) ◽  
pp. 114-120
Author(s):  
Takashi Kanai ◽  
◽  
Susumu Kunifuji
Keyword(s):  
Logic Programming ◽  
Knowledge Base ◽  
Legal Reasoning ◽  
The Other ◽  
Abductive Logic ◽  
Reasoning System ◽  
Abductive Logic Programming ◽  
Reasoning Systems ◽  
Legal Arguments

In this paper, we propose a new legal reasoning system using abductive logic programming (ALP). The system can deal with ambiguities of described facts and exceptions which is not described in relevant articles. In addition, the goal, queried to a legal reasoning system, differs in compliance with whether the user is a plaintiff or defendant. In usual deductive legal reasoning systems, there are two major problems in treating legal arguments. One is that legal facts usually have ambiguities, and the other is that two conflicting conclusions must be derived from one knowledge base, depending on whether a plaintiff of defendant is involved. To overcome these difficulties, abductive logic programming is used in our legal reasoning system, which can deal with implicit exceptions and generate presumptions according to the user’s needs.

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