scholarly journals Syntax-Preserving Belief Change Operators for Logic Programs

2018 ◽  
Vol 19 (2) ◽  
pp. 1-42
Author(s):  
Sebastian Binnewies ◽  
Zhiqiang Zhuang ◽  
Kewen Wang ◽  
Bela Stantic
Keyword(s):  
2013 ◽  
Vol 14 (6) ◽  
pp. 869-907 ◽  
Author(s):  
MARTIN SLOTA ◽  
JOÃO LEITE

AbstractLogic programs under the stable model semantics, or answer-set programs, provide an expressive rule-based knowledge representation framework, featuring a formal, declarative and well-understood semantics. However, handling the evolution of rule bases is still a largely open problem. The Alchourrón, Gärdenfors and Makinson (AGM) framework for belief change was shown to give inappropriate results when directly applied to logic programs under a non-monotonic semantics such as the stable models. The approaches to address this issue, developed so far, proposed update semantics based on manipulating the syntactic structure of programs and rules.More recently, AGM revision has been successfully applied to a significantly more expressive semantic characterisation of logic programs based onSE-models. This is an important step, as it changes the focus from the evolution of a syntactic representation of a rule base to the evolution of its semantic content.In this paper, we borrow results from the area of belief update to tackle the problem of updating (instead of revising) answer-set programs. We prove a representation theorem which makes it possible to constructively define any operator satisfying a set of postulates derived from Katsuno and Mendelzon's postulates for belief update. We define a specific operator based on this theorem, examine its computational complexity and compare the behaviour of this operator with syntactic rule update semantics from the literature. Perhaps surprisingly, we uncover a serious drawback of all rule update operators based on Katsuno and Mendelzon's approach to update and onSE-models.


1990 ◽  
Author(s):  
Chitta Baral ◽  
Jorge Lobo ◽  
Jack Minker
Keyword(s):  

1987 ◽  
Vol 10 (1) ◽  
pp. 1-33
Author(s):  
Egon Börger ◽  
Ulrich Löwen

We survey and give new results on logical characterizations of complexity classes in terms of the computational complexity of decision problems of various classes of logical formulas. There are two main approaches to obtain such results: The first approach yields logical descriptions of complexity classes by semantic restrictions (to e.g. finite structures) together with syntactic enrichment of logic by new expressive means (like e.g. fixed point operators). The second approach characterizes complexity classes by (the decision problem of) classes of formulas determined by purely syntactic restrictions on the formation of formulas.


1990 ◽  
Vol 13 (4) ◽  
pp. 465-483
Author(s):  
V.S. Subrahmanian

Large logic programs are normally designed by teams of individuals, each of whom designs a subprogram. While each of these subprograms may have consistent completions, the logic program obtained by taking the union of these subprograms may not. However, the resulting program still serves a useful purpose, for a (possibly) very large subset of it still has a consistent completion. We argue that “small” inconsistencies may cause a logic program to have no models (in the traditional sense), even though it still serves some useful purpose. A semantics is developed in this paper for general logic programs which ascribes a very reasonable meaning to general logic programs irrespective of whether they have consistent (in the classical logic sense) completions.


2021 ◽  
Author(s):  
Antti Gronow ◽  
Maria Brockhaus ◽  
Monica Di Gregorio ◽  
Aasa Karimo ◽  
Tuomas Ylä-Anttila

AbstractPolicy learning can alter the perceptions of both the seriousness and the causes of a policy problem, thus also altering the perceived need to do something about the problem. This then allows for the informed weighing of different policy options. Taking a social network perspective, we argue that the role of social influence as a driver of policy learning has been overlooked in the literature. Network research has shown that normatively laden belief change is likely to occur through complex contagion—a process in which an actor receives social reinforcement from more than one contact in its social network. We test the applicability of this idea to policy learning using node-level network regression models on a unique longitudinal policy network survey dataset concerning the Reducing Deforestation and Forest Degradation (REDD+) initiative in Brazil, Indonesia, and Vietnam. We find that network connections explain policy learning in Indonesia and Vietnam, where the policy subsystems are collaborative, but not in Brazil, where the level of conflict is higher and the subsystem is more established. The results suggest that policy learning is more likely to result from social influence and complex contagion in collaborative than in conflictual settings.


2002 ◽  
Vol 37 (3) ◽  
pp. 63-74
Author(s):  
Lunjin Lu

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