Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonoto...
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Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic programming, default logic and autoepistemic logic. In this paper, we extend AFT to dealing with non -deterministic constructs that allow to handle indefinite information, represented e.g. by disjunctive formulas. This is done by generalizing the main constructions and corresponding results of AFT to non -deterministic operators, whose ranges are sets of elements rather than single elements. The applicability and usefulness of this generalization is illustrated in the context of disjunctive logic programming.
In this paper, we introduce methods of encoding propositional logic programs in vector spaces. Interpretations are represented by vectors and programs are represented by matrices. The least model of a definite program...
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In this paper, we introduce methods of encoding propositional logic programs in vector spaces. Interpretations are represented by vectors and programs are represented by matrices. The least model of a definite program is computed by multiplying an interpretation vector and a program matrix. To optimize computation in vector spaces, we provide a method of partial evaluation of programs using linear algebra. Partial evaluation is done by unfolding rules in a program, and it is realized in a vector space by multiplying program matrices. We perform experiments using artificial data and real data, and show that partial evaluation has the potential for realizing efficient computation of huge scale of programs in vector spaces.
Traditional logic programs are written in some formal languages easy to 'unify' each other or symbol based ones such as in Prolog, and their 'NOT' mechanisms are 'negation by failure of proof' ...
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This paper develops a new formalism CDLP by combining ASP and constrained default logic to facilitate modeling questions with incomplete information, such that both Reiter’s defaults and constraint defaults can be re...
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We introduce a formalism for bipolar argumentation frameworks that combines different proposals from the literature and results in a one-to-one correspondence with logic programming. We derive the correspondence by pr...
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In the process of creating a declarative program, the programmer transforms a problem specification expressed in a natural language into an executable specification. We study the case when the given specification is e...
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Recent attention to relational knowledge bases has sparked a demand for understanding how relations change between entities. Petri nets can represent knowledge structure and dynamically simulate interactions between e...
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The main contribution of the present paper is the introduction of a simple yet expressive hybrid-dynamic logic for describing quantum programs. This version of quantum logic can express quantum measurements and unitar...
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logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that ...
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The integration of Inductive logic programming (ILP) and Bottlenose Dolphin Optimization (BDO) in this research addresses a pressing issue in today's information-saturated landscape: the proliferation of fake news...
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ISBN:
(数字)9798350348798
ISBN:
(纸本)9798350348804
The integration of Inductive logic programming (ILP) and Bottlenose Dolphin Optimization (BDO) in this research addresses a pressing issue in today's information-saturated landscape: the proliferation of fake news. In an era where misleading information can spread rapidly, traditional methods often fall short in effectively identifying deceptive content. To combat this challenge, our approach harnesses the synergies of ILP and BDO. ILP plays a crucial role in constructing logical rules that capture intricate relationships within news data. By doing so, it delves deep into the content, seeking out patterns and inconsistencies that may not be obvious at first glance. BDO, on the other hand, takes inspiration from the social behavior of bottlenose dolphins to optimize the process of generating these rules. Just as dolphins collaborate and communicate to solve complex problems, BDO helps refine the logical rules for better accuracy. Ultimately, this research underscores the potential of bio-inspired optimization, such as BDO, combined with the precision of logic programming (ILP) to strengthen the integrity of information dissemination platforms. In an age where the veracity of information is paramount, this innovative approach offers a promising solution to combat the spread of fake news and promote the dissemination of authentic, reliable information.
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