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F-logic
F-logic (Frame logic) is a knowledge representation and ontology language. It combines the advantages of conceptual modeling with Object-oriented programming, object-oriented, Frame (artificial intelligence), frame-based languages, and offers a Declarative programming, declarative, compact and simple Syntax (programming languages), syntax, and the well-defined semantics of a logic programming language. Features include, among others, object identity, complex objects, Inheritance (object-oriented programming), inheritance, Polymorphism (computer science), polymorphism, query methods, Encapsulation (computer programming), encapsulation. F-logic stands in the same relationship to object-oriented programming as classical relational calculus stands to relational database programming. Overview F-logic was developed by Michael Kifer at Stony Brook University and Georg Lausen at the University of Mannheim. F-logic was originally developed for deductive databases, but is now used most often ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Logic Programming
Logic programming is a programming, database and knowledge representation paradigm based on formal logic. A logic program is a set of sentences in logical form, representing knowledge about some problem domain. Computation is performed by applying logical reasoning to that knowledge, to solve problems in the domain. Major logic programming language families include Prolog, Answer Set Programming (ASP) and Datalog. In all of these languages, rules are written in the form of ''clauses'': :A :- B1, ..., Bn. and are read as declarative sentences in logical form: :A if B1 and ... and Bn. A is called the ''head'' of the rule, B1, ..., Bn is called the ''body'', and the Bi are called '' literals'' or conditions. When n = 0, the rule is called a ''fact'' and is written in the simplified form: :A. Queries (or goals) have the same syntax as the bodies of rules and are commonly written in the form: :?- B1, ..., Bn. In the simplest case of Horn clauses (or "definite" clauses), all ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Flora-2
Flora-2 is an open source semantic rule-based system for knowledge representation and reasoning. The language of the system is derived from F-logic, HiLog, W. Chen, M. Kifer and D.S. Warren (1993)''HiLog: A Foundation for Higher-Order Logic Programming'' Journal of Logic Programming, 1993. and Transaction logic.A.J. Bonner and M. Kifer (1993), ''Transaction Logic Programming'', International Conference on Logic Programming (ICLP), 1993. Being based on F-logic and HiLog implies that object-oriented syntax and higher-order representation are the major features of the system. Flora-2 also supports a form of defeasible reasoning called ''Logic Programming with Defaults and Argumentation Theories'' (LPDA). Applications include intelligent agents, Semantic Web, knowledge-bases networking, ontology management, integration of information, security policy analysis, automated database normalization, and more. Flora-2 relies on the XSB system for its inference engine. The design and archit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rule Interchange Format
The Rule Interchange Format (RIF) is a W3C Recommendation. RIF is part of the infrastructure for the semantic web, along with (principally) SPARQL, RDF and OWL. Although originally envisioned by many as a "rules layer" for the semantic web, in reality the design of RIF is based on the observation that there are many "rules languages" in existence, and what is needed is to exchange rules between them. RIF includes three dialects, a Core dialect which is extended into a Basic Logic Dialect (BLD) and Production Rule Dialect (PRD). History The RIF working group was chartered in late 2005. Among its goals was drawing in members of the commercial rules marketplace. The working group started with more than 50 members and two chairs drawn from industry, Christian de Sainte Marie of ILOG, and Chris Welty of IBM. The charter, to develop an interchange format between existing rule systems was influenced by a workshop in the spring of 2005 in which it was clear that one rule language ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of ''reasoning''. Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom system ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Description Logic
Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors. DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as ''terminological knowledge''). It is of particular importance in providing a logical formalism for ontologies and the Semantic Web: the Web Ontology Language (OWL) and its profiles are based on DLs. The most notable application of DLs and OWL is in biomedical in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Well-founded Semantics
In computer science, the well-founded semantics is a three-valued semantics for logic programming, which gives a precise meaning to general logic programs. History The well-founded semantics was defined by Van Gelder, et al. in 1988. The Prolog system XSB implements the well-founded semantics since 1997. Three-valued logic The well-founded semantics assigns a unique model to every general logic program. However, instead of only assigning propositions ''true'' or ''false'', it adds a third value ''unknown'' for representing ignorance. A simple example is the logic program that encodes two propositions a and b, and in which a must be true whenever b is not and vice versa: a :- not(b). b :- not(a). neither a nor b are true or false, but both have the truth value unknown. In the two-valued stable model semantics, there are two stable models, one in which a is true and b is false, and one in which b is true and a is false. Stratified logic programs have a 2-valued well-found ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Negation As Failure
Negation as failure (NAF, for short) is a non-monotonic inference rule in logic programming, used to derive \mathrm~p (i.e. that p is assumed not to hold) from failure to derive p. Note that \mathrm ~p can be different from the statement \neg p of the logical negation of p, depending on the completeness of the inference algorithm and thus also on the formal logic system. Negation as failure has been an important feature of logic programming since the earliest days of both Planner and Prolog. In Prolog, it is usually implemented using Prolog's extralogical constructs. More generally, this kind of negation is known as weak negation, in contrast with the strong (i.e. explicit, provable) negation. Planner semantics In Planner, negation as failure could be implemented as follows: :''if'' (''not'' (''goal'' p)), ''then'' (''assert'' ¬p) which says that if an exhaustive search to prove p fails, then assert ¬p. This states that proposition p shall be assumed as "not true" in any s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SIGMOD
SIGMOD is the Association for Computing Machinery's Special Interest Group on Management of Data, which specializes in large-scale data management problems and databases. The annual ACM SIGMOD Conference, which began in 1975, is considered one of the most important in the field. While traditionally this conference had always been held within North America, it took place in Paris in 2004, Beijing in 2007, Athens in 2011, and Melbourne in 2015. The acceptance rate of the ACM SIGMOD Conference, averaged from 1996 to 2012, was 18%, and it was 17% in 2012. In association with SIGACT and SIGAI, SIGMOD also sponsors the annual ACM Symposium on Principles of Database Systems (PODS) conference on the theoretical aspects of database systems. PODS began in 1982, and has been held jointly with the SIGMOD conference since 1991. Each year, the group gives out several awards to contributions to the field of data management. The most important of these is the SIGMOD Edgar F. Codd Innovations ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Undecidable Problem
In computability theory and computational complexity theory, an undecidable problem is a decision problem for which it is proved to be impossible to construct an algorithm that always leads to a correct yes-or-no answer. The halting problem is an example: it can be proven that there is no algorithm that correctly determines whether an arbitrary program eventually halts when run. Background A decision problem is a question which, for every input in some infinite set of inputs, requires a "yes" or "no" answer. Those inputs can be numbers (for example, the decision problem "is the input a prime number?") or values of some other kind, such as strings of a formal language. The formal representation of a decision problem is a subset of the natural numbers. For decision problems on natural numbers, the set consists of those numbers that the decision problem answers "yes" to. For example, the decision problem "is the input even?" is formalized as the set of even numbers. A decision pr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Open World Assumption
The closed-world assumption (CWA), in a formal system of logic used for knowledge representation, is the presumption that a statement that is true is also known to be true. Therefore, conversely, what is not currently known to be true, is false. The same name also refers to a logical formalization of this assumption by Raymond Reiter. The opposite of the closed-world assumption is the open-world assumption (OWA), stating that lack of knowledge does not imply falsity. Decisions on CWA vs. OWA determine the understanding of the actual semantics of a conceptual expression with the same notations of concepts. A successful formalization of natural language semantics usually cannot avoid an explicit revelation of whether the implicit logical backgrounds are based on CWA or OWA. Negation as failure is related to the closed-world assumption, as it amounts to believing false every predicate that cannot be proved to be true. Example In the context of knowledge management, the closed- ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Non-monotonic Reasoning
A non-monotonic logic is a formal logic whose entailment relation is not monotonic. In other words, non-monotonic logics are devised to capture and represent defeasible inferences, i.e., a kind of inference in which reasoners draw tentative conclusions, enabling reasoners to retract their conclusion(s) based on further evidence. Most studied formal logics have a monotonic entailment relation, meaning that adding a formula to the hypotheses never produces a pruning of its set of conclusions. Intuitively, monotonicity indicates that learning a new piece of knowledge cannot reduce the set of what is known. Monotonic logics cannot handle various reasoning tasks such as reasoning by default (conclusions may be derived only because of lack of evidence of the contrary), abductive reasoning (conclusions are only deduced as most likely explanations), some important approaches to reasoning about knowledge (the ignorance of a conclusion must be retracted when the conclusion becomes known), ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |