scholarly journals Knowledge Representation in a Proof Checker for Logic Programs

10.5772/37201 ◽  
2012 ◽  
Author(s):  
Emmanouil Marakakis ◽  
Haridimos Kondylakis ◽  
Nikos Papadakis
Keyword(s):  
Knowledge Representation ◽  
Logic Programs ◽  
Proof Checker

scholarly journals Vicious Circle Principle and Logic Programs with Aggregates

2014 ◽  
Vol 14 (4-5) ◽  
pp. 587-601 ◽  
Author(s):  
MICHAEL GELFOND ◽  
YUANLIN ZHANG
Keyword(s):  
Knowledge Representation ◽  
Vicious Circle ◽  
Logic Programs ◽  
Vicious Circle Principle ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Answer Sets ◽  
Mathematical Semantics

AbstractThe paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches.

scholarly journals Safe Inductions: An Algebraic Study

2017 ◽  
Author(s):  
Bart Bogaerts ◽  
Joost Vennekens ◽  
Marc Denecker
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
Logic Programs ◽  
Intended Meaning ◽  
Safety Criterion ◽  
Autoepistemic Logic ◽  
Semantics Of Logic Programs ◽  
Do So

In many knowledge representation formalisms, a constructive semantics is defined based on sequential applications of rules or of a semantic operator. These constructions often share the property that rule applications must be delayed until it is safe to do so: until it is known that the condition that triggers the rule will remain to hold. This intuition occurs for instance in the well-founded semantics of logic programs and in autoepistemic logic. In this paper, we formally define the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. For the latter, we show that safe inductions manage to capture the intended meaning of a class of theories on which all classical constructive semantics fail.

Logic Programming for Intelligent Systems

2018 ◽  
pp. 4736-4745
Author(s):  
James D. Jones
Keyword(s):  
Knowledge Representation ◽  
Intelligent Systems ◽  
Technological Changes ◽  
Logic Programs ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Smart Systems ◽  
Financial Applications ◽  
Security Applications ◽  
Robust Framework

In what seem to be never-ending quests for automation, integration, seamlessness, new genres of applications, and “smart systems”, all of which are fueled in part by technological changes, intellectual maturity (or so one thinks), and out-of-the-box thinking that says “surely, there must be a better way”, one dreams of a future. This paper suggests that logic programs employing recent advances in semantics and in knowledge representation formalisms provide a more robust framework in which to develop very intelligent systems in any domain of knowledge or application. The author has performed work applying this paradigm and these reasoning formalisms in the areas of financial applications, security applications, and enterprise information systems.

Knowledge Representation to Empower Expert Systems

2011 ◽  
pp. 576-583
Author(s):  
James D. Jones
Keyword(s):  
Knowledge Representation ◽  
Expert Systems ◽  
Logic Programming ◽  
Programming Languages ◽  
Default Reasoning ◽  
Logic Programs ◽  
Specific Domain ◽  
Negation As Failure ◽  
Complex Forms ◽  
Representation Scheme

Knowledge representation is a field of artificial intelligence that has been actively pursued since the 1940s.1 The issues at stake are that given a specific domain, how do we represent knowledge in that domain, and how do we reason about that domain? This issue of knowledge representation is of paramount importance, since the knowledge representation scheme may foster or hinder reasoning. The representation scheme can enable reasoning to take place, or it may make the desired reasoning impossible. To some extent, the knowledge representation depends upon the underlying technology. For instance, in order to perform default reasoning with exceptions, one needs weak negation (aka negation as failure. In fact, most complex forms of reasoning will require weak negation. This is a facility that is an integral part of logic programs but is lacking from expert system shells. Many Prolog implementations provide negation as failure, however, they do not understand nor implement the proper semantics. The companion article to this article in this volume, “Logic Programming Languages for Expert Systems,” discusses logic programming and negation as failure.

scholarly journals Deriving conclusions from non-monotonic cause-effect relations

2016 ◽  
Vol 16 (5-6) ◽  
pp. 670-687 ◽  
Author(s):  
JORGE FANDINNO
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
Causal Effect ◽  
Stable Model ◽  
Logic Programs ◽  
Causal Relations ◽  
Initial Approach ◽  
Sufficient Cause ◽  
Different Types ◽  
Normal Logic

AbstractWe present an extension of Logic Programming (under stable models semantics) that, not only allows concluding whether a true atom is a cause of another atom, but alsoderiving new conclusionsfrom these causal-effect relations. This is expressive enough to capture informal rules like “if some agent's actionshave beennecessaryto cause an eventEthen conclude atomcaused(,E),” something that, to the best of our knowledge, had not been formalised in the literature. To this aim, we start from a first attempt that proposed extending the syntax of logic programs with so-calledcausal literals. These causal literals are expressions that can be used in rule bodies and allow inspecting the derivation of some atomAin the program with respect to some query function ψ. Depending on how these query functions are defined, we can model different types of causal relations such as sufficient, necessary or contributory causes, for instance. The initial approach was specifically focused on monotonic query functions. This was enough to cover sufficient cause-effect relations but, unfortunately, necessary and contributory are essentiallynon-monotonic. In this work, we define a semantics for non-monotonic causal literals showing that, not only extends the stable model semantics for normal logic programs, but also preserves many of its usual desirable properties for the extended syntax. Using this new semantics, we provide precise definitions ofnecessaryandcontributorycausal relations and briefly explain their behaviour on a pair of typical examples from the Knowledge Representation literature.

Two results for prioritized logic programming

2003 ◽  
Vol 3 (2) ◽  
pp. 223-242 ◽  
Author(s):  
YAN ZHANG
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
Logic Program ◽  
Default Reasoning ◽  
Knowledge Representation And Reasoning ◽  
Logic Programs ◽  
Splitting Technique ◽  
Close Relationship ◽  
Answer Set Semantics ◽  
Answer Set

Prioritized default reasoning has illustrated its rich expressiveness and flexibility in knowledge representation and reasoning. However, many important aspects of prioritized default reasoning have yet to be thoroughly explored. In this paper, we investigate two properties of prioritized logic programs in the context of answer set semantics. Specifically, we reveal a close relationship between mutual defeasibility and uniqueness of the answer set for a prioritized logic program. We then explore how the splitting technique for extended logic programs can be extended to prioritized logic programs. We prove splitting theorems that can be used to simplify the evaluation of a prioritized logic program under certain conditions.

Logic Programming for Intelligent Systems

2019 ◽  
pp. 879-890
Author(s):  
James D. Jones
Keyword(s):  
Information Systems ◽  
Knowledge Representation ◽  
Logic Programming ◽  
Intelligent Systems ◽  
Logic Programs ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Financial Applications ◽  
Security Applications ◽  
Robust Framework

This chapter suggests that logic programs employing recent advances in semantics and in knowledge representation formalisms provide a more robust framework in which to develop very intelligent systems in any domain of knowledge or application. The author has performed work applying this paradigm and these reasoning formalisms in the areas of financial applications, security applications, and enterprise information systems.

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