Caspar: Towards decision making helpers agents for IoT, based on natural language and first order logic reasoning

2021 ◽  
Vol 104 ◽  
pp. 104269
Author(s):  
Carmelo Fabio Longo ◽  
Francesco Longo ◽  
Corrado Santoro
Keyword(s):  
Decision Making ◽  
Natural Language ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Logic Reasoning

First-Order Logic Reasoning Support for the Semantic Web

2009 ◽  
Vol 19 (12) ◽  
pp. 3091-3099 ◽  
Author(s):  
Gui-Hong XU ◽  
Jian ZHANG
Keyword(s):  
Semantic Web ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Logic Reasoning

Help Generation in a System for Learning Natural Language to First Order Logic Conversion

2011 ◽  
Author(s):  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
First Order Logic ◽  
First Order

Translating Advanced Integrity Checking Technology to SQL

2002 ◽  
pp. 203-249 ◽  
Author(s):  
Hendrik Decker
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
Predicate Calculus ◽  
Integrity Constraints ◽  
First Order Logic ◽  
First Order ◽  
Integrity Checking ◽  
Natural Language Interface ◽  
User Friendly ◽  
Declarative Specifications

The main goal of this chapter is to arrive at a coherent technology for deriving efficient SQL triggers from declarative specifications of arbitrary integrity constraints. The user may specify integrity constraints declaratively as closed queries in predicate calculus syntax (i.e., sentences in the language of first-order logic, abbr. FOL), as datalog denials, as query conditions in SQL WHERE clauses, or in some other, possibly more user-friendly manner (e.g., via a dialog-driven graphical or natural language interface which internally translates to equivalent WHERE clause conditions). As we are going to see, the triggers derived from such specifications behave such that whenever some update event would violate any of the integrity constraints, one or several of the triggers derived from that constraint are activated in order to enforce the constraint. That is, the violation is either prevented by rolling back the update or repaired instantly by subsequent further updates.

Automatic estimation of exercises'difficulty levels in a tutoring system for teaching the conversion of natural language into first-order logic

2016 ◽  
Vol 33 (6) ◽  
pp. 569-580 ◽  
Author(s):  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Konstantinos Kovas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Natural Language ◽  
Order Logic ◽  
First Order Logic ◽  
Tutoring System ◽  
First Order ◽  
Automatic Estimation

Individual Reasoning within a Reasoning Community

2012 ◽  
pp. 61-86
Keyword(s):  
Decision Making ◽  
Order Logic ◽  
First Order Logic ◽  
Default Reasoning ◽  
Argumentation Schemes ◽  
Technological Systems ◽  
First Order ◽  
Deontic Reasoning ◽  
New Knowledge

In this chapter, the nature of the process that each participant engages in individually in order to contribute to collective reasoning is discussed. The design of technological systems that will best support reasoning in its communal context requires the specification of schemes for representing knowledge and for the inference of new knowledge. Further, it is also necessary to articulate a model for the process that individuals engage in when reasoning in groups. The assertion we make is that the process iteratively includes phases of engagement, individual reasoning, group coalescing, until decision making. Representations, including the classical syllogism, first order logic, default reasoning, deontic reasoning, and argumentation schemes, are surveyed to illustrate their strengths and limitations to represent individual reasoning.

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