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About 10,000 papers for “inference rules”

Inference Rules.

David I. Spivak (206142), Robert E. Kent (333987) · Figshare · 2015

Cited by 0Open access

Inductive Inference Rules

Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning and Data Mining · 2017

Cited by 0

Zsyntax inference rules.

Adnan Rashid (4220749), Osman Hasan (4220746), Umair Siddique (327800) et al. · Figshare · 2017

Cited by 0Open access

Whither inference rules?

Hoogerwoord, RR Rob · TU/e Research Portal (Eindhoven University of Technology) · 1992

Cited by 0

Derived Inference Rules

Nikhil Shankar · Cambridge University Press eBooks · 1994

But formalized mathematics cannot in practice be written down in full.… We shall therefore very quickly abandon formalized mathematicsN. Bourbaki [Bou68]

Cited by 0

Fuzzy inference rules.

Sundus Ayyaz (5832386), Usman Qamar (5832389), Raheel Nawaz (5449475) · Figshare · 2018

Cited by 0Open access

Inference rules Owicki/Gries

Stephen Brookes · 2006

(subject to static constraints) Shared-memory parallel programs Resource-sensitive partial correctness resource names, protection lists, invariants

Cited by 0

Real Rules of Inference

Ronald P. Loui · 1986

Abstract : Israel's remarks about non-monotonic logic are used as a springboard for studying some thoughts on defeasible reasoning among contemporary philosophers of science: thoughts that A.I. workers have so far neglected. Israel's criticisms, that A.I. has misconceived rules of belief fixation and revision, and his constructive comment, that we should look to the philosophers of science for understanding and inspiration, are the two central themes.

Cited by 4

The Rules of Inference

Gary King, Martin A. Tanner · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · 2002

(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.

Cited by 2Open access

The Rules of Inference

Lee Epstein, Gary King · The University of Chicago Law Review · 2002

Although the term "empirical research" has become commonplace in legal scholarship over the past two decades, law professors have in fact been conducting research that is empirical--that is, learning about the world using quantitative data or qualitative information--for almost as long as they have been conducting research. For just as long, however, they appear to have been proceeding with little awareness of, much less compliance with, many of the rules of inference, and without paying heed to the key lessons of the revolution in empirical analysis that has been taking place over the last century in other disciplines. The tradition of including some articles devoted exclusively to the methodology of empirical analysis--so well represented in journals in traditional academic fields--is virtually nonexistent in the nation's law reviews. As a result, readers learn considerably less accurate information about the empirical world than the studies' stridently stated, but overly confident, conclusions suggest. To remedy this situation both for the producers and consumers of empirical work, this Article adapts the rules of inference used in the natural and social sciences to the special needs, theories, and data in legal scholarship, and explicates them with extensive illustrations from existing research. The Article also offers suggestions for how the infrastructure of teaching and research at law schools might be reorganized so that it can better support the creation of first-rate empirical research without compromising other important objectives.

Cited by 5Open access

The inference rules for .

Zihao Fu (3742651), Zhixian Liu (2293759), Weihua Tan (15452234) et al. · Figshare · 2025

<div> In recent years, the critical role of path planning and path tracking in autonomous vehicles (AV) has led to a growing focus on these areas. Generally speaking, the path planning layer plans the collision-free path, and then the path tracking layer ensures the stability of AV. However, in emergency scenario, the path planned by conventional method is difficult to guarantee the stability of AV. In this study, a potential field reconstruction-based path planning system (PFR-BPPS) is proposed to address the aforementioned issue. The PFR-BPPS consists of two main components: a potential field reconstruction module and an adaptive fusion module. To ensure simultaneous obstacle avoidance and stability for AV, the potential field reconstruction module reconstructs both the potential velocity field and the potential stability field. For adaptive integration of these two fields, the adaptive fusion module employs fuzzy inference rules for effective fusion. The simulation is carried out on the Matlab-Carsim co-simulation platform. The results demonstrate that the path planned by PFR-BPPS exhibits outstanding performance in improving velocity adaptability and maintaining path stability in emergency situations. </div>

Cited by 0Open access

Globally Admissible Inference Rules

V. V. Rimatskiy · The Bulletin of Irkutsk State University Series Mathematics · 2022

Setting the basic rules of inference is fundamental to logic. The most general variant of possible inference rules are admissible inference rules: in logic 𝐿, a rule of inference is admissible if the set of theorems 𝐿 is closed with respect to this rule. The study of admissible inference rules was stimulated by Friedman’s problem: Is there an algorithm for recognizing the admissibility of an inference rule in intuitionistic logic? For a wide class of non-classical logics the problem of recognizing with respect to the admissibility of inference rules was solved in 1980s. Another way of describing all admissible rules in logic goes back to the problem of A. Kuznetsov (1975): specifying a certain (finite) set of admissible rules, from which all other admissible rules in logic will be derived as consequences, i.e. setting a (finite) basis. It turned out that most basic non-classical logics do not have a finite basis for admissible inference rules. In the early 2000s, for most basic non-classical logics and some tabular logics, the Fridman-Kuznetsov problem was solved by describing an explicit basis for admissible rules. The next stage in the study of admissible inference rules for non-classical logics can be considered the concept of a globally admissible inference rule. Globally admissible rules in the logic 𝐿 are those inference rules that are admissible simultaneously in all (with finite model property) extensions of the given logic. Such rules develop and generalize the concept of an admissible inference rule. The present work is devoted to the study of globally admissible rules of logic 𝑆4. Conditions for global admissibility in the logic 𝑆4 were obtained, a characteristic (universal) model was constructed (checking global admissibility is reduced to checking the truth of a rule on its submodels), a basis was described (all globally admissible rules are derived from it) and an anti-basis (from which all rules not available globally in 𝑆4).

Cited by 0Open access