A New Approach on ρ to Decision Making Using Belief Functions Under Incomplete Information

2005 ◽  
pp. 445-456
Author(s):  
Yuliang Fan ◽  
Peter Deer
Keyword(s):  
Decision Making ◽  
Incomplete Information ◽  
Belief Functions ◽  
New Approach

Closed Sets of Boolean Terms in Relational Databases

1991 ◽  
Vol 14 (3) ◽  
pp. 367-385
Author(s):  
Andrzej Jankowski ◽  
Zbigniew Michalewicz
Keyword(s):  
Computational Complexity ◽  
Incomplete Information ◽  
Relational Databases ◽  
The Other ◽  
New Approach ◽  
Closed Sets ◽  
Null Value ◽  
Algebraic Operations

A number of approaches have been taken to represent compound, structured values in relational databases. We review a few such approaches and discuss a new approach, in which every set is represented as a Boolean term. We show that this approach generalizes the other approaches, leading to more flexible representation. Boolean term representation seems to be appropriate in handling incomplete information: this approach generalizes some other approaches (e.g. null value mark, null variables, etc). We consider definitions of algebraic operations on such sets, like join, union, selection, etc. Moreover, we introduce a measure of computational complexity of these operations.

ACRIPPER: A New Associative Classification Based on RIPPER Algorithm

2021 ◽  
Vol 20 (01) ◽  
pp. 2150013
Author(s):  
Mohammed Abu-Arqoub ◽  
Wael Hadi ◽  
Abdelraouf Ishtaiwi
Keyword(s):  
Decision Making ◽  
Class Imbalance ◽  
The Other ◽  
Associative Classification ◽  
Rule Based ◽  
New Approach ◽  
New Novel ◽  
Accuracy Measure ◽  
High Level ◽  
Substantial Interest

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

How Persons with Intellectual Disabilities Are Fighting for Decision-Making Rights

2022 ◽  
Vol 121 (831) ◽  
pp. 30-35
Author(s):  
Chester A. Finn ◽  
Matthew S. Smith ◽  
Michael Ashley Stein
Keyword(s):  
Decision Making ◽  
Intellectual Disability ◽  
United Nations ◽  
Intellectual Disabilities ◽  
Legal Capacity ◽  
Disability Rights ◽  
Persons With Disabilities ◽  
New Approach ◽  
The United Nations

Paternalistic attitudes about what is in the interests of a person with an intellectual disability have long led to abuses, and are embedded in the guardianship laws still in place in most countries. Self-advocates, who identify as people with intellectual or other disabilities and are committed to demanding their rights and educating others about them, are calling for a new approach. They have found support for reforms in the Convention on the Rights of Persons with Disabilities, adopted by the United Nations in 2006 and since acceded to by 182 countries. By supporting the fundamental right of those with disabilities to make decisions, it has enabled disability rights advocates to successfully challenge legal capacity restrictions and push for “supported decision-making.”

Real-Time Robot Capability Analysis

2005 ◽  
Author(s):  
Chalongrath Pholsiri ◽  
Chetan Kapoor ◽  
Delbert Tesar
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Real Time ◽  
Computationally Efficient ◽  
New Approach ◽  
Ellipsoid Method ◽  
Capability Analysis ◽  
Computationally Intensive ◽  
Planar Robots

Robot Capability Analysis (RCA) is a process in which force/motion capabilities of a manipulator are evaluated. It is very useful in both the design and operational phases of robotics. Traditionally, ellipsoids and polytopes are used to both graphically and numerically represent these capabilities. Ellipsoids are computationally efficient but tend to underestimate while polytopes are accurate but computationally intensive. This article proposes a new approach to RCA called the Vector Expansion (VE) method. The VE method offers accurate estimates of robot capabilities in real time and therefore is very suitable in applications like task-based decision making or online path planning. In addition, this method can provide information about the joint that is limiting a robot capability at a given time, thus giving an insight as to how to improve the performance of the robot. This method is then used to estimate capabilities of 4-DOF planar robots and the results discussed and compared with the conventional ellipsoid method. The proposed method is also successfully applied to the 7-DOF Mitsubishi PA10-7C robot.

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