A parallel cost-based abductive reasoning system on workstation cluster

2006 ◽  
Vol 37 (3) ◽  
pp. 80-89
Author(s):  
Shohei Kato ◽  
Tomonori Nakamura ◽  
Hidenori Itoh
Keyword(s):  
Abductive Reasoning ◽  
Workstation Cluster ◽  
Reasoning System

scholarly journals Abduction-Based Explanations for Machine Learning Models

2019 ◽  
Vol 33 ◽  
pp. 1511-1519 ◽  
Author(s):  
Alexey Ignatiev ◽  
Nina Narodytska ◽  
Joao Marques-Silva
Keyword(s):  
Machine Learning ◽  
Decision Problem ◽  
Abductive Reasoning ◽  
Decision Makers ◽  
Reasoning System ◽  
Constraint Reasoning ◽  
Human Decision ◽  
Applications Of Machine Learning ◽  
Machine Learning Models

The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions.

Dual systems for all: Higher-order, role-based relational reasoning as a uniquely derived feature of human cognition

2019 ◽  
Vol 42 ◽  
Author(s):  
Daniel J. Povinelli ◽  
Gabrielle C. Glorioso ◽  
Shannon L. Kuznar ◽  
Mateja Pavlic
Keyword(s):  
Temporal Reasoning ◽  
Higher Order ◽  
Important Case ◽  
Human Cognition ◽  
Relational Reasoning ◽  
Dual Systems ◽  
First Order ◽  
Reasoning System ◽  
Role Based

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

DECISION-MAKING ON INVESTMENT OF IT-INNOVATION BASED ON FUZZY EXPERT INFORMATION

2018 ◽  
pp. 103-111
Author(s):  
Olga Olegovna Eremenko ◽  
Lyubov Borisovna Aminul ◽  
Elena Vitalievna Chertina
Keyword(s):  
Decision Making ◽  
Individual Characteristics ◽  
Production Model ◽  
Investment Management ◽  
It Projects ◽  
Product Model ◽  
Project Cost ◽  
Reasoning System ◽  
Managerial Decisions ◽  
The Individual

The subject of the research is the process of making managerial decisions for innovative IT projects investing. The paper focuses on the new approach to decision making on investing innovative IT projects using expert survey in a fuzzy reasoning system. As input information, expert estimates of projects have been aggregated into six indicators having a linguistic description of the individual characteristics of the project type "high", "medium", and "low". The task of decision making investing has been formalized and the term-set of the output variable Des has been defined: to invest 50-75% of the project cost; to invest 20-50% of the project cost; to invest 10-20% of the project cost; to send the project for revision; to turn down investing project. The fuzzy product model of making investment management decisions has been developed; it adequately describes the process of investment management. The expediency of using constructed production model on a practical example is shown.

Experiences on using a workstation cluster to implement a real-time digital transient network analyzer

1996 ◽  
Vol 36 (3) ◽  
pp. 175-180
Author(s):  
L. Barretta ◽  
P. Cremonesi ◽  
E. Panzeri ◽  
N. Scarabottolo
Keyword(s):  
Real Time ◽  
Network Analyzer ◽  
Workstation Cluster ◽  
Transient Network

Online Third Places: Supporting Well-Being Through Identifying and Managing Unintended Consequences

2021 ◽  
pp. 109467052110188
Author(s):  
Joy Parkinson ◽  
Lisa Schuster ◽  
Rory Mulcahy
Keyword(s):  
Well Being ◽  
Unintended Consequences ◽  
Abductive Reasoning ◽  
Service Research ◽  
Data Sets ◽  
Management Service ◽  
Consumer Empowerment ◽  
Online Service ◽  
Third Places ◽  
Third Place

Unintended consequences of service are important yet infrequently examined in transformative service research. This research examines an online service community that transformed into an online third place, with consumers socializing and forming lasting relationships. Using practice-informed theory-building and an abductive reasoning approach, findings are presented from both manual and automated coding of three qualitative data sets that form the basis of a case study examining an online weight management service forum. Extending beyond current conceptualizations of the third place, this study is the first to propose a framework delineating online third place characteristics and their impact on consumers’ eudaimonic (the capacity for self-realization) and hedonic (attainment of pleasure and avoidance of pain) well-being. Findings show that in the absence of a physical or virtual servicescape, social factors including social density, equity, and personalization are key to constructing an online third place that supports well-being through building social connections and enjoyment. The new framework provides guidance for service managers to transform their online service communities into online third places to support consumer well-being and to identify and manage potential unintended consequences, for example, by ensuring segmentation of the community based on consumer groups’ shared interests and consumer empowerment through participation.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

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