A Reasoning System for Satisfiability of Diagrammatic Specifications

2018 ◽  
pp. 371-402
Author(s):  
Yngve Lamo ◽  
Wendy MacCaull
Keyword(s):  
Reasoning System

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.

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.

Challenges and Opportunities in Collaborative Vulnerability Research Workflows

2020 ◽  
Vol 64 (1) ◽  
pp. 420-424
Author(s):  
Ryan Mullins ◽  
Deirdre Kelliher ◽  
Ben Nargi ◽  
Mike Keeney ◽  
Nathan Schurr
Keyword(s):  
Human Performance ◽  
Collaborative Work ◽  
Research Process ◽  
Workflow Model ◽  
Human Vulnerability ◽  
Research Teams ◽  
Reasoning System ◽  
Challenges And Opportunities ◽  
New Research ◽  
Future Work

Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.

A SysML-Based Modeling Language for Mechatronic System Architecture

2015 ◽  
Author(s):  
Ruirui Chen ◽  
Yusheng Liu ◽  
Yue Cao ◽  
Jing Xu
Keyword(s):  
Systems Engineering ◽  
System Architecture ◽  
Modeling Language ◽  
Mechatronic Systems ◽  
Geometric Information ◽  
Automatic Reasoning ◽  
Control Behavior ◽  
Reasoning System ◽  
And Control ◽  
Model Based Systems Engineering

Model Based Systems Engineering (MBSE) is the mainstream methodology for the design of complex mechatronic systems. It emphasizes the application of the system architecture, which highly depends on a formalized modeling language. However, such modeling language is less researched in previous studies. This paper proposes a general modeling language for representing the system architecture, aiming for representing function, physical effect, geometric information and control behavior which the system should satisfy. It facilitates the communication of designers from different technological domains and supports a series of applications such as automatic reasoning, system simulation, etc. The language is illustrated and verified with a practical mechatronic device finally.

A self-adaptive case-based reasoning system for dose planning in prostate cancer radiotherapy

2011 ◽  
Vol 38 (12) ◽  
pp. 6528-6538 ◽  
Author(s):  
Nishikant Mishra ◽  
Sanja Petrovic ◽  
Santhanam Sundar
Keyword(s):  
Prostate Cancer ◽  
Case Based Reasoning ◽  
Cancer Radiotherapy ◽  
Reasoning System ◽  
Dose Planning ◽  
Prostate Cancer Radiotherapy ◽  
Case Based ◽  
Self Adaptive

Progress and Prospects for the Development of Computer-Generated Actors for Military Simulation: Part 1-Introduction and Background

2003 ◽  
Vol 12 (3) ◽  
pp. 311-325 ◽  
Author(s):  
Martin R. Stytz ◽  
Sheila B. Banks
Keyword(s):  
Research Area ◽  
Background Information ◽  
Life Cycle Costs ◽  
Material Management ◽  
Reasoning System ◽  
Force Structure ◽  
The Cost ◽  
Future Work ◽  
Level Training ◽  
Made In

The development of computer-generated synthetic environments, also calleddistributed virtual environments, for military simulation relies heavily upon computer-generated actors (CGAs) to provide accurate behaviors at reasonable cost so that the synthetic environments are useful, affordable, complex, and realistic. Unfortunately, the pace of synthetic environment development and the level of desired CGA performance continue to rise at a much faster rate than CGA capability improvements. This insatiable demand for realism in CGAs for synthetic environments arises from the growing understanding of the significant role that modeling and simulation can play in a variety of venues. These uses include training, analysis, procurement decisions, mission rehearsal, doctrine development, force-level and task-level training, information assurance, cyberwarfare, force structure analysis, sustainability analysis, life cycle costs analysis, material management, infrastructure analysis, and many others. In these and other uses of military synthetic environments, computer-generated actors play a central role because they have the potential to increase the realism of the environment while also reducing the cost of operating the environment. The progress made in addressing the technical challenges that must be overcome to realize effective and realistic CGAs for military simulation environments and the technical areas that should be the focus of future work are the subject of this series of papers, which survey the technologies and progress made in the construction and use of CGAs. In this, the first installment in the series of three papers, we introduce the topic of computer-generated actors and issues related to their performance and fidelity and other background information for this research area as related to military simulation. We also discuss CGA reasoning system techniques and architectures.

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