Knowledge-based decision intelligence in street lighting management

2021 ◽  
pp. 1-19
Author(s):  
Cristóvão Sousa ◽  
Daniel Teixeira ◽  
Davide Carneiro ◽  
Diogo Nunes ◽  
Paulo Novais
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Light Quality ◽  
Communication Technologies ◽  
Decision Problems ◽  
Computational Power ◽  
Street Lighting ◽  
Knowledge Based

As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

Knowledge-Based Instructional Gaming: GEO

1989 ◽  
Vol 17 (3) ◽  
pp. 189-203 ◽  
Author(s):  
Philip Duchastel
Keyword(s):  
Artificial Intelligence ◽  
Types Of Knowledge ◽  
Knowledge Based ◽  
Instructional Gaming

Games have a fascination for people which make them ideal vehicles for instruction of an informal nature. Described here is an instructional game (GEO) in which the user learns elements of Canadian geography as she chases a spy around the country. The game utilizes artificial intelligence approaches to represent and put to use various types of knowledge (knowledge of geography, of tutoring, and of the student). Our experience in designing and refining the game is discussed, as well as prospects for extending this approach to other learning situations.

scholarly journals Learning Social Media Content Optimization: How Can SMEs Draw the Users' Attention on Official WeChat Accounts?

2022 ◽  
Vol 12 ◽  
Author(s):  
Xueyun Zeng ◽  
Xuening Xu ◽  
Yenchun Jim Wu
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Psychological Needs ◽  
Media Content ◽  
Media Usage ◽  
Knowledge Based ◽  
Related Information ◽  
Artificial Intelligence Technology ◽  
Positive Effect ◽  
Content Optimization

Application of artificial intelligence is accelerating the digital transformation of enterprises, and digital content optimization is crucial to take the users' attention in social media usage. The purpose of this work is to demonstrate how social media content reaches and impresses more users. Using a sample of 345 articles released by Chinese small and medium-sized enterprises (SMEs) on their official WeChat accounts, we employ the self-determination theory to analyze the effects of content optimization strategies on social media visibility. It is found that articles with enterprise-related information optimized for content related to users' psychological needs (heart-based content optimization, mind-based content optimization, and knowledge-based content optimization) achieved higher visibility than that of sheer enterprise-related information, whereas the enterprise-related information embedded with material incentive (benefits-based content optimization) brings lower visibility. The results confirm the positive effect of psychological needs on the diffusion of enterprise-related information, and provide guidance for SMEs to apply artificial intelligence technology to social media practice.

scholarly journals Review of Artificial Intelligence

2020 ◽  
pp. 143-171
Author(s):  
K. P. V. Sai Aakarsh ◽  
Adwin Manhar
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Medical Diagnosis ◽  
Computer Application ◽  
Knowledge Based Systems ◽  
Human Race ◽  
Knowledge Based ◽  
Ordinary Person ◽  
Digital Computers

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

Challenges and Prospects of Information Communication Technology (ICT) in Teaching Technical Education towards Globalisation

2014 ◽  
pp. 237-255 ◽  
Author(s):  
Oladiran Stephen Olabiyi
Keyword(s):  
Information And Communication Technologies ◽  
21St Century ◽  
Technical Education ◽  
Communication Technologies ◽  
Vocational Education And Training ◽  
Information Communication ◽  
Knowledge Based ◽  
The World ◽  
Information And Communication ◽  
And Training

The relevance of Information and Communication Technologies (ICTs) in the field of Technical Vocational Education and Training (TVET) cannot be overemphasised in the knowledge-based and globalised society. The world of works is in as continuous a state of change as ICT itself, thus posing more challenges to the workers in the 21st century and the institutions responsible for their preparation. Therefore, this chapter discusses the challenges and prospects of ICT in teaching TVE towards globalisation. The chapter points out clearly the meaning, philosophy, and objectives of TVET, concept and types of ICT, the need for effective utilisation of ICTs and its role in TVET, the challenges and solutions to the effective utilisation of ICTs in TVET, and the prospect of using ICT in teaching TVET. The chapter concludes by suggesting solutions for proper planning, management, and effective utilisation of ICTs resources in TVET.

Applying Artificial Intelligence to Financial Investing

2018 ◽  
pp. 1-14
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

The Role of ICTs and the Management of Multinational Intellectual Capital

2011 ◽  
pp. 2457-2472 ◽  
Author(s):  
Mirghani S. Mohamed ◽  
Mona A. Mohamed
Keyword(s):  
Competitive Advantage ◽  
Information And Communication Technologies ◽  
Intellectual Capital ◽  
Communication Technologies ◽  
Supply Network ◽  
Global Knowledge ◽  
Knowledge Based ◽  
Information And Communication ◽  
Governance Strategy

This chapter provides a systematic multidisciplinary framework that defines the role of technology in leveraging IC across borders and between headquarters and subsidiaries. In reaching this conclusion, this chapter investigates the strategic importance of Information and Communication Technologies (ICTs) in the management of Intellectual Capital (IC) within a Multinational Company (MNC) ecosystem. The chapter addresses the transubstantiation of MNC into boundaryless Global Knowledge-Based Organization (GKB-MNC) which ultimately propagates into Learning MNC (LMNC). The latter is a suggested MNC category that sustains competitive advantage through systemic adoption of “Knowledge Iterative Supply Network (KISN)” model proposed by the authors. The chapter suggests a new multinational ICT/IC governance strategy that handles the emerging complexities associated with modern intangible resource synthesis.

Engineering of Experience Based Trust for E-Commerce

2010 ◽  
pp. 342-367 ◽  
Author(s):  
Zhaohao Sun ◽  
Jun Han ◽  
Dong Dong ◽  
Shuliang Zhao
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Research And Development ◽  
Multiagent Systems ◽  
System Architecture ◽  
Intelligent Systems ◽  
Multiagent System ◽  
Theoretical Foundation ◽  
Knowledge Based ◽  
Trust Inference

Trust is significant for sustainable development of e-commerce and has received increasing attention in e-commerce, multiagent systems (MAS), and artificial intelligence (AI). However, little attention has been given to the theoretical foundation and intelligent techniques for trust in e-commerce from a viewpoint of intelligent systems and engineering. This chapter will fill this gap by examining engineering of experience-based trust in e-commerce from the viewpoint of intelligent systems. It looks at knowledgebased trust, inference-based trust and their interrelationships with experience-based trust. It also examines scalable trust in e-commerce. It proposes a knowledge based model of trust in e-commerce and a system architecture for METSE: a multiagent system for experience-based trust in e-commerce. The proposed approach in this chapter will facilitate research and development of trust, multiagent systems, e-commerce and e-services.

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