Building a Belief–Desire–Intention Agent for Modeling Neural Networks

Huang, Chengbin; Chen, Long; Jiang, Haobin

doi:10.1080/08839514.2015.1071089

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Hofmann andOsterwalder, 2017). Skill and expertise (Modgil et al, 2021;Haddud et (Chen et al 2015;Pahukula et al, 2015;Pattinson and Thompson, 2014;Dawson et al, 2014;Anund et al, 2017;Brown et al, 2017;Childress et al, 2015;Chatfield and Reddick, 2019;Wirtz et al, 2019;Androutsopoulou et al, 2019;Mullet et (Sjøberg, 2019;Mahroof, 2019;Guo et al, 2016;Venkitasubramony and Adil, 2017;Derhami et al, 2017;Boysen et al, 2017) Inventory management issues (Xu et al, 2019;Sun and Medaglia, 2019).…”

Section: Technological Challengesmentioning

confidence: 99%

“…AI can also be observed as a set of technologies that are interrelated with the aim to solve complex problems (Zhang et al, 2014). It is believed that companies that apply extensive use of AI are receiving a great advantage in terms of decreased downtime, improved efficiency, and increased customer satisfaction (Chen et al, 2015). There are numerous examples and possibilities for applying robots in logistics such as intelligent robots for collecting, commissioning, and sorting of goods; robots for unloading containers based on optical character recognition) (OCR) technology (Klumpp 2017;Chung 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating Barriers and Challenges to Artificial Intelligence (AI) Implementation in Logistic Operations: A Systematic Review of Literature

Suwaidi¹,

Aydın²,

Rashid³

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The aim of this research is to investigate barriers and challenges associated with Artificial Intelligence (AI) adoption and implementation in the logistics operations. A systematic literature review was conducted to assess the major categories of challenges as well as specific challenges for each category published in peer-reviewed articles that are relevant to logistics operations. A total of 114 articles from six (6) electronic databases (i.e., IEEE Xplore, Science Direct, Elsevier's Scopus, Web of Science. Springer link, Google Scholar) were analyzed. Based on the findings, 7 major categories of barriers and challenges of AI adoption and implementation in the logistic are identified:(organisational and managerial challenges, technological challenges, economic challenges, data challenges, environmental challenges, social challenges, political, legal, and ethical challenges). Similarly, 16 specific challenges such as lack of top management support, internal requirement-organizational strategy, realtime response, disconnected systems, technology complexity, skill and expertise, high investment, operational costs, AI infrastructure, data standardization, scalability factors, data privacy and security, and insufficient operational warehouse space were identified. This research provides a further benchmark for decision making for logistics operation managers to deal with challenges and barriers in the adoption of AI in supply chain management.

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Section: Technological Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating Barriers and Challenges to Artificial Intelligence (AI) Implementation in Logistic Operations: A Systematic Review of Literature

Suwaidi¹,

Aydın²,

Rashid³

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

show abstract

“…For a final outlook towards the impact of especially future AI developments [82][83][84][85][86][87][88][89] in the supply chain and business domain a metaphor might be adapted: Bostrom started his 2014 book about superintelligence-hence AI-with a fable of sparrows looking out for an owl, symbolizing humans on the verge of developing AI instances (see [14], p. v). However, this unfinished fable lacks three important features: First, sparrows and owls are-though quite different-still both quite similar bird species.…”

Section: Discussionmentioning

confidence: 99%

Innovation Potentials and Pathways Merging AI, CPS, and IoT

Klumpp

2018

ASI

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Recent advances in the areas of Artificial Intelligence (AI) in the informatics field, Cyber-Physical Systems (CPS) in the production field, and Internet of Things (IoT) in the logistics and transportation field have induced a tremendous growth and innovation potential for global value chain setups. The question is not if further innovation and automation will happen but when-sooner than later-and how. Independent of physical production innovations (additive manufacturing) the information integration and decision autonomy tendencies themselves will drive new supply chain and customer interaction designs and business models. This article presents a technology forecast model based on extensive descriptions of developments by field as well as interaction traits. Results suggest that the crucial element in AI and technology application in logistics will be the human factor and human-artificial cooperation capacities and attitudes.

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“…Logistics companies take various measures to mitigate these aging effects through, for example, smart route planning and optimization (Verma and Verter 2010;Dondo and Cerd a 2015;Phan and Kim 2015;Gingerich et al 2016) and automation, but also by offering incentives to existing and new, young truck driver personnel. Another motivation to introduce AI applications is safety: not only the safety of other road users, but also working conditions of drivers themselves (Khorashadi et al 2005;Pattinson and Thompson 2014;Chen et al 2015aChen et al , 2015bPahukula et al 2015;Bedinger et al 2016). The potential of support by AI applications in road transport is huge, with distance control and warning systems as obvious examples.…”

Section: Transport Automationmentioning

confidence: 99%

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Klumpp

Zijm

2019

J of Business Logistics

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Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.

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Building a Belief–Desire–Intention Agent for Modeling Neural Networks

Cited by 6 publications

References 25 publications

Investigating Barriers and Challenges to Artificial Intelligence (AI) Implementation in Logistic Operations: A Systematic Review of Literature

Investigating Barriers and Challenges to Artificial Intelligence (AI) Implementation in Logistic Operations: A Systematic Review of Literature

Innovation Potentials and Pathways Merging AI, CPS, and IoT

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

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