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Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.

Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.

AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.

New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.

This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.

This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

To point out the relevance of Heinz von Foerster's work to modern embodied cognitive science and artifical intelligence research.

The paper discusses (a) von Foerster's contributions to understanding the limitations of the computer metaphor which has long dominated cognitive science, and (b) his theories concerning how reality is constructed in organizationally closed organisms, and what the underlying neural mechanisms are. The latter is exemplified with a simple neuro‐robotic model that illustrates the constructive and anticipatory nature of memory.

von Foerster's work on the integration of a radical constructivest philosophy of knowledge construction with models of the underlying neurophysiological and sensorimotor mechanisms is still highly relevant to the understanding of embodied cognition and robotic models thereof.

This paper identifies conceptual contributions that von Foerster's constructivist cybernetics can make to cognitive science's still limited understanding of the embodiment of cognition and “representation”.

It is often argued that anything observable may be simulated on a computer. Using this as a basis, workers in artificial intelligence (AI) often go forward to maintain that machines can be made intelligent by machine simulation of human intelligence processes. There are two difficulties with this concept. The first difficulty lies in the knowledge of human intelligence processes that we have presently obtained and may possibly obtain in the near future. A more basic question is of the sufficiency of the concept itself. Simulation in itself is not sufficient to produce intelligent action where perhaps modelling might be. There are fundamental difficulties in the problem of establishing an adequate mapping function. It is held that there is insufficient correspondence between human and machine intelligence processes to allow human intelligence modelling on existing digital computers.

As the most disruptive technologies, Artificial Intelligence (AI) has been considered as a reliable tool in processing data to enhance business performance. Along with the increasing amount of data generated through online activities by customers, various hospitality companies have been investing in AI-powered solutions to be able to have better understanding about their customers and provide the relevant service to them accordingly. Despite knowing the impact on the customer service orientation, little is known about the impact of AI on the business process of a hospitality company. This paper explores the impact of the adoption of the AI on the business process of a hospitality company to have a better understanding of the extent of particular part of the business process that would benefit from the adoption of AI. It is apparent that Revenue Management and Marketing are the parts of business process within the hospitality industry that would have more positive impact on the adoption of AI. While AI-based marketing would be able to identify and target effectively high-value consumers, revenue management would be able to determine the right pricing strategy in real time due to the vast amount of available data and subsequently would have positive impact on the financial performance of the hospitality company.