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In investing energy in developing reasoning machines of the future, one must abstract away from the specific solutions to specific problems and ask what are the fundamental research questions that should be addressed. This paper aims to revisit some fundamental perspectives and promote new approaches to reasoning machines and their associated form and function.

Core aspects are discussed, namely the one‐mind‐many‐bodies metaphor as introduced in the agent Chameleon work. Within this metaphor the agent's embodiment form may take many guises with the artificial mind or agent potentially exhibiting a nomadic existence opportunistically migrating between a myriad of instantiated embodiments. The paper animates these concepts with reference to two case studies.

The two case studies illustrate how a machine can have fundamentally different capabilities than a human which allows us to exploit, rather than be constrained, by these important differences.

Aids in understanding some of the fundamental research questions of reasoning machines that should be addressed.

Applies a computer model GAIA (Groups of Adaptive Inferencing Agents) to simulate the lifecycle of artificial groups directed by agendas which specify varying strategies for collective problem solving. Within GAIA, groups of artificial agents dynamically learn and interact by proposing, combining and testing inductive hypotheses in the form of genetic building blocks. Agents share and combine building block solutions to evolve decision trees to respond to environmental inputs. Effects of agendas which emphasize stages of conservative and liberal problem solving strategies over a group’s lifecycle were simulated. Conservative strategies emphasize consensus and collective memories within groups. Liberal strategies emphasize challenges to collective memory and individual agent predictions. Agendas which vary from conservative to liberal resulted in the poor group solutions. Significantly better group solutions were produced by an agenda varying from liberal to conservative and back to liberal (L‐C‐L). The L‐C‐L agenda focuses on critical evaluation and rewards for individual contribution in the beginning and ending lifecycle stages and provides a middle stage of collective exploration.

Interactions are fundamental for successful relationships and stable cooperation in a business-to-business market. The main assumption in research on interactions, so obvious that usually not stated by researchers, is that they are set between humans. The development of artificial intelligence forces the re-examination of this assumption. This paper aims to conceptualize business virtual assistants (BVAs), a type of intelligent agent, as either a boundary object or an actor within business interactions.

Reference is made to the literature on business interactions, boundary objects and identity attribution to problematize the process of interpretation through which BVA obtains an identity. The ARA model and the model of interaction process is used to create a theoretical framework.

This paper contributes to the literature on business interactions, and to the core of the IMP discussion, in three aspects. The first provides a framework to understand the phenomenon of an artificial entity as an interlocutor in business interactions. While doing that a new type of entity, BVA, is introduced. The second contribution is the exploration and augmentation of the concept of a business actor. The third calls attention to BVA as a boundary object. These issues are seen as essential to move forward the discussion about the meaning of business interaction in the near future.

This paper conceptualizes the presence of a new entity – BVA – in the business landscape.

In their authoritative literature review, Breen and Jonsson (2005) claim that ‘one of the most significant trends in the study of inequalities in educational attainment in the past decade has been the resurgence of rational-choice models focusing on educational decision making’. The starting point of the present contribution is that these models have largely ignored the explanatory relevance of social interactions. To remedy this shortcoming, this paper introduces a micro-founded formal model of the macro-level structure of educational inequality, which frames educational choices as the result of both subjective ability/benefit evaluations and peer-group pressures. As acknowledged by Durlauf (2002, 2006) and Akerlof (1997), however, while the social psychology and ethnographic literature provides abundant empirical evidence of the explanatory relevance of social interactions, statistical evidence on their causal effect is still flawed by identification and selection bias problems. To assess the relative explanatory contribution of the micro-level and network-based mechanisms hypothesised, the paper opts for agent-based computational simulations. In particular, the technique is used to deduce the macro-level consequences of each mechanism (sequentially introduced) and to test these consequences against French aggregate individual-level survey data. The paper's main result is that ability and subjective perceptions of education benefits, no matter how intensely differentiated across agent groups, are not sufficient on their own to generate the actual stratification of educational choices across educational backgrounds existing in France at the beginning of the twenty-first century. By computational counterfactual manipulations, the paper proves that network-based interdependencies among educational choices are instead necessary, and that they contribute, over and above the differentiation of ability and of benefit perceptions, to the genesis of educational stratification by amplifying the segregation of the educational choices that agents make on the basis of purely private ability/benefit calculations.

Agent‐based modelling is a bottom‐up approach to understanding systems which provides a powerful tool for analysing complex, non‐linear markets. The method involves creating artificial agents designed to mimic the attributes and behaviours of their real‐world counterparts. The system’s macro‐observable properties emerge as a consequence of these attributes and behaviours and the interactions between them. The simulation output may be potentially used for explanatory, exploratory and predictive purposes. The aim of this paper is to introduce the reader to some of the basic concepts and methods behind agent‐based modelling and to present some recent business applications of these tools, including work in the telecoms and media markets.

Service providers and consumers alike are increasingly adopting artificial intelligence service agents (AISA) for service. Yet, no service quality scale exists that can fully capture the key factors influencing AISA service quality. This study aims to address this shortcoming by developing a scale for measuring AISA service quality (AISAQUAL).

Based on extant service quality research and established scale development techniques, the study constructs, refines and validates a multidimensional AISAQUAL scale through a series of pilot and validation studies.

AISAQUAL contains 26 items across six dimensions: efficiency, security, availability, enjoyment, contact and anthropomorphism. The new scale demonstrates good psychometric properties and can be used to evaluate service quality across AISA, providing a means of examining the relationships between AISA service quality and satisfaction, perceived value as well as loyalty.

Future research should validate AISAQUAL with other AISA types, as they diffuse throughout the service sector. Moderating factors related to services, the customer and the AISA can be investigated to uncover the boundary conditions under which AISAQUAL is likely to influence service outcomes. Longitudinal studies can be carried out to assess how ongoing use of AISA can change service outcomes.

Service managers can use AISAQUAL to effectively monitor, diagnose and improve services provided by AISA while enhancing their understanding of how AISA can deliver better service quality and customer loyalty outcomes.

Anthropomorphism is identified as a new service quality dimension. AISAQUAL facilitates theory development by providing a reliable scale to improve the current understanding of consumers’ perspectives concerning AISA services.

Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.

The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.

The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.

In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.

The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.

The study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services.

Multi-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact.

The study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population.

Customers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims.

Various aspects of the Framsticks system are described. The system is a universal tool for modeling, simulating and optimizing virtual agents, with three‐dimensional body and embedded control system. Simulation model is described first. Then features of the system framework are presented, with typical and potential applications. Specific tools supporting human understanding of evolutionary processes, control and behaviors are also outlined. Finally, the most interesting research experiments are summarized.