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In this digital era, marketing theory and practice are being transformed by increasing complexity due to information availability, higher reach and interactions, and faster speeds of transactions. These have led to the adoption of intelligent agent technologies (IATs) by many companies. As IATs are relatively new and technologically complex, several definitions are evolving, and the theory in this area is not yet fully developed. There is a need to provide structure and guidance to marketers to further this emerging stream of research. As a first step, this paper proposes a marketing-centric definition and a systematic taxonomy and framework. The authors, using a grounded theory approach, conduct an extensive literature review and a qualitative study in which interviews with managers from 50 companies in 22 industries reveal the importance of understanding IAT applications and adopting them. Further, the authors propose an integrated conceptual framework with several propositions regarding IAT adoption. This research identifies the gaps in the literature and the need for adoption of IATs in the future of marketing given changing consumer behavior and product and industry characteristics.

A personal intelligent agent (PIA) is a system that acts intelligently to assist a human using natural language. Examples include Siri and Alexa. These agents are powerful computer programs that operate autonomously and proactively, learn and adapt to change, react to the environment, complete tasks within a favorable timeframe and communicate with the user using natural language to process commands and compose replies. PIAs are different from other systems previously explored in Information Systems (IS) due to their personalized, intelligent, and human-like behavior. Drawing on research in IS and Artificial Intelligence, we build and test a model of user adoption of PIAs leveraging their uique characteristics. Analysis of data collected from an interactive lab-based study for new PIA users confirms that both perceived intelligence and anthropomorphism are significant antecedents of PIA adoption. Our findings contribute to the understanding of a quickly-changing and fast-growing set of technologies that extend users’ capabilities and their sense of self​.

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

A Digital Twin (DT) is a frequently updated virtual representation of a physical or a digital instance that captures its properties of interest. Incorporating both cyber and physical parts to build a digital twin is challenging due to the high complexity of the requirements that should be addressed and satisfied during the design, implementation and operation. In this context, we introduce the MADTwin (Multi-Agent Digital Twin) framework driven by a Multi-agent Systems (MAS) paradigm and supported by flexible architecture and extendible upper ontology for modelling agent-based digital twins. A comprehensive case study of a smart warehouse supported by multi-robots has been presented to show the feasibility and applicability of this framework. The introduced framework powered by intelligent agents integrated with enabler technologies enabled us to cope with parts of the challenges imposed by modelling and integrating Cyber-Physical Systems (CPS) with digital twins for multi-robots of the smart warehouse. In this framework, different components of CPS (robots) are represented as autonomous physical agents with their digital twin agents in the digital twin environment. Agents act autonomously and cooperatively to achieve their local goals and the objectives of the whole system. Eventually, we discuss the framework’s strengths and identify areas of improvement and plans for future work.

Goal reasoning has a bright future as a foundation for the research and development of intelligent agents. Goal reasoning is the study of agents that can deliberate on and self‐select their objectives, which is a desirable capability for some applications of deliberative autonomy. This capability is of interest to several AI subcommunities and applications. Our group has focused on how goal reasoning can assist with controlling autonomous systems. The importance of how agents reason about goals is growing and it merits increased attention, particularly from the perspective of research on AI safety. In this article, I introduce goal reasoning, briefly relate it to other AI topics, summarize some of our group's work on goal reasoning foundations and emerging applications, and describe some current and future research directions.

This text provides a detailed, practical guide to building multi-agent systems using Jason, one of the most prominent agent programming languages.

What makes teamwork tick? Cooperation matters, in daily life and in complex applications. After all, many tasks need more than a single agent to be effectively performed. Therefore, teamwork rules! Teams are social groups of agents dedicated to the fulfilment of particular persistent tasks. In modern multiagent environments, heterogeneous teams often consist of autonomous software agents, various types of robots and human beings. Teamwork in Multi-agent Systems: A Formal Approach explains teamwork rules in terms of agents' attitudes and their complex interplay. It provides the first comprehensive logical theory, TeamLog, underpinning teamwork in dynamic environments. The authors justify design choices by showing TeamLog in action. The book guides the reader through a fascinating discussion of issues essential for teamwork to be successful: What is teamwork, and how can a logical view of it help in designing teams of agents? What is the role of agents' awareness in an uncertain, dynamic environment? How does collective intention constitute a team? How are plan-based collective commitments related to team action? How can one tune collective commitment to the team's organizational structure and its communication abilities?\\ What are the methodological underpinnings for teamwork in a dynamic environment? How does a team and its attitudes adjust to changing circumstances? How do collective intentions and collective commitments arise through dialogue? What is the computational complexity of TeamLog? How can one make TeamLog efficient in applications? This book is an invaluable resource for researchers and graduate students in computer science and artificial intelligence as well as for developers of multi-agent systems. Students and researchers in organizational science, in particular those investigating teamwork, will also find this book insightful. Since the authors made an effort to introduce TeamLog as a conceptual model of teamwork, understanding most of the book requires solely a basic logical background.

To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a $2.5 million competition that challenges university teams to build conversational agents, or “socialbots,” that can converse coherently and engagingly with humans on popular topics for 20 minutes. The Alexa Prize offers the academic community a unique opportunity to perform research at scale with real conversational data obtained by interacting with millions of Alexa users, along with user‐provided ratings and feedback, over several months. This opportunity enables teams to effectively iterate, improve, and evaluate their socialbots throughout the competition. Eighteen teams were selected for the inaugural competition last year. To build their socialbots, the students combined state‐of‐the‐art techniques with their own novel strategies in the areas of natural language understanding and conversational AI. This article reports on the research conducted over the 2017–2018 year. While the 20‐minute grand challenge was not achieved in the first year, the competition produced several conversational agents that advanced the state of the art, that are interesting for everyday users to interact with, and that help form a baseline for the second year of the competition.

With the rapid advancement of artificial intelligence theory, this paper adopts a multi-intelligent agent collaboration method and derives through data mining. In combination with content filtering methods and intelligent agent learning optimization, it improves the high performance by using a personalized information service system architecture. The performance of the library system of vocational colleges. According to the difference of readers’ interest, it matches the results of traditional document retrieval, effectively filtering out readers’ demand information, reducing the time for readers to search for required information, improving reader retrieval efficiency, realizing information push of similar users, and realizing “information looking for people”.

Purpose: This study demonstrates that microcontent, a snippet of personalized content that responds to users' needs, is a form of localization reliant on a content ecology. In contributing to users' localized experiences, technical communicators should recognize their work as part of an assemblage in which users, content, and metrics augment each other to produce personalized content that can be consumed by and delivered through artificial intelligence (AI)-assisted technology. Method: We use an exploratory case study on an AI-driven chatbot to demonstrate the assemblage of user, content, metrics, and AI. By understanding assemblage roles and function of different units used to build AI systems, technical and professional communicators can contribute to microcontent development. We define microcontent as a localized form of content deployed by AI and quickly consumed by a human user through online interfaces. Results: We identify five insertion points where technical communicators can participate in localizing content: * Creating structured content for bots to better meet user needs * Training corpora for bots with data-informed user personas that can better address specific needs of user groups * Developing chatbot user interfaces that are more responsive to user needs * Developing effective human-in-the-loop approaches by moderating content for refining future human-chatbot interactions * Creating more ethically and user-centered data practices with different stakeholders. Conclusion: Technical communicators should teach, research, and practice competencies and skills to advocate for localized users in assemblages of user, content, metrics, and AI.