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"Intelligent agents"
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Building intelligent systems : a guide to machine learning engineering
\"Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems.\"--Page 4 of cover.
Book
How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents
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.
Journal Article
\Research Perspectives: The Rise of Human Machines: How Cognitive Computing Systems Challenge Assumptions of User-System Interaction \
Cognitive computing systems (CCS) are a new class of computing systems that implement more human-like cognitive abilities. CCS are not a typical technological advancement but an unprecedented advance toward human-like systems fueled by artificial intelligence. Such systems can adapt to situations, perceive their environments, and interact with humans and other technologies. Due to these properties, CCS are already disrupting established industries, such as retail, insurance, and healthcare. As we make the case in this paper, the increasingly human-like capabilities of CCS challenge five fundamental assumptions that we as IS researchers have held about how users interact with IT artifacts. These assumptions pertain to (1) the direction of the userartifact relationship, (2) the artifact's awareness of its environment, (3) functional transparency, (4) reliability, and (5) the user's awareness of artifact use. We argue that the disruption of these five assumptions limits the applicability of our extant body of knowledge to CCS. Consequently, CCS present a unique opportunity for novel theory development and associated contributions. We argue that IS is well positioned to take this opportunity and present research questions that, if answered, will lead to interesting, influential, and original theories.
Journal Article
Teamwork in Multi-Agent Systems
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.
eBook
Integrating cognitive architectures into virtual character design
\"This book presents emerging research on virtual character artificial intelligence systems and procedures and the integration of cognitive architectures by emphasizing innovative methodologies for intelligent virtual character integration and design\"-- Provided by publisher.
Book
Programming multi-agent systems in AgentSpeak using Jason
This text provides a detailed, practical guide to building multi-agent systems using Jason, one of the most prominent agent programming languages.
eBook
AI Chatbots and Subject Cataloging: A Performance Test
Libraries show an increasing interest in incorporating AI tools into their workflows, particularly easily accessible and free-to-use chatbots. However, empirical evidence is limited regarding the effectiveness of these tools to perform traditionally time-consuming subject cataloging tasks. In this study, researchers sought to assess the performance of AI tools in performing basic subject heading and classification number assignment. Using a well-established instructional cataloging text as a basis, researchers developed and administered a test designed to evaluate the effectiveness of three chatbots (ChatGPT, Gemini, Copilot) in assigning Dewey Decimal Classification, Library of Congress Classification, and Library of Congress Subject Heading terms and numbers. The quantity and quality of errors in chatbot responses were analyzed. Overall performance of these tools was poor, particularly for assigning classification numbers. Frequent sources of error included assigning overly broad numbers or numbers for incorrect topics. Although subject heading assignment was also poor, ChatGPT showed more promise here, backing up previous observations that chatbots may hold more immediate potential for this task. Although AI chatbots do not show promise in reducing time and effort associated with subject cataloging at this time, this may change in the future. For now, findings from this study offer caveats for catalogers already working with these tools and underscore the continuing importance of human expertise and oversight in cataloging.
Journal Article