scholarly journals Rationalisation of Profiles of Abstract Argumentation Frameworks: Characterisation and Complexity

2017 ◽  
Vol 60 ◽  
pp. 149-177 ◽  
Author(s):  
Stéphane Airiau ◽  
Elise Bonzon ◽  
Ulle Endriss ◽  
Nicolas Maudet ◽  
Julien Rossit
Keyword(s):  
Artificial Intelligence ◽  
Social Choice ◽  
Research Agenda ◽  
Multiagent System ◽  
Social Values ◽  
Algorithmic Problem ◽  
Argumentation Framework ◽  
Popular Approach ◽  
Abstract Argumentation ◽  
Points Of View

Different agents may have different points of view. Following a popular approach in the artificial intelligence literature, this can be modeled by means of different abstract argumentation frameworks, each consisting of a set of arguments the agent is contemplating and a binary attack-relation between them. A question arising in this context is whether the diversity of views observed in such a profile of argumentation frameworks is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual attack-relation between the arguments and, second, the personal preferences of the agent concerned regarding the moral or social values the arguments under scrutiny relate to. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. In doing so, we distinguish different constraints on admissible rationalisations, e.g., concerning the types of preferences used or the number of distinct values involved. We also distinguish two different semantics for rationalisability, which differ in the assumptions made on how agents treat attacks between arguments they do not report. This research agenda, bringing together ideas from abstract argumentation and social choice, is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

scholarly journals Rationalisation of Profiles of Abstract Argumentation Frameworks: Extended Abstract

2017 ◽  
Author(s):  
Stephane Airiau ◽  
Elise Bonzon ◽  
Ulle Endriss ◽  
Nicolas Maudet ◽  
Julien Rossit
Keyword(s):  
Multiagent System ◽  
Algorithmic Problem ◽  
Factual Information ◽  
Argumentation Framework ◽  
Abstract Argumentation

We review a recently introduced model in which each of a number of agents is endowed with an abstract argumentation framework reflecting her individual views regarding a given set of arguments. A question arising in this context is whether the diversity of views observed in such a situation is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual information and, second, the personal preferences of the agent concerned. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. This is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

scholarly journals Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

2021 ◽  
Vol 7 (1) ◽  
pp. 71
Author(s):  
Tan Yigitcanlar ◽  
Juan M. Corchado ◽  
Rashid Mehmood ◽  
Rita Yi Man Li ◽  
Karen Mossberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Local Government ◽  
Conceptual Framework ◽  
Research Agenda ◽  
Future Studies ◽  
Expert Team ◽  
Research Questions ◽  
Urban Problems ◽  
Technological Opportunities ◽  
Made In

The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.

How and where is artificial intelligence in the public sector going? A literature review and research agenda

2019 ◽  
Vol 36 (4) ◽  
pp. 101392 ◽  
Author(s):  
Weslei Gomes de Sousa ◽  
Elis Regina Pereira de Melo ◽  
Paulo Henrique De Souza Bermejo ◽  
Rafael Araújo Sousa Farias ◽  
Adalmir Oliveira Gomes
Keyword(s):  
Artificial Intelligence ◽  
Public Sector ◽  
Literature Review ◽  
Research Agenda ◽  
The Public

Framework kognitive Produktionsarbeit 4.0/Framework for Cognitive Production Work 4.0 – Conceptualization and Model Approach for the Production System of the Future within the Future Work Lab

2020 ◽  
Vol 110 (03) ◽  
pp. 108-112
Author(s):  
Simon Schumacher ◽  
Bastian Pokorni
Keyword(s):  
Artificial Intelligence ◽  
Production System ◽  
Research Agenda ◽  
Reference Model ◽  
Work Organisation ◽  
Research Topics ◽  
Interdisciplinary Project ◽  
The Future ◽  
Future Work ◽  
Model Approach

Das Future Work Lab ist ein Innovationslabor für Arbeit, Mensch und Technik am Standort Stuttgart mit Fokus auf Künstlicher Intelligenz (KI) und vernetzter Arbeitsorganisation. Ein zentraler Bestandteil ist das Framework kognitive Produktionsarbeit 4.0, das als Referenzmodell für das Themenfeld Produktionsarbeit 4.0 dienen soll. Ein entsprechendes Konzept wurde in einem interdisziplinären Projektteam entwickelt. In diesem Beitrag wird das Grobmodell vorgestellt und die weitere Forschungsagenda präsentiert.   The Future Work Lab is an innovation lab for work, people and technology in Stuttgart, Germany with a focus on artificial intelligence and interconnected work organisation. A key component consists of the framework for cognitive production work 4.0, which will serve as a reference model for the research topics. A corresponding concept was developed in an interdisciplinary project team. In this article the raw model is introduced and the further research agenda is presented.

Human Factors Considerations for Teaming between Construction Workersand Voice-based Intelligent Virtual Agent (VIVA)

2021 ◽  
Vol 65 (1) ◽  
pp. 72-76
Author(s):  
Maryam Rahimi Movassagh ◽  
Nazila Roofigari-Esfahan ◽  
Sang Won Lee ◽  
Carlos Evia ◽  
David Hicks ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Research Agenda ◽  
Task Environment ◽  
Virtual Agent ◽  
Work Processes ◽  
Construction Sites

Construction sites experience low productivity due to particular characteristics such as unique designs in each project, sporadic arrival of projects, and complexity of the process. Another contributing factor to low productivity is poor communication among workers, supervisors, and a site’s centralized knowledge hub. Research shows that introducing advanced artificial intelligence (AI) technology in construction can tackle these problems. In this paper, we analyzed human factors considerations–user, task, environment, and technology and identified their characteristics and challenges to design an interactive AI system to facilitate communication between workers and other stakeholders. Based on the analysis, we propose a voice-based intelligent virtual agent (VIVA) as a multi-purpose AI system on construction sites with a further research agenda. We hope that this effort can guide the design of construction-specific AI systems and that this worker-AI teaming can improve overall work processes, enhance productivity, and promote safety in construction.

Engineering of Experience Based Trust for E-Commerce

2010 ◽  
pp. 342-367 ◽  
Author(s):  
Zhaohao Sun ◽  
Jun Han ◽  
Dong Dong ◽  
Shuliang Zhao
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Research And Development ◽  
Multiagent Systems ◽  
System Architecture ◽  
Intelligent Systems ◽  
Multiagent System ◽  
Theoretical Foundation ◽  
Knowledge Based ◽  
Trust Inference

Trust is significant for sustainable development of e-commerce and has received increasing attention in e-commerce, multiagent systems (MAS), and artificial intelligence (AI). However, little attention has been given to the theoretical foundation and intelligent techniques for trust in e-commerce from a viewpoint of intelligent systems and engineering. This chapter will fill this gap by examining engineering of experience-based trust in e-commerce from the viewpoint of intelligent systems. It looks at knowledgebased trust, inference-based trust and their interrelationships with experience-based trust. It also examines scalable trust in e-commerce. It proposes a knowledge based model of trust in e-commerce and a system architecture for METSE: a multiagent system for experience-based trust in e-commerce. The proposed approach in this chapter will facilitate research and development of trust, multiagent systems, e-commerce and e-services.

Engineering AI Systems

2021 ◽  
pp. 1-19 ◽  
Author(s):  
Jan Bosch ◽  
Helena Holmström Olsson ◽  
Ivica Crnkovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Case Studies ◽  
Research Agenda ◽  
Production Quality ◽  
Research Community ◽  
Design Methods ◽  
Engineering Approach ◽  
Quality Design

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.

scholarly journals How artificial intelligence will change the future of marketing

2019 ◽  
Vol 48 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Thomas Davenport ◽  
Abhijit Guha ◽  
Dhruv Grewal ◽  
Timna Bressgott
Keyword(s):  
Artificial Intelligence ◽  
Research Agenda ◽  
Marketing Strategies ◽  
Important Policy ◽  
The Future ◽  
Task Types ◽  
The Impact ◽  
Future Of Marketing

Abstract In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.

Social choice ethics in artificial intelligence

AI & Society ◽  
2017 ◽  
Vol 35 (1) ◽  
pp. 165-176 ◽  
Author(s):  
Seth D. Baum
Keyword(s):  
Artificial Intelligence ◽  
Social Choice
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