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Interaction- and Communication-based Systems


Prof. Dr. Simon Mayer

In our research group, we explore interactions among devices and people in ubiquitous computing environments. Our focus is on the integration of physical things into the Web, on increasing the autonomy of Web-enabled devices, and on making interactions of connected devices intelligible for people.

News

Towards Semantic Content Negotiation

A new poster with involvement of our group has been published at the 23rd International Conference on Knowledge Engineering and Knowledge Management: A Step Toward Semantic Content Negotiation

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Abstract:

Content negotiation aims at enabling a server to provide a client with a representation of a resource that meets its needs. However, client and server might desire to negotiate constraints that go beyond the media type or language of the alternative representation. This is especially true in the Semantic Web, as a resource can be described with a single media type, but with different vocabularies (FOAF, schema.org, etc.), and may match specific patterns. In this paper, we propose an approach to increase the flexibility when negotiating a representation between client and server. Our approach follows the goals of the World Wide Web and uses a set of existing technologies: SHACL and profile-based negotiation. We define the mechanism (in terms of protocol and algorithm) for clients to announce their expectations and for servers to react and respond to them. We then explain, through a use case, how the same approach could be used in Web-based Multi-Agent Systems to help autonomous agents achieve their goals on the Web.

Link to the full paper

Author: Simon Mayer

Date: 28. September 2022

Machine Capacity of Judgment

A new paper from our group has been published in Technology and Society: Machine Capacity of Judgment: An Interdisciplinary Approach for making Machine Intelligence Transparent to End Users.

Abstract: Intelligent machines surprise us with unexpected behaviors, giving rise to the question of whether such machines exhibit autonomous judgment. With judgment comes (the allocation of) responsibility. While it can be dangerous or misplaced to shift responsibility from humans to intelligent machines, current frameworks to think about responsible and transparent distribution of responsibility between all involved stakeholders are lacking. A more granular understanding of the autonomy exhibited by intelligent machines is needed to promote a more nuanced public discussion and allow laypersons as well as legal experts to think about, categorize, and differentiate among the capacities of artificial agents when distributing responsibility. To tackle this issue, we propose criteria that would support people in assessing the Machine Capacity of Judgment (MCOJ) of artificial agents. We conceive MCOJ drawing from the use of Human Capacity of Judgment (HCOJ) in the legal discourse, where HCOJ criteria are legal abstractions to assess when decision-making and judgment by humans must lead to legally binding actions or inactions under the law. In this article, we show in what way these criteria can be transferred to machines.

Link to the full paper

Author: Simon Mayer

Date: 7. September 2022

A Typology of Automatically Processable Regulation

A new paper from our group has been published in Law, Innovation and Technology: A Typology of Automatically Processable Regulation.

Abstract: The possibility of encoding regulation to make it processable automatically by computers has been gaining attention within the legal discipline. With it, an abundance of terms has emerged as much as an array of academic discussions providing different examples, raising different concerns, while, unfortunately, having different premises in mind. This makes contributions within the field of what we refer to as ‘automatically processable regulation’ difficult to compare with each other and research results hard to transfer among different research projects and groups. To overcome this problem, we propose a typology that enables researchers to locate their research project within the domain of automatically processable regulation, understand what issues might arise depending on where within the typology a project falls, and determine the relationship between projects.The typology revolves around three dimensions: the primary aim of the project, the potential for divergence of interests amongst stakeholders, and the degree of mediation by computers.

Link to the full paper

Author: Simon Mayer

Date: 6. September 2022

Why Worry About Automatically Processable Regulation?

A new paper from our group that provides a framework to structure arising issues of computational law while bridging the gap between theoretical literature and practical implementation has been published in the August 2022 issue of Artificial Intelligence and Law.

Abstract: The field of computational law has increasingly moved into the focus of the scientific community, with recent research analysing its issues and risks. In this article, we seek to draw a structured and comprehensive list of societal issues that the deployment of automatically processable regulation could entail. We do this by systematically exploring attributes of the law that are being challenged through its encoding and by taking stock of what issues current projects in this field raise. This article adds to the current literature not only by providing a needed framework to structure arising issues of computational law but also by bridging the gap between theoretical literature and practical implementation. Key findings of this article are: (1) The primary benefit (efficiency vs. accessibility) sought after when encoding law matters with respect to the issues such an endeavor triggers; (2) Specific characteristics of a project—project type, degree of mediation by computers, and potential for divergence of interests—each impact the overall number of societal issues arising from the implementation of automatically processable regulation.

Access the full paper via Alexandria or in Artificial Intelligence and Law.

Author: Simon Mayer

Date: 9. August 2022

The FutureMe Mobile Health Intervention

A new paper from our group and together with colleagues from across the University of St.Gallen and ETH Zurich has been published in the July 2022 issue of the Journal of Medical Internet Research.

Abstract: In the paper, we aimed to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior and examine the feasibility of a novel automated nutrition tracking system. We also aimed to understand how this intervention impacts related attitudinal and motivational constructs. We recruited 167 participants, and 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The FutureMe intervention led to small statistically insignificant increases in physical activity (median +242 steps/day) and small insignificant improvements in the nutritional quality of food purchases (median −1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased in the FutureMe group, but decreased in the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and accepted fully automated nutrition tracking system.

Link to the full paper on Alexandria and on JMIR

Author: Simon Mayer

Date: 15. July 2022

Semantic Knowledge for Autonomous Smart Farming

A new paper from our group on the integration of autonomous agents (software agents as well as humans) to perform complex agricultural tasks while integrating heterogeneous devices and services across multiple vendors will be presented at the 7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture.

Abstract: A higher degree of automation – and autonomization – of agricultural processes is expected to lead to productivity gains, especially in light of more environmentally-friendly farming practices, while improving the safety of agricultural processes. To exploit the potential of this development, it should be possible to flexibly integrate devices and services within service mashups, and thereby enable them to provide higher-value services together. However, current farm automation tools instead tend to reinforce vertical functional silos and tight coupling within often proprietary systems that manage the farm environment information. We propose to describe capabilities of individual devices and services and interlink them across components and with the description of the farm environment. We posit that this will better enable autonomous agents – software agents as well as humans – to perform complex agricultural tasks while integrating heterogeneous devices and services across multiple vendors. Concretely, we describe – and demonstrate in a laboratory setting – the usage of a Knowledge Graph to describe the environment and equipment used to perform farming tasks. We show how a multi-agent-based automation system for smart farming uses this graph to reason about the state of the environment and the agents to plan the achievement of user-specified goals. Furthermore, we show how such knowledge-driven autonomous systems may include human agents alongside artificial agents as first-class citizens, towards realizing “Social Machines” in the agriculture domain.

Link to the full paper

Author: Simon Mayer

Date: 4. July 2022

Secure Communication with Batteryless Sensors

A new paper from our group and with colleagues from ETH Zurich and KU Leuven was published at the 11th Mediterranean Conference on Embedded Computing.

Abstract: Batteryless sensors have recently been proposed as an energy-efficient and cost-effective alternative to battery-powered sensors. By harvesting and immediately consuming ambient energy, it becomes unnecessary to design systems with large energy storages, which significantly increases their form factor, cost, and environmental impact. Many works have focused on designing batteryless systems that sense data, process it, and wirelessly broadcast processed data. Yet wireless security aspects of batteryless applications are only now receiving attention. In this work, we propose a secure communication system based on symmetric key encryption, that enables batteryless sensors to securely broadcast data. Furthermore, we demonstrate this system on a batteryless smartcard in two application scenarios: static sensor deployment for secure data gathering, and mobile device for identification purposes. Our experimental results demonstrate not only the feasibility of secure communication with batteryless devices but also the small overheads of introducing security in wireless beaconing applications.

Link to the full paper

Author: Simon Mayer

Date: 17. June 2022

Explainability of Cyber-Physical Systems

A new paper from our group on the explainability of cyber-physical systems was presented at the FLAIRS-35 Conference.

Abstract: The increase in automating complicated physical processes using Cyber-Physical Systems (CPS) raises the complexity of CPS and their behavior. It creates the necessity to make them explainable. The popular Explainable Artificial Intelligence (XAI) methodologies employed to explain the behavior of CPS usually overlook the impact of physical and virtual context when explaining the outputs of decision-making software models, which are essential factors in explaining CPS’ behavior to stakeholders. Hence in this article, we survey the most relevant XAI methods to identify their shortcomings and applicability in explaining the behavior of CPS. Our main findings are (i) Several papers emphasize the relevance of context in describing CPS. However, the approaches for explaining CPS fall short of being context-aware; (ii) the explanation delivery mechanisms use low-level visualization tools that make the explanations unintelligible. Finally (iii), these unintelligible explanations lack actionability. Therefore, we propose to enrich the explanations further with contextual information using Semantic Technologies, user feedback, and enhanced explanation visualization techniques to improve their understandability. To that end, context-aware explanation and better explanation presentation based on knowledge graphs might be a promising research direction for explainable CPS.

Link to the full paper

Author: Simon Mayer

Date: 17. June 2022

Team

Mayer

Full Professor, Interaction and Communication based Systems

Vachtsevanou

Research Assistant, Hypermedia Multi-agent Systems