The so-called Internet of Things is of increasing importance for facilitating productivity across industries, i.e., by connecting sensors with manufacturing lines and IT system landscapes with an increasing degree of autonomy. In this context, a common challenge is enabling reasonable trade-offs between structure and control on the one hand and flexibility and human-like intelligent behavior on the other hand. To address this challenge, we establish the need for and requirements of a hybrid IoT-/agent-based business process orchestration architecture that utilizes open standards. We propose a four-layered architecture, which integrates autonomous agents and business process orchestration for IoT/agents, and provide a running example for a supply chain management (purchasing) use case.

Complex information systems operate with increasing degrees of autonomy. Consequently, such systems should not only optimize for simple metrics (like clicks and views) that reflect the system provider's preferences but also consider norms or rules, as well as the preferences of other agents that are affected by the systems' actions. As a means to achieve such behavior, we propose the design and development of empathic agents that use a mixed rule/utility-based approach when deciding on how to act, considering both their own and others' utility functions. The agents make use of formal argumentation to reach an agreement on how to act in case of inconsistent beliefs. A promising domain for applying our empathic agents is recommender systems.

Identifying and resolving conflicts of interests is a key challenge when designing autonomous agents. For example, such conflicts often occur when complex information systems interact persuasively with humans and are in the future likely to arise in non-human agent-to-agent interaction. We introduce a theoretical framework for an empathic autonomous agent that proactively identifies potential conflicts of interests in interactions with other agents (and humans) by considering their utility functions and comparing them with its own preferences using a system of shared values to find a solution all agents consider acceptable. To illustrate how empathic autonomous agents work, we provide running examples and a simple prototype implementation in a general-purpose programing language. To give a high-level overview of our work, we propose a reasoning-loop architecture for our empathic agent.

There is a multitude of agent-oriented software engineering frame-works available, most of them produced by the academic multi-agent systemscommunity. However, these frameworks often impose programming paradigmson their users that are hard to learn for engineers who are used to modern high-level programming languages such as JavaScript and Python. To show how theadoption of agent-oriented programming by the software engineering mainstreamcan be facilitated, we provide an early, simplistic JavaScript library prototype forimplementing belief-desire-intention (BDI) agents. The library focuses on thecore BDI concepts and refrains from imposing further restrictions on the pro-gramming approach. To illustrate its usefulness, we demonstrate how the librarycan be used for multi-agent systems simulations on the web, as well as embeddedin Python-based data science tools.

The spread of radical opinions, facilitated by homophilic Internet communities (echo chambers), has become a threat to the stability of societies around the globe. The concept of choice architecture-the design of choice information for consumers with the goal of facilitating societally beneficial decisions-provides a promising (although not uncontroversial) general concept to address this problem. The choice architecture approach is reflected in recent proposals advocating for recommender systems that consider the societal impact of their recommendations and not only strive to optimize revenue streams. However, the precise nature of the goal state such systems should work towards remains an open question. In this paper, we suggest that this goal state can be defined by considering target opinion spread in a society on different topics of interest as a multivariate normal distribution; i.e., while there is a diversity of opinions, most people have similar opinions on most topics. We explain why this approach is promising, and list a set of cross-disciplinary research challenges that need to be solved to advance the idea.

Technologies that shape human behavior are of high societal relevance, both when considering their current impact and their future potential. In information systems research and in behavioral psychology, such technologies are typically referred to as persuasive technologies. Traditional definitions like the ones created by Fogg, and Harjumaa and Oinas-Kukkonen, respectively, limit the scope of persuasive technology to non-coercive, non-deceptive technologies that are explicitly designed for persuasion. In this paper we analyze existing technologies that blur the line between persuasion, deception,and coercion. Based on the insights of the analysis, we lay down an updated definition of persuasive technologies that includes coercive and deceptive forms of persuasion. Our definition also accounts for persuasive functionality that was not designed by the technology developers. We argue that this definition will help highlight ethical and societal challenges related to technologies that shape human behavior and encourage research that solves problems with technology-driven persuasion. Finally, we suggest multidisciplinary research that can help address the challenges our definition implies. The suggestions we provide range from empirical studies to multi-agent system theory.

In a previous publication, we introduced the core concepts of empathic agents as agents that use a combination of utility-based and rule-based approaches to resolve conflicts when interacting with other agents in their environment. In this work, we implement proof-of-concept prototypes of empathic agents with the multi-agent systems development framework Jason and apply argumentation theory to extend the previously introduced concepts to account for inconsistencies between the beliefs of different agents. We then analyze the feasibility of different admissible set-based argumentation semantics to resolve these inconsistencies. As a result of the analysis we identify the maximal ideal extension as the most feasible argumentation semantics for the problem in focus.

Identifying and resolving conflicts of interests is a key challenge when designing autonomous agents. For example, such conflicts often occur when complex information systems interact persuasively with humans and are in the future likely to arise in non-human agent-to-agent interaction. We work towards a theoretical framework for an empathic autonomous agent that proactively identifies potential conflicts of interests in interactions with other agents (and humans) byl earning their utility functions and comparing them with its own preferences using a system of shared values to find a solution all agents consider acceptable.To provide a high-level overview of our work, we propose a reasoning-loop architecture to address the problem in focus. To realize specific components of the architecture, we suggest applying existing concepts in argumentation and utility theory. Reinforcement learning methods can be used by the agent to learn from and interact with its environment.