Marija Slavkovik

This article provides a short report on a recent Dagstuhl Seminar on “Engineering Moral Agents”. Imbuing robots and autonomous systems with ethical norms and values is an increasingly urgent challenge, given rapid developments in, for example, driverless cars, unmanned air vehicles (drones), and care assistant robots. Seminar participants discussed two immediate problems. A challenge for philosophical research is the formalisation of ethics in a format that lends itself to machine implementation; a challenge for computer science and robotics is the actual implementation of moral reasoning and conduct in autonomous systems. This article reports on these two challenges.

Social bots are computer programs that act like human users on social media platforms. Social bot detection is a rapidly growing field dominated by machine learning approaches. In this paper, we propose a complementary method to machine learning by exploring bot detection as a model checking problem. We introduce Temporal Network Logic (TNL) which we use to specify social networks where agents can post and follow each other. Using this logic, we formalize different types of social bot behavior with formulas that are satisfied in a model of a network with bots. We also consider an extension of the logic where we explore the expressive power of including elements from hybrid logic in our framework. We give model checking algorithms for TNL and its hybrid extension, and show that the complexity of the former is in p and the latter in pspace.

Software-controlled bots, also called social bots, are computer programs that act like human users on social media platforms. Recent work on detection of social bots is dominated by machine learning approaches. In this paper we explore bot detection as a model checking problem. We introduce Temporal Network Logic which we use to specify social networks where agents can post and follow each other. In this logic we formalize different types of social bot behavior. These are formulas that are satisfied in a model of a network with bots. We provide a simple algorithm to extract a logical model from a real-life social network. We show that we can reduce TNL to a fragment of linear temporal logic with past and use this to establish the computational efficiency of model checking for social bot detection.

The European Summer School in Logic, Language and Information is organized every year by the Association for Logic, Language and Information in different sites around Europe. The main focus of ESSLLI is on the interface between linguistics, logic and computation. ESSLLI offers foundational, introductory and advanced courses, as well as workshops, covering a wide variety of topics within the three areas of interest: Language and Computation, Language and Logic, and Logic and Computation. During two weeks, around 50 courses and 10 workshops are offered to the attendants, each of 1.5 hours per day during a five days week, with up to seven parallel sessions. ESSLLI also includes a student session and four evening lectures by senior scientists in the covered areas. The 15 revised full papers presented were carefully reviewed and selected. The papers are organized in topical sections on The papers are organized in topical sections on language and computation; logic and computation; and logic and language.

We introduce a conceptual model for reaching group decisions. Our model extends a well-known, single-agent cognitive model, the recognition-primed decision (RPD) model. The RPD model includes a recognition phase and an evaluation phase. Group extensions of the RPD model, applicable to a group of RPD agents, have been considered in the literature, however the proposed models do not formalize how distributed and possibly inconsistent information can be combined in either phase. We show how such information can be utilized by aggregating it using a specific social choice method, namely judgment aggregation. Our model is applicable to hierarchical groups of agents containing at least one RPD agent