A rule-based argumentation framework for distributed contextual reasoning in dynamic environments

In this study, we tackled the problem of distributed reasoning in environments in which agents may have incomplete and inconsistent knowledge. Conflicts between agents are resolved through defeasible argumentation-based semantics with a preference function. Support for dynamic environments, where agents constantly enter and leave the system, was achieved by means of rules whose premises can be held by arbitrary agents. Moreover, we presented a formalism that enables agents to share information about their current situation or focus when issuing queries to other agents. This is necessary in environments where agents have a partial view of the world and must be able to cooperate with one another to reach conclusions. Hence, we presented the formalization of a multi-agent system and the argument construction and semantics that define its reasoning approach. Using example scenarios, we demonstrated that our system enables the modeling of a broader range of scenarios than related work.

A logic of defeasible argumentation: Constructing arguments in justification logic

In the 1980s, Pollock’s work on default reasons started the quest in the AI community for a formal system of defeasible argumentation. The main goal of this paper is to provide a logic of structured defeasible arguments using the language of justification logic. In this logic, we introduce defeasible justification assertions of the type t : F that read as “t is a defeasible reason that justifies F”. Such formulas are then interpreted as arguments and their acceptance semantics is given in analogy to Dung’s abstract argumentation framework semantics. We show that a large subclass of Dung’s frameworks that we call “warranted” frameworks is a special case of our logic in the sense that (1) Dung’s frameworks can be obtained from justification logic-based theories by focusing on a single aspect of attacks among justification logic arguments and (2) Dung’s warranted frameworks always have multiple justification logic instantiations called “realizations”. We first define a new justification logic that relies on operational semantics for default logic. One of the key features that is absent in standard justification logics is the possibility to weigh different epistemic reasons or pieces of evidence that might conflict with one another. To amend this, we develop a semantics for “defeaters”: conflicting reasons forming a basis to doubt the original conclusion or to believe an opposite statement. This enables us to formalize non-monotonic justifications that prompt extension revision already for normal default theories. Then we present our logic as a system for abstract argumentation with structured arguments. The format of conflicting reasons overlaps with the idea of attacks between arguments to the extent that it is possible to define all the standard notions of argumentation framework extensions. Using the definitions of extensions, we establish formal correspondence between Dung’s original argumentation semantics and our operational semantics for default theories. One of the results shows that the notorious attack cycles from abstract argumentation cannot always be realized as justification logic default theories.

Culturally Aware Social Robots That Carry Humans Inside Them, Protected by Defeasible Argumentation Systems

After taking note of the conceptual fact that robots may well carry humans inside them, and more specifically that modern AI-infused cars, jets, spaceships, etc. can be viewed as such robots, we present a case study in which inconsistent attitude measurements resulted in the tragic crash in Sweden of such a jet and the death of both pilots. After setting out desiderata for an automated defeasible inductive reasoner able to suitably prevent such tragedies, we formalize the scenario in a first-order defeasible reasoner—OSCAR—and find that it can quickly generate a partial solution to the dilemma the pilots couldn’t conquer. But we then note and address the shortcomings of OSCAR relative to the desiderata, and adumbrate a solution supplied by a more expressive reasoner based on an inductive defeasible multi-operator cognitive calculus (ℐ𝒟𝒞ℰ𝒞) that is inspired by a merely deductive (monotonic) precursor (𝒟𝒞ℰ𝒞). Our solution in this calculus exploits both the social and cultural aspects of of the jet/robot we suggest be engineered in the future. After describing our solution, some remarks about related prior work follow, we present and rebut two objections, and then wrap up with a brief conclusion.

Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

This work proposes a persuasion model based on argumentation theory and users’ characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.