Claudia Schon

System description: E-KRHyper 1.4: extensions for unique names and description logic

Formal ontologies may go beyond first-order logic (FOL) in their expressivity, hindering the usage of common automated theorem provers (ATP) for ontology reasoning. The Unique Name Assumption (UNA) is an extension to FOL that is valuable for ontology specification, allowing the definition of distinct objects. Likewise, the Description Logic

Automated Reasoning in Deontic Logic

\mathcal{SHIQ}

Deontic Logic for Human Reasoning

is a popular language for knowledge representation (KR). This system description provides details on the extension of the prover E-KRHyper by the ability to handle both the UNA and

Commonsense Reasoning Meets Theorem Proving

\mathcal{SHIQ}

Automated Reasoning in the Wild

. This ATP was developed for embedding in KR applications and hence already equipped with special features and extensions to FOL, making it natural to add the new capabilities in E-KRHyper version 1.4. We report on the theory, the implementation and also the evaluation results of the new features.

Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges.

Deontic logic is a very well researched branch of mathematical logic and philosophy. Various kinds of deontic logics are discussed for different application domains like argumentation theory, legal reasoning, and acts in multi-agent systems. In this paper, we show how standard deontic logic can be stepwise transformed into description logic and DL-clauses, such that it can be processed by Hyper, a high performance theorem prover which uses a hypertableau calculus. Two use cases, one from multi-agent research and one from the development of normative system are investigated.

Semantically Guided Evolution of \(\mathcal{SHI}\) ABoxes

Deontic logic is shown to be applicable for modelling human reasoning. For this the Wason selection task and the suppression task are discussed in detail. Different versions of modelling norms with deontic logic are introduced and in the case of the Wason selection task it is demonstrated how differences in the performance of humans in the abstract and in the social contract case can be explained. Furthermore, it is shown that an automated theorem prover can be used as a reasoning tool for deontic logic.

Linkless Normal Form for \(\mathcal{ALC}\) Concepts and TBoxes

The area of commonsense reasoning aims at the creation of systems able to simulate the human way of rational thinking. This paper describes the use of automated reasoning methods for tackling commonsense reasoning benchmarks. For this we use a benchmark suite introduced in literature. Our goal is to use general purpose background knowledge without domain specific hand coding of axioms, such that the approach and the result can be used as well for other domains in mathematics and science. Furthermore, we discuss the modeling of normative statements in commonsense reasoning and in robot ethics (This paper is an extended version of the informal proceedings [9] and [10]).

The RatioLog Project: Rational Extensions of Logical Reasoning

This paper discusses the use of first order automated reasoning in question answering and cognitive computing. For this the natural language question answering project LogAnswer is briefly depicted and the challenges faced therein are addressed. This includes a treatment of query relaxation, web-services, large knowledge bases and co-operative answering. In a second part a bridge to human reasoning as it is investigated in cognitive psychology is constructed by using standard deontic logic.

Deontic Logic for Human Reasoning.

Current trends, like digital transformation and ubiquitous computing, yield in massive increase in available data and information. In artificial intelligence (AI) systems, capacity of knowledge bases is limited due to computational complexity of many inference algorithms. Consequently, continuously sampling information and unfiltered storing in knowledge bases does not seem to be a promising or even feasible strategy. In human evolution, learning and forgetting have evolved as advantageous strategies for coping with available information by adding new knowledge to and removing irrelevant information from the human memory. Learning has been adopted in AI systems in various algorithms and applications. Forgetting, however, especially intentional forgetting, has not been sufficiently considered, yet. Thus, the objective of this paper is to discuss intentional forgetting in the context of AI systems as a first step. Starting with the new priority research program on ‘Intentional Forgetting’ (DFG-SPP 1921), definitions and interpretations of intentional forgetting in AI systems from different perspectives (knowledge representation, cognition, ontologies, reasoning, machine learning, self-organization, and distributed AI) are presented and opportunities as well as challenges are derived.