Author name cluster

Christian Eichhorn

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

AIJ Journal 2021 Journal Article

Properties and interrelationships of skeptical, weakly skeptical, and credulous inference induced by classes of minimal models

Christoph Beierle
Christian Eichhorn
Gabriele Kern-Isberner
Steven Kutsch

There are multiple ways of defining nonmonotonic inference relations based on a conditional knowledge base. While the axiomatic system P is an important standard for such plausible nonmonotonic reasoning, inference relations obtained from system Z or from c-representations have been designed which go beyond system P by selecting preferred models for inference. For any class of models M, we propose the notion of weakly skeptical inference, first introduced in an ECAI conference paper this article revises and extends, that lies between skeptical and credulous inference with respect to M. Weakly skeptical c-inference properly extends skeptical c-inference, but avoids disadvantages of a too liberal credulous c-inference. We extend the concepts of skeptical, weakly skeptical, and credulous c-inference modes by taking models obtained from different minimality criteria into account. We illustrate the usefulness of the obtained inference relations and show that they fulfill various desirable properties put forward for nonmonotonic reasoning. Furthermore, we elaborate in detail the interrelationships among the inference relations when taking the different inference modes and various classes of minimal models into account.

Details DOI

AAAI Conference 2018 Conference Paper

Rational Inference Patterns Based on Conditional Logic

Christian Eichhorn
Gabriele Kern-Isberner
Marco Ragni

Conditional information is an integral part of representation and inference processes of causal relationships, temporal events, and even the deliberation about impossible scenarios of cognitive agents. For formalizing these inferences, a proper formal representation is needed. Psychological studies indicate that classical, monotonic logic is not the approriate model for capturing human reasoning: There are cases where the participants systematically deviate from classically valid answers, while in other cases they even endorse logically invalid ones. Many analyses covered the independent analysis of individual inference rules applied by human reasoners. In this paper we deﬁne inference patterns as a formalization of the joint usage or avoidance of these rules. Considering patterns instead of single inferences opens the way for categorizing inference studies with regard to their qualitative results. We apply plausibility relations which provide basic formal models for many theories of conditionals, nonmonotonic reasoning, and belief revision to asses the rationality of the patterns and thus the individual inferences drawn in the study. By this replacement of classical logic with formalisms most suitable for conditionals, we shift the basis of judging rationality from compatibility with classical entailment to consistency in a logic of conditionals. Using inductive reasoning on the plausibility relations we reverse engineer conditional knowledge bases as explanatory model for and formalization of the background knowledge of the participants. In this way the conditional knowledge bases derived from the inference patterns provide an explanation for the outcome of the study that generated the inference pattern.

PDF Details

IJCAI Conference 2016 Conference Paper

Simulating Human Inferences in the Light of New Information: A Formal Analysis

Marco Ragni
Christian Eichhorn
Gabriele Kern-Isberner

Human answer patterns in psychological reasoning experiments systematically deviate from predictions of classical logic. When interactions between any artificial reasoning system and humans are necessary this difference can be useful in some cases and lead to problems in other cases. Hence, other approaches than classical logic might be better suited to capture human inference processes. Evaluations are rare of how good such other approaches, e. g. , non-monotonic logics, can explain psychological findings. In this article we consider the so-called Suppression Task, a core example in cognitive science about human reasoning that demonstrates that some additional information can lead to the suppression of simple inferences like the modus ponens. The psychological findings for this task have often been replicated and demonstrate a key-effect of human inferences. We analyze inferences of selected formal approaches and compare them by their capacity to cover human inference observed in the Suppression Task. A discussion on formal properties of successful theories conclude the paper.

PDF Details