Third Workshop on Human Reasoning and Computational Logic
From the 4th to the 5th of April 2019, we organize the third workshop on Human Reasoning and Computational Logic at TU Dresden, Germany. The goal of this workshop is to provide a platform for the scientific exchange with respect to Human Reasoning between the areas of Cognitive Science and Computational Logic. For the first and second workshop on Human Reasoning and Computational Logic at TU Dresden, see 2017 and 2018.
Dates
- 4th to 5th of April, 2019
- Participation fee is 50EUR (negotiable), please contact Emmanuelle Dietz for details
- Dinner on 4th of April, at Genussatelier
Venue
The workshop is held at the Computer Science Faculty building of Technische Universität Dresden, Nöthnitzer Straße 46, Dresden-Räcknitz
How to reach us
Contact person
Cognitive Argumentation by Antonis Kakas (University of Cyprus) joint work with Emmanuelle Dietz (Technische Universität Dresden)
Cognitive Argumentation emerges out of the synthesis of argumentation in AI and empirical and theoretical studies of the psychology of reasoning from Cognitive Psychology and Philosophy. Formal argumentation in AI provides a good computational basis for argumentation but for this to become a cognitive model for human reasoning it needs to be heavily informed and guided by cognitive principles of human thinking that are identified out of studies in Cognitive Psychology over many decades now. Realizing Cognitive Argumentation we need to consider how cognitive principles affect and to a certain extent determine the construction and evaluation of arguments. This talk will present the general framework of Cognitive Argumentation and illustrate this in human conditional reasoning. A related system, called COGNICA, will be demonstrated.
Ethical Decision Making under the Weak Completion Semantics by Steffen Hölldobler (Technische Universität Dresden)
The weak completion semantics is a novel computational theory based on logic programs. It is extended to deal with equalities, which is a prerequisite to represent and reason about actions and causality as in the fluent calculus. This is discussed in the context of ethical decision making. In order to decide questions about the moral permissibility of actions, counterfactuals need to be considered. Somewhat surprisingly, this can be straightforwardly done in the extended approach.
CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning by Sophie Siebert and Frieder Stolzenburg (Hochschule Harz)
Commonsense reasoning is an everyday task that is intuitive for humans but hard to implement for computers. It requires large knowledge bases to get the required data from, although this data is still incomplete or even inconsistent. While machine learning algorithms perform rather well on these tasks, the reasoning process remains a black box. To close this gap, our system CoRg aims to build an explainable and well-performing system, which consists of both an explainable deductive derivation process and a machine learning part. We conduct our experiments on the Copa question-answering benchmark using the ontologies WordNet, Adimen-SUMO, and ConceptNet. The knowledge is fed into the theorem prover Hyper and in the end the conducted models will be analyzed using machine learning algorithms, to derive the most probable answer.
Names are not just Sound and Smoke: Word Embeddings for Axiom Selection by Claudia Schon (University of Koblenz)
First order theorem proving with large knowledge bases makes it necessary to select those parts of the knowledge base, which are necessary to prove the theorem at hand. We propose to extend syntactic axiom selection procedures like SInE to the use of the semantics of symbol names. For this not only occurrences of symbol names but also similar names are taken into account. We propose to use a similarity measure based on word embeddings like ConceptNet Numberbatch. An evaluation of this similarity based SInE is given by using problem sets from TPTP's CSR problem class and Adimen-SUMO. This evaluation is done with two very different systems, namely the HYPER tableau prover and the saturation based system E.
Inference patterns for rational human reasoning by Gabriele Kern-Isberner (Technische Universität Dortmund) joint work with Christian Eichhorn and Marco Ragni (based on the paper "Rational Inference Patterns Based on Conditional Logic", published with AAAI-2018)
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 appropriate 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 talk we present 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.
The CCOBRA Framework for Evaluating Models for Human Reasoning by Nicolas Riesterer ( Albert-Ludwigs-Universität Freiburg)
Empirical Study on Human Evaluation of Complex Argumentation Frameworks by Marcos Cramer (Technische Universität Dresden)
In abstract argumentation, multiple argumentation semantics have been proposed that allow to select sets of jointly acceptable arguments from a given argumentation framework, i.e. based only on the attack relation between arguments. The existence of multiple argumentation semantics raises the question which of these semantics predicts best how humans evaluate arguments. Previous empirical cognitive studies that have tested how humans evaluate sets of arguments depending on the attack relation between them have been limited to a small set of very simple argumentation frameworks, so that some semantics studied in the literature could not be meaningfully distinguished by these studies. In this paper we report on an empirical cognitive study that overcomes these limitations by taking into consideration twelve argumentation frameworks of three to eight arguments each. These argumentation frameworks were mostly more complex than the argumentation frameworks considered in previous studies. All twelve argumentation framework were systematically instantiated with natural language arguments based on a certain fictional scenario, and participants were shown both the natural language arguments and a graphical depiction of the attack relation between them. Our data shows that among the existing semantics, grounded and CF2 semantics were the best predictors of human argument evaluation. In combination with certain theoretical considerations, the results also motivated the development of the SCF2 semantics, a new argumentation semantics similar to CF2.
Abductive reasoning in a neural-symbolic system by Andrzej Gajda (Adam Mickiewicz University Poznań)
A Semantics for Conditionals with Default Negation by Marco Wilhelm (TU Dortmund) joint work with Christian Eichhorn, Richard Niland, and Gabriele Kern-Isberner
Ranking functions constitute a powerful formalism for non-monotonic reasoning based on qualitative conditional knowledge. Conditionals are formalized defeasible rules and thus allow to express that certain individuals or subclasses of some broader concept behave differently. More precisely, in order to model these exceptions by means of ranking functions, it is necessary to state that they behave contrarily with respect to the considered property. This paper proposes conditionals with default negation which instead enable a knowledge engineer to formalize exceptions without giving more specific information. This is useful when a subclass behaves indifferent towards a certain property, or the knowledge engineer wants to exclude a certain subclass because she is not aware of its behavior. Based on this novel type of conditionals, we further present and discuss a nonmonotonic inference formalism.
The talks will take place in APB 2026.
Thursday, 4.4.2019
11:00 - 11:15 Opening
11:15 - 11:45 Ethical Decision Making under the Weak Completion Semantics by Steffen Hölldobler
11:45 - 13:00 Lunch Break
13:00 - 14:00 Talk in Knowledge Based Systems Seminar in APB 3027. See here for more information.
14:00 - 14:45 Cognitive Argumentation by Antonis Kakas
14:45 - 15:00 Coffee break
15:00 - 15:30 Empirical Study on Human Evaluation of Complex Argumentation Frameworks by Marcos Cramer
15:30 - 16:00 Coffee break
16:00 - 16:30 CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning by Sophie Siebert and Frieder Stolzenburg
16:30 - 17:00 Coffee break
17:00 - 17:30 Names are not just Sound and Smoke: Word Embeddings for Axiom Selection by Claudia Schon
19:00 - Dinner
Friday, 6.4.2018
9:30 - 10:00 Inference patterns for rational human reasoning by Gabriele Kern-Isberner
10:00 - 10:30 Coffee break
10:30 - 11:00 A Semantics for Conditionals with Default Negation by Marco Wilhelm
11:00 - 11:15 Coffee break
11:15 - 11:45 The CCOBRA Framework for Evaluating Models for Human Reasoning by Nicolas Riesterer
11:45 - 13:00 Lunch Break
13:00 - 13:30 Cognitive Principles and Individual Differences in Human Syllogistic Reasoning by Emmanuelle Dietz
13:30 - 14:00 Coffee break
14:00 - 14:30 Abductive reasoning in a neural-symbolic system by Andrzej Gajda
