CORG - Cognitive Reasoning

• Applicants: Professor Dr. Ulrich Furbach; Professorin Dr. Claudia Schon; Professor Dr. Frieder Stolzenburg
• Subject Area: Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Theoretical Computer Science
• Term: from 2017 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 388853480

Cognitive computing addresses problems characterized by ambiguity and uncertainty, meaning that it is used to handle problems humans are confronted with in everyday life. When developing a cognitive computing system which is supposed to act human-like we cannot rely on automated theorem proving techniques alone, because humans performing commonsense reasoning do not obey the rules of classical logics. This causes humans to be susceptible to logical fallacies, but on the other hand to draw useful conclusions automated reasoning systems are incapable of. Humans naturally reason in the presence of incomplete and inconsistent knowledge and are able to reason in the presence of norms as well as conflicting norms. The versatility of human reasoning illustrates that any attempt to model the way humans perform commonsense reasoning has to use a combination of many different techniques.This project aims at the construction of a cognitive computing system by modeling aspects of human reasoning like emotions and human interactions. We will extend classical logical reasoning with non-monotonic reasoning like defeasible and normative logics in combination with machine learning. This will not only be carried out on a theoretical level. Different components for modeling the commonsense reasoning process will be developed and combined to a cognitive computing system which will be tested using benchmarks from commonsense reasoning.We want to address the following challenges:(C1) Finding Appropriate Logics for Cognitive Computing: There are essential differences between human and automated reasoning: Humans are able to reason with incomplete and inconsistent knowledge and naturally take background knowledge into account. Logics used for cognitive computing have to capture this versatility of human reasoning.(C2) Dealing with Large Background Knowledge Bases: Cognitive reasoning requires enormous amounts of background knowledge describing everyday experience humans use for reasoning. This background knowledge has to be constructed by combining appropriate sources. Further, the cognitive system must contain mechanisms to deal with the sheer size of this knowledge.(C3) Reasoning with Multiple Formats: Logical reasoning alone is not sufficient to model human reasoning. A cognitive system has to be able to handle natural language and deliberate about different conclusions which may be conflicting. For this, different reasoning techniques have to be combined in an infrastructure that ensures efficient cooperation.These challenges arise when constructing a cognitive computing system but have not been sufficiently addressed in the state of the art. We address them by combining reasoning mechanisms for logics suitable for cognitive computing, large amounts of background knowledge, and other techniques like machine learning. This will result in a cognitive reasoning system able to address problems which none of the techniques alone would have been able to address.

• DFG Programme: Research Grants