Iman Awaad successfully defends her PhD

We would like to congratulate our research assosiate Iman Awaad, who successfully defended her PhD yesterday (19.03.2025) in Osnabrück University.

The dissertation was a cooperative PhD project with the Institute of Computer Science at Osnabrück University. The project was originally advised by Prof. Gerhard Kraetzschmar and Prof. Joachim Hertzberg from Osnabrück University. Prof. Paul G. Plöger stepped in as an advisor after Prof. Kraetzschmar's passing.

The dissertation was awarded a magna cum laude.

Dissertation abstract

The success of any agent, human or artificial, ultimately depends on their successfully accomplishing the given goals. Agents may, however, fail to do so for many reasons. With artificial agents, such as robots, this may be due to internal faults or exogenous events in the complex, dynamic environments in which they operate. The bottom line is that plans, even good ones, can fail. Despite decades of research, effective methods for artificial agents to cope with plan failure remain limited and are often impractical in the real world. 

One common reason for failure that plagues agents, human and artificial alike, is that objects that are expected to be used to get the job done are often found to be missing or unavailable. Humans might, with little effort, accomplish their tasks by making substitutions. When they are not sure if an object is available, they may even proceed optimistically and switch to making a substitution when they confirm that an object is indeed unavailable.

In this work, the system uses Description Logics to enable open-world reasoning --- making it possible to distinguish between cases where an object is missing/unavailable and cases where the failure to even generate a plan is due to the planner's use of the closed-world assumption (where the fact stating that something is true is missing from its knowledge base and so it is assumed to be not true). 

This ability to distinguish between something being missing and having incomplete information enables the agent to behave intelligently: recognising whether it should identify and then plan with a suitable substitute or create a placeholder, in the case of incomplete information. By representing the functional affordances of objects (i.e. what they are meant to be used for),  socially-expected and accepted object substitutions are made possible.  The system also uses the Conceptual Spaces approach to provide feature-based similarity measures that make the given task a first-class citizen in the identification of a suitable substitute. The generation of plans to "get the job done" is made possible by incorporating the Hierarchical Task Network planning approach. It is combined with a robust execution/monitoring system and contributes to the success of the robot in achieving its goals.