http://dl.gi.de/handle/20.500.12116/36220 Thu, 23 Jul 2026 10:25:45 GMT 2026-07-23T10:25:45Z http://dl.gi.de/handle/20.500.12116/36297 Interactive Transfer Learning in Relational Domains Kumaraswamy, Raksha; Ramanan, Nandini; Odom, Phillip; Natarajan, Sriraam We consider the problem of interactive transfer learning where a human expert provides guidance to the transfer learning algorithm that aims to transfer knowledge from a source task to a target task. One of the salient features of our approach is that we consider cross-domain transfer, i.e., transfer of knowledge across unrelated domains. We present an intuitive interface that allows for an expert to refine the knowledge in target task based on his/her expertise. Our results show that such guided transfer can effectively reduce the search space thus improving the efficiency and effectiveness of the transfer process. Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36297 2020-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36295 Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning Sioutis, Michael We discuss a research roadmap for going beyond the state of the art in qualitative spatial and temporal reasoning (QSTR). Simply put, QSTR is a major field of study in Artificial Intelligence that abstracts from numerical quantities of space and time by using qualitative descriptions instead (e.g., precedes, contains, is left of); thus, it provides a concise framework that allows for rather inexpensive reasoning about entities located in space or time. Applications of QSTR can be found in a plethora of areas and domains such as smart environments, intelligent vehicles, and unmanned aircraft systems. Our discussion involves researching novel local consistencies in the aforementioned discipline, defining dynamic algorithms pertaining to these consistencies that can allow for efficient reasoning over changing spatio-temporal information, and leveraging the structures of the locally consistent related problems with regard to novel decomposability and theoretical tractability properties. Ultimately, we argue for pushing the envelope in QSTR via defining tools for tackling dynamic variants of the fundamental reasoning problems in this discipline, i.e., problems stated in terms of changing input data. Indeed, time is a continuous flow and spatial objects can change (e.g., in shape, size, or structure) as time passes; therefore, it is pertinent to be able to efficiently reason about dynamic spatio-temporal data. Finally, these tools are to be integrated into the larger context of highly active areas such as neuro-symbolic learning and reasoning, planning, data mining, and robotic applications. Our final goal is to inspire further discussion in the community about constraint-based QSTR in general, and the possible lines of future research that we outline here in particular. Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36295 2020-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36294 One Explanation Does Not Fit All Sokol, Kacper; Flach, Peter The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system’s operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations—a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up “What if?” questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats. Furthermore, we discuss the challenges of mirroring the explainee’s mental model, which is the main building block of intelligible human–machine interactions. We also deliberate on the risks of allowing the explainee to freely manipulate the explanations and thereby extracting information about the underlying predictive model, which might be leveraged by malicious actors to steal or game the model. Finally, building an end-to-end interactive explainability system is a challenging engineering task; unless the main goal is its deployment, we recommend “Wizard of Oz” studies as a proxy for testing and evaluating standalone interactive explainability algorithms. Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36294 2020-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36296 News Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36296 2020-01-01T00:00:00Z