I am a Feodor Lynen Postdoctoral Research Fellow funded by the Humboldt Foundation and I am affiliated to the Centre for Quantum Software and Information at the University of Technology Sydney as a visiting fellow.
Prior to joining UTS, I was a research fellow in the Artificial Intelligence group of the Research School of Computer Science at the Australian National University. Before that I was based in Germany and was a member of the Cognitive Systems Group at the University of Bremen, working in the project R3 of the Transregional Collaborative Research Center 'Spatial Cognition'. I was also affiliated to the International Research Training Group on Semantic Integration of Geospatial Information.
I received my doctoral degree in computer science from the University of Bremen in October 2013, where a part of my doctoral research was carried out at the University at Buffalo and at North Carolina State University. I received my Diplom (MSc equivalent) in mathematics from the University of Bremen in 2009.
Knowledge Representation and Reasoning, Mulltiagent Systems, Machine Learning, Causal Inference
Dylla, F., Lee, J.H., Mossakowski, T., Schneider, T., Van Delden, A., Van De Ven, J. & Wolter, D. 2017, 'A survey of qualitative spatial and temporal calculi: Algebraic and computational properties', ACM Computing Surveys, vol. 50, no. 1, pp. 1-39.View/Download from: UTS OPUS or Publisher's site
Ge, X., Lee, J., Renz, J. & Zhang, P. 2016, 'Hole in One: Using Qualitative Reasoning for Solving Hard Physical Puzzle Problems', Frontiers in Artificial Intelligence and Applications, European Conference on Artificial Intelligence, AAAI Press, Netherlands, pp. 1762-1763.View/Download from: UTS OPUS or Publisher's site
The capability of determining the right sequence of physical actions to achieve a given task is essential for AI that interacts with the physical world. The great difficulty in developing this capability has two main causes: (1) the world is continuous and therefore the action space is infinite, (2) due to noisy perception, we do not know the exact physical properties of our environment and therefore cannot precisely simulate the consequences of a physical action.
In this paper we define a realistic physical action selection problem that has many features common to these kind of problems, the minigolf hole-in-one problem: given a two-dimensional minigolf-like obstacle course, a ball and a hole, determine a single shot that hits the ball into the hole. We assume gravity as well as noisy perception of the environment. We present a method that solves this problem similar to how humans are approaching these problems, by using qualitative reasoning and mental simulation, combined with sampling of actions in the real environment and adjusting the internal knowledge based on observing the actual outcome of sampled actions. We evaluate our method using difficult minigolf levels that require the ball to bounce at several objects in order to hit the hole and compare with existing methods.
Ge, X., Lee, J.H., Renz, J. & Zhang, P. 2016, 'Trend-based prediction of spatial change', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, New York City, New York, United States, pp. 1074-1080.View/Download from: UTS OPUS
The capability to predict changes of spatial regions is important for an intelligent system that interacts with the physical world. For example, in a disaster management scenario, predicting potentially endangered areas and inferring safe zones is essential for planning evacuations and countermeasures. Existing approaches usually predict such spatial changes by simulating the physical world based on specific models. Thus, these simulation-based methods will not be able to provide reliable predictions when the scenario is not similar to any of the models in use or when the input parameters are incomplete. In this paper, we present a prediction approach that overcomes the aforementioned problem by using a more general model and by analysing the trend of the spatial changes. The method is also flexible to adopt to new observations and to adapt its prediction to new situations.
Lee, J. & Wolter, D. 2016, 'Connecting qualitative spatial and temporal representations by propositional closure', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, IJCAI, New York, pp. 1308-1314.View/Download from: UTS OPUS
Lee, J.H., Li, S., Long, Z. & Sioutis, M. 2016, 'On Redundancy in Simple Temporal Networks', Frontiers in Artificial Intelligence and Applications, European Conference on Artificial Intelligence, AAAI Press, Netherlands, pp. 828-836.View/Download from: UTS OPUS or Publisher's site
The Simple Temporal Problem (STP) has been widely used in various applications to schedule tasks. For dynamical systems, scheduling needs to be efficient and flexible to handle uncertainty and perturbation. To this end, modern approaches usually encode the temporal information as an STP instance. This representation contains redundant information, which can not only take a significant amount of storage space, but also make scheduling inefficient due to the non-concise representation. In this paper, we investigate the problem of simplifying an STP instance by removing redundant information. We show that such a simplification can result in a unique minimal representation without loss of temporal information, and present an efficient algorithm to achieve this task. Evaluation on a large benchmark dataset of STP exhibits a significant reduction in redundant information for the involved instances.
Schockaert, S. & Lee, J.H. 2015, 'Qualitative reasoning about directions in semantic spaces', Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, AAAI Press / International Joint Conferences on Artificial Intelligence, Buenos Aires, Argentina, pp. 3207-3213.View/Download from: UTS OPUS
We introduce a framework for qualitative reasoning about directions in high-dimensional spaces, called EER, where our main motivation is to develop a form of commonsense reasoning about semantic spaces. The proposed framework is, however, more general; we show how qualitative spatial reasoning about points with several existing calculi can be reduced to the realisability problem for EER (or REER for short), including LR and calculi for reasoning about betweenness, collinearity and parallelism. Finally, we propose an efficient but incomplete inference method, and show its effectiveness for reasoning with EER as well as reasoning with some of the aforementioned calculi.
Zhang, P., Lee, J.H. & Renz, J. 2015, 'From raw sensor data to detailed spatial knowledge', Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, `AAAI Press / International Joint Conferences on Artificial Intelligence, Buenos Aires, Argentina, pp. 910-917.View/Download from: UTS OPUS
Qualitative spatial reasoning deals with relational spatial knowledge and with how this knowledge can be processed efficiently. Identifying suitable representations for spatial knowledge and checking whether the given knowledge is consistent has been the main research focus in the past two decades. However, where the spatial information comes from, what kind of information can be obtained and how it can be obtained has been largely ignored. This paper is an attempt to start filling this gap. We present a method for extracting detailed spatial information from sensor measurements of regions. We analyse how different sparse sensor measurements can be integrated and what spatial information can be extracted from sensor measurements. Different from previous approaches to qualitative spatial reasoning, our method allows us to obtain detailed information about the internal structure of regions. The result has practical implications, for example, in disaster management scenarios, which include identifying the safe zones in bushfire and flood regions.
Lee, J.H. 2014, 'The complexity of reasoning with relative directions', Frontiers in Artificial Intelligence and Applications, European Conference on Artificial Intelligence, IOS Press BV, Prague, Czech Republic, pp. 507-512.View/Download from: UTS OPUS or Publisher's site
© 2014 The Authors and IOS Press. Whether reasoning with relative directions can be performed in NP has been an open problem in qualitative spatial reasoning. Efficient reasoning with relative directions is essential, for example, in rule-compliant agent navigation. In this paper, we prove that reasoning with relative directions is -complete. As a consequence, reasoning with relative directions is not in NP, unless NP=.
Lee, J.H., Renz, J. & Wolter, D. 2013, 'StarVars-effective reasoning about relative directions', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, Beijing, China, pp. 976-982.View/Download from: UTS OPUS
Relative direction information is very commonly used. Observers typically describe their environment by specifying the relative directions in which they see other objects or other people from their point of view. Or they receive navigation instructions with respect to their point of view, for example, turn left at the next intersection. However, it is surprisingly hard to integrate relative direction information obtained from different observers, and to reconstruct a model of the environment or the locations of the observers based on this information. Despite intensive research, there is currently no algorithm that can effectively integrate this information: this problem is NP-hard, but not known to be in NP, even if we only use left and right relations. In this paper we present a novel qualitative representation, StarVars, that can solve these problems. It is an extension of the STAR calculus [Renz and Mitra, 2004] ) by a VARiable interpretation of the orientation of observers. We show that reasoning in StarVars is in NP and present the first algorithm that allows us to effectively integrate relative direction information from different observers.
Bhatt, M., Lee, J.H. & Schultz, C. 2011, 'CLP(QS): A declarative spatial reasoning framework', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Conference on Spatial Information Theory, Springer, Belfast, ME, USA, pp. 210-230.View/Download from: UTS OPUS or Publisher's site
We propose CLP(QS), a declarative spatial reasoning framework capable of representing and reasoning about high-level, qualitative spatial knowledge about the world. We systematically formalize and implement the semantics of a range of qualitative spatial calculi using a system of non-linear polynomial equations in the context of a classical constraint logic programming framework. Whereas CLP(QS) is a general framework, we demonstrate its applicability for the domain of Computer Aided Architecture Design. With CLP(QS) serving as a prototype, we position declarative spatial reasoning as a general paradigm open to other formalizations, reinterpretations, and extensions. We argue that the accessibility of qualitative spatial representation and reasoning mechanisms via the medium of high-level, logic-based formalizations is crucial for their utility toward solving real-world problems. © 2011 Springer-Verlag.