Towards Explainable Inference about Object Motion using Qualitative Reasoning Artificial Intelligence

The capability of making explainable inferences regarding physical processes has long been desired. One fundamental physical process is object motion. Inferring what causes the motion of a group of objects can even be a challenging task for experts, e.g., in forensics science. Most of the work in the literature relies on physics simulation to draw such infer- ences. The simulation requires a precise model of the under- lying domain to work well and is essentially a black-box from which one can hardly obtain any useful explanation. By contrast, qualitative reasoning methods have the advan- tage in making transparent inferences with ambiguous infor- mation, which makes it suitable for this task. However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space. In this paper, we take this challenge and develop a qualitative theory for the motion of rigid objects. Based on this theory, we develop a reasoning method to solve a very interesting problem: Assuming there are several objects that were initially at rest and now have started to move. We want to infer what action causes the movement of these objects.


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Qualitative physics addresses the problem of modeling physical systems and reasoning about *This research has been sponsored by the Belgian Government with the contract "Incentive Program For Fundamental Research In Artificial Intelligence; Project: Self-organization in subsymbolic computation" and "Geconcerteerde Actie: Artifici le intelligentie, Parallelle Architecturen en Interfaces". Part of this research has also been funded by the Esprit Program with the contract P440: "Message Passing" Furthermore the basic strategy which consists in qualifying the equations of physics in order to obtain a useful set of inference rules is not applicable to liquids. We elaborate on these criticisms in the next section. The rest of this paper is divided into two parts. In section three we propose a hybrid architecture for the representation of the behavior of liquids. This architecture is composed of 216 a traditional symbolic reasoning module and an analogical simulation module coupled through different interpretation and visualization routines. We also briefly comment on the kind of analogical simulation needed for the purpose of predicting qualitatively the behavior of liquids in a large variety of possible situations. In the fourth section we discuss the complementary aspects of analogical and symbolic representation and argue that both are needed to build powerful and accurate models of liquid behavior.

Multi-Strategy Learning of Robotic Behaviours via Qualitative Reasoning

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When given a task, an autonomous agent must plan a series of actions to perform in order to complete the goal. In robotics, planners face additional challenges as the domain is typically large (even infinite) continuous, noisy, and non- deterministic. Typically stochastic planning has been used to solve robotic control tasks. Such planners have been very successful in their various domains. The downside to such approaches is that the models and planners are highly specialised to a single control task. To change the control task, requires developing an entirely new planner. The research in my thesis focuses on the problem of specialisation in continuous, noisy and non-deterministic robotic domains by developing a more generic planner. It builds on previous research in the area, specifically using the technique of Multi-Strategy Learning. Qualitative Modelling and Qualitative Reasoning is used to provide the generality, from which specific, Quantitative controllers can be quickly learnt. The resulting system is applied to a real world robotic platform for rough terrain navigation.