Qualitative spatial reasoning about relative point position

Moratz, Reinhard; Ragni, Marco

doi:10.1016/j.jvlc.2006.11.001

Cited by 52 publications

(37 citation statements)

References 22 publications

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“…This strategy has often been adopted in spatial reasoning approaches [13,25,29], with the aim of developing generic strategies for generating spatial reference by automatic systems across situational contexts. The main prerequisite for this kind of strategy is to understand the semantic contribution made by each of the spatial prepositions (or preposition-like structures, such as the phrase in the front of).…”

Section: Spatial Languagementioning

confidence: 99%

“…One major challenge concerns the fundamental difference between human concepts represented by natural language, especially in the domain of space [2], and formal systems suited for computational purposes, e.g., spatial reasoning-even if based on qualitative rather than metric relations [25]. Humans integrate a wide range of aspects of a spatial scene in order to interpret direction and distance concepts meaningfully, which is difficult to formalize in generic or context-adaptable terms [29].…”

Section: Spatial Dialoguementioning

confidence: 99%

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Situated Interaction with a Smart Environment: Challenges and Opportunities

Tenbrink

2017

Künstl Intell

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Section: Spatial Languagementioning

confidence: 99%

Section: Spatial Dialoguementioning

confidence: 99%

Situated Interaction with a Smart Environment: Challenges and Opportunities

Tenbrink

2017

Künstl Intell

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“…Moreover, there are several useful binary relations to describe point locations in two-dimensional space. For example, binary relations can describe relative positions with respect to a global coordinate system using the STAR relations [15], relative position with respect to points augmented with an orientation using STARVARS relations [16], or ternary relations that jointly capture distance and direction information using TPCC relations [17]. See Figure 5 for an illustration of these relations.…”

Section: Qualitative Representation Of Spacementioning

confidence: 99%

“…To this end, we only employ relations that have previously been investigated. [17] relations, P is in relation "close back left" with respect to origin O and reference R.…”

Section: Qualitative Representation Of Spacementioning

confidence: 99%

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Wolter

Kirsch

2015

Robotics

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Learning and planning are powerful AI methods that exhibit complementary strengths. While planning allows goal-directed actions to be computed when a reliable forward model is known, learning allows such models to be obtained autonomously. In this paper we describe how both methods can be combined using an expressive qualitative knowledge representation. We argue that the crucial step in this integration is to employ a representation based on a well-defined semantics. This article proposes the qualitative spatial logic QSL, a representation that combines qualitative abstraction with linear temporal logic, allowing us to represent relevant information about the learning task, possible actions, and their consequences. Doing so, we empower reasoning processes to enhance learning performance beyond the positive effects of learning in abstract state spaces. Proof-of-concept experiments in two simulation environments show that this approach can help to improve learning-based robotics by quicker convergence and leads to more reliable action planning.Keywords: qualitative spatial reasoning; robot learning; AI robotics MotivationRobotic applications have evolved considerably, yet they still lack the efficiency, flexibility and adaptability that is needed to improve our everyday life as versatile service robots. Planning and learning Robotics 2015, 4 254 are the two major paradigms employed to make a robot act intelligently. Planning is a form of symbolic reasoning applied on various levels of abstraction. Planning is effective whenever reliable forward models of the robot and its environment are available. Learning is a powerful method to generate such models from data. However, it requires carefully handcrafted state space representations to converge to high-quality models in reasonable time. Neither planning nor learning on their own suffice to meet the demands of versatile service robots. By integrating both paradigms one can benefit from their respective strengths. So far, a true integration of learning and planning has not been achieved that allows a robot to acquire new skills efficiently in a truly autonomous way.Our work is motivated by this basic question of how to integrate learning and planning, leveraging their individual strengths to produce reliable and versatile robot behavior. We propose a qualitative reasoning framework as the glue between those two paradigms. Qualitative reasoning lifts information, e.g., obtained by perception, to a knowledge level, enabling us to integrate it with common sense knowledge and to use available reasoning techniques [1][2][3]. The qualitative representation serves two purposes: (1) to make a planning or action selection process with learned models more efficient; and (2) to control selection of learning samples in order to make a learning process more reliable and to obtain high-quality models with little manual design effort. We present two experimental studies in simulation for a specific robot task: to throw an object to a specific destination. The results ind...

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“…Такие приложения часто требуют манипулирования и анализа с качественными пространственными данными. Качественное пространственное рассуждение (Qualitative spatial reasoning -QSR) (Cohn, Hazarika, 2001, Moratz, Ragni, 2008, Wallgrün, 2012 имеет несколько направлений развития. Одним из основных направлений является качественное пространственное рассуждение, связанное с предоставлением исчисления, которое позволяет компьютеру представлять и рассуждать с пространственными объектами без применения традиционных количественных методов.…”

Section: Introductionunclassified

Qualitative Spatial Reasoning and Spatial Relations

2017

European Journal of Psychological Studies

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The article describes the relationship between spatial reasoning and a series of spatial models, the most important of which are spatial relationships. Paper reveals the content of qualitative reasoning. Paper reveals the content of qualitative spatial reasoning. The article offers new concepts in the field of qualitative spatial reasoning. Paper reveals these new concepts: information units, spatial relationships, information structures and information situations. The article shows that information units are the basis of reasoning, information constructions by the mechanism of spatial conclusions. The article proves that spatial relations form the basis of qualitative analysis and qualitative spatial reasoning.

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Qualitative spatial reasoning about relative point position

Cited by 52 publications

References 22 publications

Situated Interaction with a Smart Environment: Challenges and Opportunities

Situated Interaction with a Smart Environment: Challenges and Opportunities

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Qualitative Spatial Reasoning and Spatial Relations

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