A General Framework for Naming Qualitative Models Based on Intervals

Martínez-Martín, Ester; Escrig, M. Teresa; Pobil, Ángel P. del

doi:10.1007/978-3-642-28765-7_82

“…On the contrary, our work follows the first path: by applying the simplifications outlined in the introductory section, a high-level model is proposed which does not represent the individual features of objects in motion but, instead, is able to efficiently reason about the relations among trajectories when viewed as complete paths, scaling up to hundreds of trajectories. In that sense our work is not directly comparable to (Martínez-Martín et al 2012), since their reasoning process focuses exclusively on one of distance, velocity or acceleration features of a moving object, while ours proposes a trajectory model that abstracts away these features.…”

Section: Related Workmentioning

confidence: 99%

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Baryannis¹,

Tachmazidis²,

Batsakis³

et al. 2018

EasyChair Preprints

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Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi. This manuscript is under consideration for acceptance in TPLP.

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“…In order to achieve the latter, models need to be less elaborate either through simplification or by focusing on one feature at a time. In their attempts to provide a general framework for qualitative spatiotemporal representation and reasoning, Martínez-Martín et al (2012) follow the second path, providing CSP formalisations (Freuder and Mackworth 2006) that only focus on one feature of moving objects at a time. On the contrary, our work follows the first path: by applying the simplifications outlined in the introductory section, a high-level model is proposed which does not represent the individual features of objects in motion but, instead, is able to efficiently reason about the relations among trajectories when viewed as complete paths, scaling up to hundreds of trajectories.…”

Section: Related Workmentioning

confidence: 99%

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Baryannis¹,

Tachmazidis²,

Batsakis³

et al. 2018

Theory and Practice of Logic Programming

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Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.

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“…So, with the aim of overcoming all these issues, we have developed a method for adequately representing and reasoning with qualitative depth based on our previous work [ 47 ]. The implemented qualitative model allows the system to properly deal with situations in which the quantitative information is not sufficiently precise, and a number of distinctions that are of interest can provide the system with the ability to properly act and interact with the environment, independently of the extension of the surrounding elements.…”

Section: System Overviewmentioning

confidence: 99%

A Biologically Inspired Approach for Robot Depth Estimation

Martínez-Martín

¹

,

Pobil

²

2018

Computational Intelligence and Neuroscience

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0

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Aimed at building autonomous service robots, reasoning, perception, and action should be properly integrated. In this paper, the depth cue has been analysed as an early stage given its importance for robotic tasks. So, from neuroscience findings, a hierarchical four-level dorsal architecture has been designed and implemented. Mainly, from a stereo image pair, a set of complex Gabor filters is applied for estimating an egocentric quantitative disparity map. This map leads to a quantitative depth scene representation that provides the raw input for a qualitative approach. So, the reasoning method infers the data required to make the right decision at any time. As it will be shown, the experimental results highlight the robust performance of the biologically inspired approach presented in this paper.

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A General Framework for Naming Qualitative Models Based on Intervals

Cited by 10 publications

References 14 publications

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

A Biologically Inspired Approach for Robot Depth Estimation

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