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A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

Published online by Cambridge University Press:  10 August 2018

GEORGE BARYANNIS Open the ORCID record for GEORGE BARYANNIS [Opens in a new window] ,
ILIAS TACHMAZIDIS ,
SOTIRIS BATSAKIS ,
GRIGORIS ANTONIOU ,
MARIO ALVIANO ,
TIMOS SELLIS  and
PEI-WEI TSAI
GEORGE BARYANNIS
Affiliation:
University of Huddersfield, UK (e-mails: [email protected], [email protected], [email protected], [email protected])
ILIAS TACHMAZIDIS
Affiliation:
University of Huddersfield, UK (e-mails: [email protected], [email protected], [email protected], [email protected])
SOTIRIS BATSAKIS
Affiliation:
University of Huddersfield, UK (e-mails: [email protected], [email protected], [email protected], [email protected])
GRIGORIS ANTONIOU
Affiliation:
University of Huddersfield, UK (e-mails: [email protected], [email protected], [email protected], [email protected])
MARIO ALVIANO
Affiliation:
University of Calabria, Italy (e-mail: [email protected])
TIMOS SELLIS
Affiliation:
Swinburne University of Technology, Australia (e-mails: [email protected], [email protected])
PEI-WEI TSAI
Affiliation:
Swinburne University of Technology, Australia (e-mails: [email protected], [email protected])
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Abstract

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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.

Keywords

Answer Set ProgrammingSpatial ReasoningQualitative ReasoningTrajectory
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 3-4: 34th International Conference on Logic Programming , July 2018 , pp. 355 - 371
Copyright
Copyright © Cambridge University Press 2018 

References

Allen, J. F. 1981. An interval-based representation of temporal knowledge. In IJCAI, Hayes, P. J., Ed. William Kaufmann, 221–226.Google Scholar
Andrienko, G. L., Andrienko, N. V., Fuchs, G., and Wood, J. 2017. Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Trans. Vis. Comput. Graph. 23, 9, 2120–2136.Google Scholar
Brenton, C., Faber, W., and Batsakis, S. 2016. Answer set programming for qualitative spatio-temporal reasoning: Methods and experiments. In ICLP (Technical Communications), Carro, M., King, A., Saeedloei, N., and Vos, M. D., Eds. OASICS, vol. 52. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 4:1–4:15.Google Scholar
Cadoli, M. and Schaerf, M. 1993. A survey of complexity results for nonmonotonic logics. J. Log. Program. 17, 2/3&4, 127–160.Google Scholar
Cohn, A. G., Bennett, B., Gooday, J., and Gotts, N. M. 1997. Qualitative spatial representation and reasoning with the region connection calculus. GeoInformatica 1, 3, 275–316.Google Scholar
Delafontaine, M., Bogaert, P., Cohn, A. G., Witlox, F., Maeyer, P. D. and de Weghe, N. V. 2011. Inferring additional knowledge from qtcn relations. Inf. Sci. 181, 9, 1573–1590.Google Scholar
Delafontaine, M., Cohn, A. G. and de Weghe, N. V. 2011. Implementing a qualitative calculus to analyse moving point objects. Expert Syst. Appl. 38, 5, 5187–5196.Google Scholar
Eiter, T., Gottlob, G., and Mannila, H. 1997. Disjunctive datalog. ACM Trans. Database Syst. 22, 3, 364–418.Google Scholar
Escrig, M. T. and Toledo, F. 2002. Qualitative velocity. In CCIA, Escrig, M. T., Toledo, F., and Golobardes, E., Eds. Lecture Notes in Computer Science, vol. 2504. Springer, 29–39.Google Scholar
Freuder, E. C. and Mackworth, A. K. 2006. Constraint Satisfaction: An Emerging Paradigm. In Handbook of Constraint Programming, Rossi, F., van Beek, P., and Walsh, T., Eds. Foundations of Artificial Intelligence, vol. 2. Elsevier Science Inc., New York, NY, USA, 13–27.Google Scholar
Gebser, M., Harrison, A., Kaminski, R., Lifschitz, V., and Schaub, T. 2015. Abstract gringo. TPLP 15, 4–5, 449–463.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., and Wanko, P. 2016. Theory solving made easy with clingo 5. In Technical Communications of the Thirty-second International Conference on Logic Programming (ICLP'16), Carro, M. and King, A., Eds. Open Access Series in Informatics (OASIcs), vol. 52. Schloss Dagstuhl, 2:1–2:15.Google Scholar
Hazarika, S. M. and Cohn, A. G. 2001. Qualitative spatio-temporal continuity. In COSIT, Montello, D. R., Ed. Lecture Notes in Computer Science, vol. 2205. Springer, 92–107.Google Scholar
Hu, S.-R., Peeta, S., and Liou, H.-T. 2016. Integrated determination of network origin-destination trip matrix and heterogeneous sensor selection and location strategy. IEEE Trans. Intelligent Transportation Systems 17, 1, 195–205.Google Scholar
Li, J. J. 2012. Qualitative spatial and temporal reasoning with answer set programming. In ICTAI. IEEE Computer Society, 603–609.Google Scholar
Ma, Z., Lu, D., Liu, Q., Wang, J., and Xiong, Z. 2017. City-eyes: A multi-source data integration basec smart city analysis system. In Proceedings of the IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM2017). IEEE, 1–3.Google Scholar
Marek, V. W. and Truszczynski, M. 1989. Stable semantics for logic programs and default theories. In Logic Programming, Proceedings of the North American Conference 1989, Cleveland, Ohio, USA, October 16-20, 1989. 2 Volumes, Lusk, E. L. and Overbeek, R. A., Eds. MIT Press, 243–256.Google Scholar
Martínez-Martín, E., Escrig, M. T., and del Pobil, A. P. 2012. A general qualitative spatio-temporal model based on intervals. J. UCS 18, 10, 1343–1378.Google Scholar
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., and Damas, L. 2013. Predicting taxi-passenger demand using streaming data. IEEE Trans. Intelligent Transportation Systems 14, 3, 1393–1402.Google Scholar
Muller, P. 1998. A qualitative theory of motion based on spatio-temporal primitives. In KR, Cohn, A. G., Schubert, L. K., and Shapiro, S. C., Eds. Morgan Kaufmann, 131–143.Google Scholar
Noyon, V., Claramunt, C., and Devogele, T. 2007. A relative representation of trajectories in geogaphical spaces. GeoInformatica 11, 4, 479–496.Google Scholar
Patroumpas, K. and Sellis, T. K. 2015. Managing big trajectory data: Online processing of positional streams. In Big Data - Algorithms, Analytics, and Applications, Li, K.-C., Jiang, H., Yang, L. T., and Cuzzocrea, A., Eds. Chapman and Hall/CRC, 257–280.Google Scholar
Renz, J., Ed. 2002. The Region Connection Calculus. Springer Berlin Heidelberg, 41–50.Google Scholar
Renz, J., Rauh, R., and Knauff, M. 2000. Towards cognitive adequacy of topological spatial relations. In Spatial Cognition, Freksa, C., Brauer, W., Habel, C., and Wender, K. F., Eds. Lecture Notes in Computer Science, vol. 1849. Springer, 184–197.Google Scholar
Van de Weghe, N. 2017. Spatiotemporal relations for moving objects. In Encyclopedia of GIS, Shekhar, S., Xiong, H., and Zhou, X., Eds. Springer, 2177–2186.Google Scholar
Van de Weghe, N., Cohn, A. G., De Tré, G., and De Maeyer, P. 2006. A qualitative trajectory calculus as a basis for representing moving objects in geographical information systems. Control and Cybernetics 35, 1, 97–119.Google Scholar
Van de Weghe, N., Kuijpers, B., Bogaert, P., and Maeyer, P. D. 2005. A qualitative trajectory calculus and the composition of its relations. In GeoS, Rodríguez, M. A., Cruz, I. F., Egenhofer, M. J., and Levashkin, S., Eds. Lecture Notes in Computer Science, vol. 3799. Springer, 60–76.Google Scholar
Wałěga, P. A., Schultz, C. P. L., and Bhatt, M. 2017. Non-monotonic spatial reasoning with answer set programming modulo theories. TPLP 17, 2, 205–225.Google Scholar
Wang, Z., Jin, B., Zhang, F., Yang, R., and Ji, Q. 2017. Exploiting trip patterns in passenger trajectory streams for bus scheduling optimization in real time. In Proceedings of the 18th IEEE International Conference on Mobile Data Management. IEEE Computer Society, 266–271.Google Scholar
Yuan, J., Zheng, Y., Xie, X., and Sun, G. 2013. T-drive: Enhancing driving directions with taxi drivers' intelligence. IEEE Trans. Knowl. Data Eng. 25, 1, 220–232.Google Scholar
Zimmermann, K. and Freksa, C. 1996. Qualitative spatial reasoning using orientation, distance, and path knowledge. Appl. Intell. 6, 1, 49–58.Google Scholar
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