A fundamental issue concerning the treatment of meaning in context is how to deal with the extremely flexible relationship that appears to hold between descriptions, which are taken as the exchangeable bearers of meaning, and the actual contexts which those descriptions are taken to pick out. Such contexts appear always only to be suggested, or constrained, by descriptions of contexts and so relating levels of description, such as linguistic utterances, to actual contexts of use, such as a situated, fully embodied environment in which language users find themselves, remains an unsolved challenge. The present article sets out a framework and illustrative implementation of an approach to contextualization that combines ontological engineering principles and situated embodied simulations. For concreteness, we illustrate the approach within an already established architecture for situated robotic agents in order to allow implementation and experimentation in a manner that is not generally accessible when considering linguistic analysis alone. The paper then proposes a hybrid, multi-level architecture that contextualizes linguistic utterances by means of embodied simulations, which then serve further as contextualization constraints on semantic interpretation.
Alshawi, H. (Ed.) (1992). The Core Language Engine. Cambridge, Massachusetts: MIT Press.
2.
Andonova, E., Tenbrink, T. & Coventry, K.R. (2010). Function and context affect spatial information packaging at multiple levels. Psychonomic Bulletin & Review, 17, 575–580. doi:10.3758/PBR.17.4.575.
3.
Arbib, M.A., Gasser, B. & Barrès, V. (2014). Language is handy but is it embodied?Neuropsychologia, 55, 57–70. doi:10.1016/j.neuropsychologia.2013.11.004.
4.
Asher, N. & Lascarides, A. (2003). Logics of Conversation. Cambridge: Cambridge University Press.
5.
Aziz-Zadeh, L. & Damasio, A. (2008). Embodied semantics for actions: Findings from functional brain imaging. Journal of Physiology-Paris, 102(1), 35–39. doi:10.1016/j.jphysparis.2008.03.012.
6.
Baker, C.F., Fillmore, C.J. & Lowe, J.B. (1998). The berkeley FrameNet project. In Proceedings of the ACL/COLING-98 (pp. 86–90). San Mateo, CA: Association for Computational Linguistics and Morgan Kaufmann Publishers. 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics.
7.
Baldridge, J. & Kruijff, G.-J. (2002). Coupling CCG and hybrid logic dependency semantics. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania (pp. 319–326).
8.
Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M. & Schneider, N. (2013). Abstract meaning representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop & Interoperability with Discourse (pp. 178–186). ACL.
9.
Barsalou, L.W. (2003). Situated simulation in the human conceptual system. Language and Cognitive Processes, 18(5/6), 543–562.
10.
Barsalou, L.W. (2016). Can cognition be reduced to action? Processes that mediate stimuli and responses make human action possible. In A.K.Engel, K.J.Friston and D.Kragic (Eds.), The Pragmatic Turn: Towards Action-Oriented Views in Cognitive Science (pp. 81–96). Cambridge, MA: MIT Press. doi:10.7551/mitpress/9780262034326.003.0005.
11.
Bastianelli, E., Bloisi, D.D., Capobianco, R., Cossu, F., Gemignani, G., Iocchi, L. & Nardi, D. (2013a). On-line semantic mapping. In Proceedings of the 16th International Conference on Advanced Robotics (ICAR). IEEE.
12.
Bastianelli, E., Castellucci, G., Croce, D. & Basili, R. (2013b). Textual inference and meaning representation in human robot interaction. In Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora (pp. 65–69).
13.
Bastianelli, E., Croce, D., Basili, R. & Nardi, D. (2015). Using semantic maps for robust natural language interaction with robots. In EuroSpeech 2015ISCA.
14.
Bastianelli, E., Croce, D., Vanzo, A., Basili, R. & Nardi, D. (2016). A discriminative approach to grounded spoken language understanding in interactive robotics. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI’16) (pp. 2747–2753). AAAI Press. http://dl.acm.org/citation.cfm?id=3060832.3061005.
15.
Bateman, J.A. (1990). Upper modeling: Organizing knowledge for natural language processing. In O.Kathleen and R.McKeown (Eds.), Proceedings of the Fifth International Natural Language Generation Workshop, Pittsburgh, PA (pp. 54–60). Organized by Kathleen R. McKeown (Columbia University), Johanna D. Moore (University of Pittsburgh) and Sergei Nirenburg (Carnegie Mellon University). Held 3–6 June 1990, Dawson, PA. http://acl.ldc.upenn.edu/W/W90/W90-0108.pdf.
16.
Bateman, J.A. (2004). Realist linguistics and dynamic ontology. In A.Graumann, P.Holz and M.Plümacher (Eds.), Towards a Dynamic Theory of Language: A Festschrift in Honour of Wolfgang Wildgen (pp. 79–117). Bochum: Diversitas Linguarum, Universitätsverlags N. Brockmann.
17.
Bateman, J.A. (2010a). Language and space: A two-level semantic approach based on principles of ontological engineering. International Journal of Speech Technology, 13(1), 29–48. doi:10.1007/s10772-010-9069-x.
18.
Bateman, J.A. (2010b). Ontologies of language and language processing. In R.Poli, M.Healy and A.Kameas (Eds.), Theory and Applications of Ontology: Computer Applications (pp. 393–410). Dordrecht, Heidelberg, London and New York: Springer. doi:10.1007/978-90-481-8847-5_17.
19.
Bateman, J.A. (2013). Space, language and ontology: A response to Davis. Spatial Cognition & Computation, 13(4), 295–314. doi:10.1080/13875868.2013.808491.
20.
Bateman, J.A., Beetz, M., Beßler, D., Bozcuoğlu, A.K. & Pomarlan, M. (2018). Heterogeneous ontologies and hybrid reasoning for service robotics: The EASE framework. In Advances in Intelligent Systems and Computing, ROBOT 2017: Third Iberian Robotics Conference (Vol. 693, pp. 417–428). Springer.
21.
Bateman, J.A., Hois, J., Ross, R.J. & Tenbrink, T. (2010). A linguistic ontology of space for natural language processing. Artificial Intelligence, 174(14), 1027–1071. doi:10.1016/j.artint.2010.05.008.
22.
Bateman, J.A., Kasper, R.T., Moore, J.D. & Whitney, R.A. (1990). A general organization of Knowledge for natural language processing: The PENMAN Upper Model. Technical report, USC/Information, Sciences Institute, Marina del Rey, California. http://www.fb10.uni-bremen.de/anglistik/langpro/kpml/um89/um89-penman.pdf.
23.
Bateman, J.A., Magnini, B. & Rinaldi, F. (1994). The generalized {Italian, German,English} upper model. In Proceedings of the ECAI94 Workshop: Comparison of Implemented Ontologies, Amsterdam. http://citeseer.ist.psu.edu/bateman94generalized.html.
24.
Bateman, J.A., Tenbrink, T. & Farrar, S. (2007). The role of conceptual and linguistic ontologies in discourse. Discourse Processes, 44(3), 175–213. doi:10.1080/01638530701600912.
25.
Beetz, M., Jain, D., Mösenlechner, L., Tenorth, M., Kunze, L., Blodow, N. & Pangercic, D. (2012). Cognition-enabled autonomous robot control for the realization of home chore task intelligence. Proceedings of the IEEE, 100(8), 2454–2471. doi:10.1109/JPROC.2012.2200552.
26.
Beetz, M., Mösenlechner, L. & Tenorth, M. (2010). CRAM – a cognitive robot abstract machine for everyday manipulation in human environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (pp. 1012–1017).
27.
Bennett, B. & Agarwal, P. (2007). Semantic categories underlying the meaning of ‘place’. In S.Winter, M.Duckham, L.Kulik and B.Kuipers (Eds.), Spatial Information Theory: Proceedings of the 8th International Conference (COSIT-2007, Melbourne, Australia). Lecture Notes in Computer Science (Vol. 4736, pp. 78–95). Berlin and Heidelberg: Springer. doi:10.1007/978-3-540-74788-8_6.
28.
Bergen, B.K. & Chang, N. (2005). Embodied construction grammar in simulation-based language understanding. In J.-O.Östman and M.Fried (Eds.), Construction Grammar(s): Cognitive and Cross-Language Dimensions (pp. 147–190). Amsterdam: Johns Benjamins. doi:10.1075/cal.3.08ber.
29.
Bierwisch, M. (1999). How much space gets into language. In P.Bloom, M.A.Peterson, L.Nadel and M.F.Garrett (Eds.), Language and Space (pp. 31–76). Cambride, MA: MIT Press.
30.
Bierwisch, M. & Lang, E. (Eds.) (1989). Dimensional Adjectives. Grammatical Structure and Conceptual Interpretation. Berlin: Springer.
31.
Borgo, S., Guarino, N. & Masolo, C. (1996). Stratified ontologies: The case of physical objects. In Proceedings of the Workshop on Ontological Engineering at ECAI’96, Budapest, Hungary.
32.
Bos, J. & Oka, T. (2007). A spoken language interface with a mobile robot. Artificial Life and Robotics, 11(1), 42–47. doi:10.1007/s10015-006-0397-5.
33.
Bouquet, P., Ehrig, M., Euzenat, J., Franconi, E., Hitzler, P., Franconi, E., Hitzler, P., Krötzsch, M., Tessaris, S., Fensel, D. & Leger, A. (2005). Specification of a common framework for characterizing alignment. Knowledge Web Deliverable D2.2.1.
34.
Casati, R. & Varzi, A.C. (1999). Parts and Places: The Structures of Spatial Representation. Cambridge, MA and London: MIT Press (Bradford Books).
35.
Ceusters, W., Desimpel, I., Smith, B. & Schulz, S. (2003). Underspecification in formal ontologies. In Medical Informatics Europe 2003 (pp. 391–396). Amsterdam: IOS Press. http://ontology.buffalo.edu/medo/underspecification.pdf.
36.
Clark, H.H. (1996). Using Language. Cambridge: Cambridge University Press.
37.
Copestake, A., Flickinger, D., Pollard, C. & Sag, I. (2005). Minimal recursion semantics: An introduction. Research on Language and Computation, 3, 281–332. http://lingo.stanford.edu/sag/papers/copestake.pdf. doi:10.1007/s11168-006-6327-9.
38.
Coventry, K.R. (1998). Spatial prepositions, functional relations, and lexical specification. In P.Olivier and K.-P.Gapp (Eds.), Representation and Processing of Spatial Expressions (pp. 247–262). Mahwah, New Jersey: Lawrence Erlbaum Associates.
39.
Coventry, K.R. & Garrod, S.C. (2004a). Saying, Seeing and Acting. The Psychological Semantics of Spatial Prepositions. Essays in Cognitive Psychology Series. Hove, UK: Psychology Press.
40.
Coventry, K.R. & Garrod, S.C. (2004b). Towards a classification of extra-geometric influences on the comprehension of spatial prepositions. In L.Carlson and E.van der Zee (Eds.), Functional Features in Language and Space: Insights from Perception, Categorization and Development (pp. 149–162). Oxford: Oxford University Press. doi:10.1093/acprof:oso/9780199264339.003.0010.
41.
Davis, E. (2013a). Qualitative spatial reasoning in interpreting text and narrative. Spatial Cognition & Computation, 13(4), 264–294. doi:10.1080/13875868.2013.824976.
42.
Davis, E. (2013b). Space, language, and ontology: A response to bateman. Spatial Cognition & Computation13(4), 315–318. 10.1080/13875868.2013.824977.
43.
Davis, E. & Marcus, G. (2016). The scope and limits of simulation in automated reasoning. Artificial Intelligene, 233, 60–72. doi:10.1016/j.artint.2015.12.003.
44.
del Socorro Bernados Galindo, M. & Aguado de Ceo, G. (2001). Adapting the generalized upper model to Spanish. In G.Angelova, K.Bontcheva, R.Mitkov, N.Nicolov and N.Nikolov (Eds.), Proceedings of the Euroconference Recent Advances in Natural Language Processing (RANLP-2001), Tzigov, Bulgaria (pp. 103–107).
45.
Dudschig, C., Lachmair, M., de la Vega, I., De Fllippis, M. & Kaup, B. (2012). Do task-irrelevant direction-associated motion verbs affect action planning? Evidence from a stroop paradigm. Memory & Cognition, 40, 1081–1094. doi:10.3758/s13421-012-0201-9.
46.
Eschenbach, C. (1999). Geometric structures of frames of reference and natural language semantics. Spatial Cognition & Computation, 1(4), 329–348. doi:10.1023/A:1010075227531.
47.
Gallese, V. & Lakoff, G. (2005). ‘The brain’s concepts: The role of the sensory-motor system in conceptual knowledge’. Cognitive Neuropsychology, 22(3–4), 455–479. doi:10.1080/02643290442000310.
48.
Gennari, S.P. (2012). Representing motion in language comphrension: Lessons from neuroimaging. Language and Linguistics Compass, 6(2), 67–84. doi:10.1002/lnc3.317.
49.
Gibson, J.J. (1977). The theory of affordances. In R.Shaw and J.Bransford (Eds.), Perceiving, Acting, and Knowing: Toward and Ecological Psychology (pp. 62–82). Hillsdale, NJ: Erlbaum.
50.
Guarino, N. & Welty, C. (2004). An overview of OntoClean. In S.Staab and R.Studer (Eds.), Handbook on Ontologies (pp. 151–171). Heidelberg and Berlin: Springer. doi:10.1007/978-3-540-24750-0_8.
51.
Harnad, S. (1990). The symbol grounding problem. Physica, 42, 335–346.
52.
Hedblom, M.M., Kutz, O. & Neuhaus, F. (2015). Choosing the right path: Image schema theory as a foundation for concept invention. Journal of Artificial General Intelligence, 6(1), 21–54. doi:10.1515/jagi-2015-0003.
53.
Henschel, R. & Bateman, J.A. (1994). The merged upper model: A linguistic ontology for German and English. In Proceedings of COLING ’94, Vol. II (pp. 803–809). Japan: Kyoto. http://acl.ldc.upenn.edu/C/C94/C94-2128.pdf.
54.
Herskovits, A. (1986). Language and Spatial Cognition: An Interdisciplinary Study of the Prepositions in English. Studies in Natural Language Processing. London: Cambridge University Press.
55.
Hobbs, J.R. (1995). Sketch of an ontology underlying the way we talk about the world. International Journal of Human-Computer Studies, 43(5/6), 819–830. doi:10.1006/ijhc.1995.1076.
56.
Horrocks, I., Kutz, O. & Sattler, U. (2005). The Even More Irresistible SROIQ. Technical report, School of Computer Science, University of Manchester, Manchester, England. http://www.cs.man.ac.uk/~sattler/publications/sroiq-tr.pdf.
57.
Johnson, M. (1987). The Body in the Mind. Chicago, IL: University of Chicago Press.
58.
Johnson, M. & Lakoff, G. (2002). Why cognitive linguistics requires embodied realism. Cognitive Linguistics, 13(3), 245–263. doi:10.1515/cogl.2002.016.
59.
Kasper, R.T. (1989). A flexible interface for linking applications to PENMAN’s sentence generator. In L.Hirschman (Ed.), Proceedings of the DARPA Workshop on Speech and Natural Language, San Mateo, CA: Morgan Kaufmann. Available from ACL Anthology H89-1022: http://www.cs.mu.oz.au/acl/H/H89/H89-1022.pdf.
60.
Kaup, B., Lüdtke, J. & Maienborn, C. (2010). The drawer is still closed: Simulating past and future actions when processing sentences that describe a state. Brain & Language, 112, 159–166. doi:10.1016/j.bandl.2009.08.009.
61.
Kruijff, G.-J.M., Zender, H., Jensfelt, P. & Christensen, H.I. (2006). Clarification dialogues in human-augmented mapping. In Proceedings of the 1st Annual Conference on Human-Robot Interaction (HRI’06), Salt Lake City, UT (pp. 282–289).
62.
Kutz, O., Lutz, C., Wolter, F. & Zakharyaschev, M. (2004). -Connections of abstract description systems. Artificial Intelligence, 156(1), 1–73. doi:10.1016/j.artint.2004.02.002.
63.
Kutz, O., Mossakowski, T. & Codescu, M. (2008). Shapes of alignments: Construction, combination, and computation. In U.Sattler and A.Tamilin (Eds.), Proceedings of the 1st Workshop on Ontologies: Reasoning and Modularity (WORM-08) (Vol. 348). Tenerife, Spain: CEUR-WS, ESWC.
64.
Lang, E. & Maienborn, C. (2011). Two-level semantics: Semantic form and conceptual structure. In C.Maienborn, K.von Heusinger and P.Portner (Eds.), Semantics. An International Handbook of Natural Language Meaning; Volume 1, Number 33.1. HSK (pp. 709–740). Berlin and New York: Mouton de Gruyter.
65.
Levinson, S.C. (2003). Space in Language and Cognition: Explorations in Cognitive Diversity. Cambridge: Cambridge University Press.
66.
Liu, R. & Zhang, X. (2017). A review of methodologies for natural-language-facilitated human-robot cooperation. https://arxiv.org/abs/1701.08756.
67.
Logan, G.D. & Sadler, D.D. (1999). A computational analysis of the apprehension of spatial relations. In P.Bloom, M.A.Peterson, L.Nadel and M.F.Garrett (Eds.), Language and Space (pp. 493–530). Cambride, MA: MIT Press.
68.
Lukassek, J., Prysłopska, A., Hörnig, R. & Maienborn, C. (2017). The semantic processing of motion verbs: Coercion or underspecification. Journal of Psycholinguistic Research, 46, 805–825. doi:10.1007/s10936-016-9466-7.
69.
Mann, W.C. (1983). An overview of the PENMAN text generation system. In Proceedings of the National Conference on Artificial Intelligence (pp. 261–265). AAAI. Also appears as USC/Information Sciences Institute, RR-83-114.
70.
Matuszek, C., Herbst, E., Zettlemoyer, L. & Fox, D. (2013). Learning to Parse Natural Language Commands to a Robot Control System (pp. 403–415). Heidelberg: Springer.
71.
Min, H., Yi, C., Luo, R., Zhu, J. & Bi, S. (2016). Affordance research in developmental robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 8(4), 237–255. doi:10.1109/TCDS.2016.2614992.
72.
Misra, D.K., Sung, J., Lee, K. & Saxena, A. (2014). Tell me dave: Context-sensitive grounding of natural language to manipulation instructions. In Proceedings of Robotics: Science and Systems, Berkeley, USA.
73.
Morris, C.W. (1938). Foundations of the Theory of Signs. Chicago: University of Chicago Press.
74.
Mösenlechner, L. & Beetz, M. (2013). Fast temporal projection using accurate physics-based geometric reasoning. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (pp. 1821–1827).
75.
Mossakowski, T., Codescu, M., Neuhaus, F. & Kutz, O. (2015). The distributed ontology, modeling and specification language – DOL. In A.Koslow and A.Buchsbaum (Eds.), The Road to Universal Logic, Vol. I (pp. 489–520). Birkhäuser. http://www.springer.com/gp/book/9783319101927. doi:10.1007/978-3-319-15368-1_21.
76.
Nüchter, A. & Hertzberg, J. (2008). Towards semantic maps for mobile robots. Robotics and Autonomous Systems, 56(11), 915–926. doi:10.1016/j.robot.2008.08.001.
77.
Nyga, D., Picklum, M., Koralewski, S. & Beetz, M. (2017). Instruction completion through instance-based learning and semantic analogical reasoning. In International Conference on Robotics and Automation (ICRA).
78.
OMG (2015). The Distributed Ontology, Modeling, and Specification Language (DOL). Technical report, OMG. https://github.com/tillmo/DOL/raw/master/Standard/dol.pdf.
79.
Pomarlan, M. & Bateman, J.A. (2018). Robot program construction via grounded natural language semantics & simulation. In Proceedings of the 17th Conference on Autonomous Agents and MultiAgent Systems. AAMAS ’18.
80.
Pomarlan, M., Koralewski, S. & Beetz, M. (2017). From natural language instructions to structured robot plans. In G.Kern-Isberner, J.Fürnkranz and M.Thimm (Eds.), KI 2017: Advances in Artificial Intelligence (pp. 344–351). Cham: Springer. doi:10.1007/978-3-319-67190-1_30.
81.
Porzel, R., Gurevych, I. & Malaka, R. (2006). Context: Integrating domain- and situation-specific knowledge. In W.Wahlster (Ed.), SmartKom – Foundations of Multimodal Dialogue Systems (pp. 269–284). Heidelberg: Springer. doi:10.1007/3-540-36678-4_18.
82.
Porzel, R. & Warden, T. (2010). Working simulations with a foundational ontology. In K.Schill, B.Scholz-Reiter and L.Frommberger (Eds.), Proceedings of the Workshop on Artificial Intelligene and Logistics at the 19th European Conference on Artificial Intelligence, Lisbon.
83.
Regier, T. & Carlson, L.A. (2001). Grounding spatial language in perception: An empirical and computational investigation. Journal of Experimental Psychology: General, 130(2), 273–298. doi:10.1037/0096-3445.130.2.273.
84.
Richardson, D.C., Spivey, M.J., Barsalou, L.W. & McRae, K. (2003). Spatial representations activated during real-time comprehension of verbs. Cognitive Science, 27(5), 767–780. doi:10.1207/s15516709cog2705_4.
85.
Ross, R.J. (2011). Situated Dialogue Systems: Agency & Spatial Meaning in Task-Oriented Dialogue, CreateSpace Independent Publishing Platform. Published Doctoral Dissertation, University of Bremen.
86.
Roy, D. (2005). Semiotic schemas: A framework for grounding language in action and perception. Artificial Intelligence, 167, 170–205. doi:10.1016/j.artint.2005.04.007.
87.
Scheutz, M., Krause, E., Oosterveld, B., Frasca, T. & Platt, R. (2017). Spoken instruction-based one-shot object and action learning in a cognitive robotic architecture. In Proceedings of the 16th International Conference on Autoomous Agents and Multiagent Systems.
88.
Steedman, M.J. (2000). The Syntactic Process. Cambridge, Massachusetts: MIT Press.
89.
Stulp, F. & Beetz, M. (2008). Combining declarative, procedural, and predictive knowledge to generate, execute, and optimize robot plans. Robotics and Autonomous Systems, 56(11), 967–979. doi:10.1016/j.robot.2008.08.011.
90.
Stulp, F., Fedrizzi, A., Mösenlechner, L. & Beetz, M. (2012). Learning and reasoning with action-related places for robust mobile manipulation. Journal of Artificial Intelligence Research (JAIR), 43, 1–42. doi:10.1613/jair.3451.
91.
Talmy, L. (2000). Towards a Cognitive Semantics. Cambridge, MA: A Bradford Book, MIT Press.
92.
Talmy, L. (2006). The fundamental system of spatial schemas in language. In B.Hampe (Ed.), From Perception to Meaning: Image Schemas in Cognitive Linguistics (pp. 37–47). Berlin: Mouton de Gruyter.
93.
Tenorth, M. & Beetz, M. (2013). KnowRob – a knowledge processing infrastructure for cognition-enabled robots. Int. Journal of Robotics Research, 32(5), 566–590. doi:10.1177/0278364913481635.
94.
Tenorth, M., Jain, D. & Beetz, M. (2010). Knowledge representation for cognitive robots. Künstliche Intelligenz, 24(3), 233–240. doi:10.1007/s13218-010-0044-0.
95.
Varadarajan, K.M. & Vincze, M. (2012). AfRob: The affordance network ontology for robots. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 1343–1350). doi:10.1109/IROS.2012.6386232.
96.
Varzi, A.C. (2007). From language to ontology: Beware of the traps. In M.Aurnague, M.Hickmann and L.Vieu (Eds.), The Categorization of Spatial Entities in Language and Cognition. Human Cognitive Processing (Vol. 20, pp. 269–284). Amsterdam, The Netherlands: John Benjamins Publishing Company. doi:10.1075/hcp.20.16var.
97.
Welty, C. & Andersen, W. (2005). Towards OntoClean 2.0: A framework for rigidity. Applied Ontology, 1(1), 107–116.
98.
Winkler, J., Bozcuoğlu, A.K., Pomarlan, M. & Beetz, M. (2017). Task parametrization through multi-modal analysis of robot experiences. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS ’17, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (pp. 1754–1756). http://dl.acm.org/citation.cfm?id=3091125.3091428.
99.
Winkler, J., Tenorth, M., Bozcuoglu, A.K. & Beetz, M. (2014). CRAMm – memories for robots performing everyday manipulation activities. Advances in Cognitive Systems, 3, 47–66.
100.
Zimmer, H.D., Speiser, H.R., Baus, J., Blocher, A. & Stopp, E. (1998). The use of locative expressions in dependence of the spatial relation between target and reference object in two-dimensional layouts. In C.Freksa, C.Habel and K.F.Wender (Eds.), Spatial Cognition I – An Interdisciplinary Approach to Representing and Processing Spatial Knowledge (pp. 223–240). Springer.
101.
Zlatev, J. (2007). Spatial semantics. In H.Cuyckens and D.Geeraerts (Eds.), Oxford Handbook of Cognitive Linguistics (pp. 318–350). Oxford, U.K.: Oxford University Press.
