1. The “Spatial Turing Test” Stephan Winter, Yunhui Wu winter@unimelb.edu.au

2. Turing’s test Can machines think? Turing’s suggestion: an imitation game –A computer has to convince players that they are communicating with a person Questions: –Is imitating human behavior intelligent? –Isn’t the computer superior to the human mind? (Turing 1950)

3. Responses to questions Machines are superior in: –memory theoretically complete and up-to-date (network data) –executing algorithms theoretically correct (routes with specific properties) People are superior in: –choosing appropriate routes flexible, context-adaptive, personalized –communication of routes qualitative, cognitively ergonomic, meaningful

4. What’s then a spatial Turing test? Imitation game for spatial information –web-based map services, route planners, etc. –mobile location-based services –(excluded: systems for experts, such as GIS) Concerns whole human-computer interaction –input –interface –output –environment / situation To pass a spatial Turing test a service has to: –accept and understand all input a person would understand –produce information a person would produce in the given situation –formulate the information in expressions a person would do

5. Why do we need a spatial Turing test? Setting up a vision Investigating the gaps Devising a research agenda Note: –(at least by this approach) it is impossible to prove that machines can think spatially –but closing gaps means to refute that machines cannot think spatially

6. Case study: Input Service ontology: –stop names? (plan only for trains and subway) –address? –points of interest? User ontology –place descriptions? –spatial relations? What if: start city = destination city departure from here / now

7. Case study: Interface Service ontology: –(structured) text –absolute loc & time –hierarchical maps: visual aid only content not sufficient (different ontology) User ontology: –speech, gesture, dialog –relative loc & time –hierarchical place descriptions

8. Case study: Output Service ontology: –metric directions –map –single modes User ontology: –by landmarks –by sketch (map is excessive) –smooth mode transitions

9. Case study: Environment / Situation Service ontology: –self-contained –location-aware, but rarely orientation-aware User ontology: –device part of the environment –other information in the environment

10. Discussion of case study Service and user ontology at best overlapping –service ontology not covering cognitive concepts –service ontology based on spatial data network: route planning address: unambiguous referencing to buildings points of interest: geographic names –user ontology based on experience of environment inter-personal communication People have to serve the service –trial-and-error instead of negotiating –explicit (and limited) semantics

11. Conclusions Spatial Turing test useful to set up a vision Gaps identified in current technology –similarly: gaps in current research (input, interface, output, situation) Next: devising a research agenda

12. Short term research topics What we have: –extensive knowledge and models to generate better output cognitively ergonomic directions multi-modal, multi-criteria optimal routing route graphs, you-are-here maps What is next: –first commercial implementations under way, hindered by lack of data black box routing engines –experience with cognitively ergonomic directions

13. Long term research topics What is completely missing yet: –cognitively motivated data models experience, salience hierarchies links between elements –models to understand input place descriptions implicit semantics relations, imprecision, vagueness –integration with environment / with ambient intelligence capturing context

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