This is a list of notes/questions captured during the Workshop on the 12th of October 2015. Names denote corresponding presentations. 


S. Scheider:

The wrong modeling (computer?) architectures?

	Embodied spatial cognition for information science?

	Feedback mechanisms and Eigen-stability?

	Using neural/psychological information processing models?

How to integrate behavior observation (Psychology) with design (Engineering)?

How could a common infrastructure for modeling and empirical testing look like?

How to test cognitive spatial models under realistic situations for the purpose of information science?



K.-F. Richter:

Teamwork between computer and human (divide of labor). However, cooperation requires understanding of the other. 

Design with perfect seams in mind, rather than strive for seamless perfection

People and computers are good at different things (and that is a good thing)

the role of context 

Personalization (groups instead of individuals)

From lab to street?

Ethical questions? dilemma of deciding about saving lifes



K. Stock:

new level of vagueness on relations in location descriptions

vagueness, ambiguity, underspecification w.r.t. gis

idea: ambiguity resolution based on interaction, disambiguation 



S. Winter:

What is an accuracy measure for place graphs? evaluation?

Context is lost?

Collective human spatial knowledge vs. group based vs. individual



G. McKenzie:

Is this only an extraction technique (where context is lost)?

good for labeling problems, but for finding different conceptualizations? where do the categories come from? 


Andre Frank: 

Adress systems provide spatial information that tries to optimize a certain task (iranian adress system: reach a place, austrian/uk address system: postman)

experts vs. novices?


Christian Freksa:

a full cognitive/embodied system has affordances

are maps more abstract than books?

Brooks: how do you do planning? 
intelligence without representation +
intelligence without environment

cognitive off-loading

abstract vs. concrete

How to reconstruct quantitative information from qualitative information + environment?
abstract + context = concrete

perception is fuzzy

concepts are non-fuzzy (crisp-relational)

concepts are instantiated (not by potential instances), need contrast

radical abstraction (language)