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propagation (algebraic closure), resulting in the generation of useful indirect knowledge from partial observations in a spatial scenario. In our sample application, a spatial agent (a simulated robot, cognitive simulation of a biological system etc.) explores a spatial scenario. The agent collects local observations and wants to generate survey knowledge.

Fig. 30 shows our spatial environment. It consists of a street network in which some streets continue straight after a crossing and some streets run parallel. These features are typical of real-world street networks. Spatial reasoning in our example uses constraint propagation (e.g. algebraic closure computation)
to derive indirect constraints between the relative location of streets which are further apart from local observations between neighboring streets. The resulting survey knowledge can be used for several tasks including navigation tasks.

The environment is represented as streets si and crossings Cj . The streets and crossings have unique names (e.g. s1, ... , s12, and C1, ..., C9 in one concrete example). The local observations are modeled in the following way, based on specific visibility rules (we want to simulate prototypical features of visual perception): Both at each crossing and at each straight street segment we have an observation. At each crossing the agent observes the neighboring crossings. At the middle of each straight street segment the agent can observe the direction of the outgoing streets at the adjacent crossings (but not at their other ends). Two specific examples of observations are marked in Fig. 30. The observation "s1 errs s7" is marked green at crossing C1. The observation "s8 rrllP s9" is marked red at street s4.

These observations relate spatially neighboring streets to each other in a pairwise manner, using DRAfp base relations. The agent has no additional knowledge about the specific environment. The spatial world knowledge of the agent is expressed in the converse and composition tables of DRAfp .

The following sequence of partial observations could be the result of a tour made by the spatial agent, exploring the street network of our example (see Fig. 30):