Learning Relational Concepts from Sensor Data of a Mobile Robot

Volker Klingspor, Katharina J. Morik, Anke D. Rieger

We provide here a set of data sets, where each data set is represented in first order logic. Valid restrictions of all data sets are: facts can be linked using the argument of type TIME, and there are never two different facts concerning the same sensor and the same point in time.
Each data set corresponds to learning disjoint concepts at one level. The levels are organized in a hierarchy as shown below:

        		                    high-level concepts
                  		                |	       \
	 		perception-integrating actions          \
	   		|		  | 			 \
	perceptual features               |                       \
	|	|			  |                        \
      	sensorgroup features              |                         \
       /	|			  |			    |	
      / sensor features                   |                         |
     /		|			  |                 	    |
    /	basic perceptual features         |                         |
sclass,        	|		      basic-actions,		    |	
dXsucc  raw sensor data               period-of-time-perceptions    pdirections
Each node in the hierarchy denotes a set of predicates. The links are directed from bottom to top. They link the sets of predicates in nodes of lower level to a set of predicates in a node of higher level, if the predicates of the lower level are necessary to learn the concepts of the higher level. Hence, a sequence of learning passes can learn high-level concepts from raw sensor data.
Further information on the data is contained in the BL-MLJ.names file. The files needed to perform the learning passes can be retreived from our ftp server.