Personalised Route Planning:
A Case-Based Approach

Lorraine McGinty and Barry Smyth

Smart Media Institute, Department of Computer Science,
University College Dublin, Belfield, Dublin 4, Ireland
{Lorraine . McGinty ,Barry. Smyth}@ucd.ie



Abstract. Automatically generating high-quality routes using real map
data is difficult for a number of reasons. Real maps rarely contain the
sort of information that is useful for constructing high quality routes. In
addition, the notion of route quality is difficult to define and is likely to
change from person to person. In this sense the automatic construction
of high-quality routes that match the preferences of individuals is an
example of a weak-theory problem, and therefore well suited to a case-based 
approach. In this paper we describe and evaluate a case-based
route planning system that is capable of efficiently generating routes
that reflect the implicit preferences of individual users.
References

1.	Branting, L. and Aha, D.: Stratified Case-Based Reasoning: Reusing hierarchical
problem solving episodes, Proceedings of the Fourteenth International Joint Conference 
on Artificial Intelligence, Morgan Kaufmann, 1995, pp. 384390.
2.	Haigh, K., Shewchuk, J., and Veloso, M.: Exploiting Domain Geometry in Analogical 
Route Planning, Journal of Experimental and Theoretical Artificial Intelligence
9 (1997), 509541.
3.	Haigh, K. and Veloso, M.: Route Planning by Analogy, Proceedings of the International 
Conference of Case-Based Reasoning, Springer-Verlag, 1995, pp. 169180.
4.	Kolodner, J. (ed.): Case-Based Reasoning, Morgan Kaufmann, 1993.
5.	Liu, B.: Using Knowledge To Isolate Search in Route Finding, Proceedings of Fourteenth 
International Joint Conference on Artificial Intelligence, 1995, pp. 119124.
6.	Liu, B.: Intelligent Route Finding: Combining Knowledge, Cases and An Efficient
Search Algorithm, Proceedings of the 12th European Conference on Artificial Intelligence, 
1996, pp. 380384.
7.	Rogers, S., and Fiechter, C.: A Route Advice Agent that Models Driver Preferences, 
Proceedings of the American Association of Artificial Intelligence Spring
Symposium on Agents with Adjustable Autonomy, 1999, pp. 106113.
8.	Rogers, S., and Langley, P.: Personalixed Driving Route Recommendations, Proceedings 
of the American Association of Artificial Intelligence Workshop on Recommender Systems, 1998, pp. 96100.
9.	Smyth, B. and Cunningham, P.: The Utility Problem Analysed: A Case-Based Reasoning 
Perspective, Proceedings of the American Association of Artificial Intelligence 
Spring Symposium on Agents with Adjustable Autonomy (I. Smith and
B. Faltings, eds.), Springer-Verlag, 1996, pp. 392399.
10.	Smyth, B. and Keane, M.: Adaptation-Guided Retrieval: Questioning the Similarity
Assumption in Reasoning, Artificial Intelligence 102 (1998), 249293.
11.	Watson, I. (ed.): Applying Case-Based Reasoning: Techniques for Enterprise Systems, 
Morgan Kaufmann, 1997.
