What commonsense knowledge do intelligent systems need, in order to recover from failures or deal with unexpected situations? It is impractical to represent predetermined solutions to deal with every unanticipated situation or provide predetermined fixes for all the different ways in which systems may fail. We contend that intelligent systems require only a finite set of anomaly-handling strategies to muddle through anomalous situations. We describe a generalized metacognition module that implements such a set of anomaly-handling strategies and that in principle can be attached to any host system to improve the robustness of that system. Several implemented studies are reported, that support our contention.

In this abstract, we compare semantic search (in the RDF model) with keyword search (in the relational model), and illustrate how these two search paradigms are different. This comparison addresses the following questions: (1) What can semantic search achieve that keyword search can not (in terms of behavior)? (2) Why is it difficult to simulate semantic search, using keyword search on the relational data model? We use the term keyword search, when the search is performed on data stored in the relational data model, as in traditional relational databases, and an example of keyword search in databases is [Hri02]. We use the term semantic search, when the search is performed on data stored in the RDF data model. Note that when the data is modeled in RDF, it inherently contains explicit typed relations or semantics, and hence the use of the term “semantic search.” Let us begin with an example, to illustrate the differences between semantic search and keyword search.