Analog retrieval by constraint satisfaction
Artificial Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Distributed representations and nested compositional structure
Distributed representations and nested compositional structure
The importance of retrieval in creative design analogies
Knowledge-Based Systems
| Hi-index | 0.00 |
RADAR is a model of analogy retrieval that employs the principle of systematicity as its primary retrieval cue. RADAR was created to address the current bias toward semantics in analogical retrieval models, to the detriment of structural factors. RADAR recalls 100% of structurally identical domains. We describe a technique based on "derived attributes" that captures structural descriptions of the domain's representation rather than domain contents. We detail their use, recall and performance within RADAR through empirical evidence. We contrast RADAR with existing models of analogy retrieval. We also demonstrate that RADAR can retrieve both semantically related and semantically unrelated domains, even without a complete target description, which plagues current models.