Recognizing hand-sketched symbols is a definitely complex problem. The input drawings are often intrinsically ambiguous, and require context to be interpreted in a correct way. Many existing sketch recognition systems avoid this problem by recognizing single segments or simple geometric shapes in a stroke. However, for a recognition system to be effective and precise, context must be exploited, and both the simplifications on the sketch features, and the constraints under which recognition may take place, must be reduced to the minimum. In this paper, we present an agent-based framework for sketched symbol interpretation that heavily exploits contextual information for ambiguity resolution. Agents manage the activity of low level hand-drawn symbol recognizers, that may be heterogeneous for better adapting to the characteristics of each symbol to be recognized, and coordinate themselves in order to exchange contextual information, thus leading to an efficient and precise interpretation of sketches. We also present AgentSketch, a multi-domain sketch recognition system implemented according to the proposed framework. A first experimental evaluation has been performed on the domain of UML Use Case Diagrams to verify the effectiveness of the proposed approach.
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