Automatic synthesis and compression of cardiological knowledge
Machine intelligence 11
Synthesizing information-tracing automata from environment descriptions
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Artificial Intelligence - Special issue on knowledge representation
Vm: representing time-dependent relations in a medical setting
Vm: representing time-dependent relations in a medical setting
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
An architecture for adaptive intelligent systems
Artificial Intelligence
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Decision trees have provided a classical mechanism for progressively narrowing down a search from a large group of possibilities to a single alternative. The structuring of a decision tree is based on a heuristic that maximizes the value of the information gained at each level in the hierarchy. Decision trees are effective when an agent needs to reach the goal of complete diagnosis as quickly as possible and cannot accept a partial solution. We present an alternative to the decision tree heuristic which is useful when partial solutions do have value and when limited resources may require an agent to accept a partial solution. Our heuristic maximizes the improvement in the value of the partial solution gained at each level in the hierarchy; we term the resulting structure an action-based hierarchy. We present the results of a set of experiments designed to compare these two heuristics for hierarchy structuring. Finally, we describe some preliminary work we have done in applying these ideas to a medical domain--surgical intensive care unit (SICU) patient monitoring.