While recent advances in offline reasoning techniques and online execution strategies have made planning under uncertainty more robust, the application of plans in partially-known environments is still a difficult and important topic. In this paper we present an approach for predicting new information about a partially-known initial state, represented as a multi- graph utilizing Maximum-Margin Multi-Valued Regression. We evaluate this approach in four different domains, demonstrating high recall and accuracy.
|Title of host publication||The AAAI-17 Workshop on Knowledge-Based Techniques for Problem Solving and Reasoning - Technical Report|
|Place of Publication||Menlo Park, US-CA.|
|Number of pages||8|
|Publication status||Published - 5 Feb 2017|
- initial state prediction