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 multigraph 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|
|Place of Publication||Palo Alto, US-CA.|
|Publication status||Published - 21 Mar 2017|
- reasoning techniques
- online execution strategies
- contingency planning
- artifical intelligence