Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Nonmonotonic Logic: Context-Dependent Reasoning
Nonmonotonic Logic: Context-Dependent Reasoning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
The proof algorithms of plausible logic form a hierarchy
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Propositional Clausal Defeasible Logic
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Architecture for Hybrid Robotic Behavior
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A dynamic metalogic argumentation framework implementation
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
A defeasible logic for clauses
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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Symbolic reasoning has rarely been applied to filter sensor information; and for data fusion, probabilistic models are favoured over reasoning with logic models. However, we show that in the fast dynamic environment of robotic soccer, Plausible Logic can be used effectively to deploy non-monotonic reasoning. We show this is also possible within the frame rate of vision in the (not so powerful) hardware of the AIBO ERS-7 used in the legged league. The non-monotonic reasoning with Plausible Logic not only has algorithmic completion guarantees but we show that it effectively filters the visual input for improved robot localisation. Moreover, we show that reasoning using Plausible Logic is not restricted to the traditional value domain of discerning about objects in one frame. We present a model to draw conclusions over consecutive frames and illustrate that adding temporal rules can further enhance the reliability of localisation.