Authors:
Julian Eggert
1
;
Joerg Deigmoeller
1
;
Pavel Smirnov
1
;
Johane Takeuchi
2
and
Andreas Richter
1
Affiliations:
1
Honda Research Institute Europe, Carl-Legien-Straße 30, 63073 Offenbach am Main, Germany
;
2
Honda Research Institute Japan, 8-1 Honcho, Wako, Saitama 351-0114, Japan
Keyword(s):
Situational Question Answering, Knowledge Representation and Reasoning, Natural Language Understanding.
Abstract:
In this paper, we explore how artificial agents (AAs) can understand and reason about so called ”action pat-terns” within real-world settings. Essentially, we want AAs to determine which tools fit specific actions, and which actions can be executed with certain tools, objects or agents, based on real-world situations. To achieve this, we utilize a comprehensive Knowledge Graph, called ”Memory Net” filled with interconnected everyday concepts, common actions, and environmental data. Our approach involves an inference technique that harnesses semantic proximity through subgraph matching. Comparing our approach against human responses and a state-of-the-art natural language model based machine learning approach in a home scenario, our Knowledge Graph method demonstrated strong generalization capabilities, suggesting its promise in dynamic, incremental and interactive real world settings.