@article {pub5299,
	title = {Situational Question Answering over Commonsense Knowledge using Memory Nets},
	author = {J{\"o}rg Deigm{\"o}ller AND Pavel Smirnov AND Julian Eggert AND Chao Wang AND Johane Takeuchi},
	year = {1970},
	month = {January},
	abstract = {Embodied Question Answering (EQA) is a rather novel research direction, which bridges the gap between environmental reasoning and actionable capabilities of mobile robotic platforms. Such mobile robotic platforms are usually located in random simulated environments. The task is to answer user requests about the environment by dynamic exploration. User requests are mostly related to object properties or positional relations to other objects in a scene. 
In this paper we present an approach called Situational Question Answering (SQA), which differs to EQA by enforcing the embodied agent to reason about context-relevant commonsense information. The approach relies on reasoning over an explicit knowledge graph complemented by inference mechanisms with transparent, human-understandable explanations. In particular, we combine a set of facts with basic knowledge about the world, a situational memory, commonsense understanding, and reasoning capabilities, which go beyond dedicated object knowledge. On top, we propose a Semantics Abstraction Layer (SAL) that acts as intermediate level between knowledge and natural language. The SAL is designed in a way that reasoning functions can be executed hierarchically to provide complex query resolution. 
We evaluate our system on a set of object related questions first (similar to EQA) and, second, on a set of questions that requires commonsense information about tools, actions and objects. For the latter evaluation, we compared our method against human performance, which is simulated by a mental exercise, similar to the urn problem. The human answers are created from a normal distribution based on real annotated data including 20 subjects. We further give insights into failure cases to provide possible directions for future research.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	journal = {Communications in Computer and Information Science}
}